A method and system for anti-corrosion treatment of steel pipe to be processed

Abstract

The present invention discloses an anti-corrosion treatment method and system for a steel pipe to be processed, which belongs to the field of steel processing, wherein the method comprises: determining the steel pipe to be processed, performing image acquisition on the steel pipe to be processed, and obtaining the initial image information of the steel pipe to be processed; performing feature extraction on the initial image information, and determining the surface defect characteristics; judging whether the surface defect characteristics meet the surface defect constraints of the steel pipe; if they meet, generating anti-corrosion process parameters based on the surface defect characteristics, performing anti-corrosion treatment on the steel pipe to be processed by using the anti-corrosion process parameters, and obtaining a target anti-corrosion pipe; performing anti-corrosion quality inspection on the target anti-corrosion pipe according to preset anti-corrosion indicators, and if the inspection result meets the preset anti-corrosion indicators, adding the target anti-corrosion pipe into the finished product stacking area. By analyzing the surface defects of the steel pipe, generating the corresponding anti-corrosion process parameters, and implementing personalized anti-corrosion treatment on the steel pipe, the technical effect of improving the anti-corrosion quality and anti-corrosion effect of the steel pipe is achieved.

Description

Technical Field

[0001] The invention relates to the field of steel processing, and in particular to an anti-corrosion treatment method and system for a steel pipe to be processed. Background Art

[0002] Steel pipes are widely used in the fields of construction, petrochemicals, municipal engineering, etc. In order to extend the service life and prevent corrosion, steel pipes need to be treated with anti-corrosion before being put into use. In the prior art, anti-corrosion treatment uses fixed process parameters to uniformly treat all steel pipes to be processed. However, due to differences in the production and processing processes, the surface defects of different steel pipes vary greatly, such as pits, burrs, etc. It is difficult to fully consider the actual surface conditions of each steel pipe when using fixed anti-corrosion process parameters for anti-corrosion treatment, which affects the anti-corrosion quality and anti-corrosion effect of the steel pipe. Summary of the invention

[0003] The present application provides an anti-corrosion treatment method and system for steel pipes to be processed, aiming to solve the technical problem in the prior art of using fixed anti-corrosion process parameters to perform anti-corrosion treatment on all steel pipes to be processed, resulting in poor anti-corrosion quality and anti-corrosion effect.

[0004] In view of the above problems, the present application provides an anti-corrosion treatment method and system for a steel pipe to be processed.

[0005] The first aspect disclosed in the present application provides an anti-corrosion treatment method for a steel pipe to be processed, the method comprising: determining the steel pipe to be processed, performing image acquisition on the steel pipe to be processed, and obtaining initial image information of the steel pipe to be processed; performing feature extraction on the initial image information and determining surface defect characteristics; judging whether the surface defect characteristics meet the surface defect constraints of the steel pipe; if so, generating anti-corrosion process parameters based on the surface defect characteristics, performing anti-corrosion treatment on the steel pipe to be processed according to the anti-corrosion process parameters, and obtaining a target anti-corrosion pipe; performing anti-corrosion quality inspection on the target anti-corrosion pipe according to preset anti-corrosion indicators, and if the inspection result meets the preset anti-corrosion indicators, adding the target anti-corrosion pipe to the finished product stacking area.

[0006] Another aspect disclosed in the present application provides an anti-corrosion treatment system for a steel pipe to be processed, the system comprising: an initial image acquisition module, used to determine the steel pipe to be processed, perform image acquisition on the steel pipe to be processed, and obtain initial image information of the steel pipe to be processed; a defect feature extraction module, used to extract features from the initial image information and determine surface defect features; a defect constraint judgment module, used to judge whether the surface defect features meet the surface defect constraints of the steel pipe; an anti-corrosion treatment module, used to generate anti-corrosion process parameters based on the surface defect features if they are satisfied, and perform anti-corrosion treatment on the steel pipe to be processed according to the anti-corrosion process parameters to obtain a target anti-corrosion pipe; an anti-corrosion quality detection module, used to perform anti-corrosion quality detection on the target anti-corrosion pipe according to preset anti-corrosion indicators, and if the detection result meets the preset anti-corrosion indicators, add the target anti-corrosion pipe to the finished product stacking area.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The method adopts the method of determining the steel pipe to be processed, collecting images of the steel pipe to be processed, obtaining the initial image information of the steel pipe to be processed, and laying the foundation for subsequent defect recognition and analysis; extracting features of the initial image information, determining surface defect features, and extracting various defect features on the surface of the steel pipe; judging whether the surface defect features meet the surface defect constraints of the steel pipe, comparing the extracted defect features with the preset surface defect constraints of the steel pipe, and judging whether the defects of the steel pipe are within an acceptable range; for steel pipes with defects within an acceptable range, selecting corresponding anti-corrosion process parameters, and implementing personalized anti-corrosion treatment on the steel pipe to obtain the final target anti-corrosion steel pipe; conducting anti-corrosion quality inspection on the target anti-corrosion pipe according to the preset anti-corrosion index, and adding the target anti-corrosion pipe to the finished product stacking area if the inspection result meets the preset anti-corrosion index, solves the technical problem of poor anti-corrosion quality and anti-corrosion effect in the prior art that all steel pipes to be processed are treated with fixed anti-corrosion process parameters, and the corresponding anti-corrosion process parameters are generated by analyzing the surface defects of the steel pipe, and personalized anti-corrosion treatment is implemented on the steel pipe, thereby achieving the technical effect of improving the anti-corrosion quality and anti-corrosion effect of the steel pipe.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of an anti-corrosion treatment method for a steel pipe to be processed is provided for an embodiment of the present application;

[0011] Figure 2A structural schematic diagram of an anti-corrosion treatment system for a steel pipe to be processed is provided for an embodiment of the present application.

[0012] Explanation of the reference numerals: initial image acquisition module 11 , defect feature extraction module 12 , defect constraint judgment module 13 , anti-corrosion treatment module 14 , anti-corrosion quality detection module 15 . DETAILED DESCRIPTION

[0013] The overall idea of ​​the technical solution provided by this application is as follows:

[0014] The embodiment of the present application provides an anti-corrosion treatment method and system for a steel pipe to be processed. First, the image of the steel pipe to be processed is collected to obtain its initial image information. Then, the initial image information is processed and feature extracted to determine the various defect features existing on the surface of the steel pipe. Next, the extracted defect features are compared and judged with the pre-set steel pipe surface defect constraints to determine whether the defects of the steel pipe are within an acceptable range. If the steel pipe surface defect constraints are met, the steel pipe is subjected to targeted and personalized anti-corrosion treatment based on the anti-corrosion process parameters to obtain a target anti-corrosion steel pipe. Afterwards, according to the preset anti-corrosion quality index, the target anti-corrosion steel pipe after the anti-corrosion treatment is subjected to quality inspection. When the inspection results meet the anti-corrosion quality index, the target anti-corrosion steel pipe can be added to the finished product stacking area as a qualified product to ensure the anti-corrosion quality and anti-corrosion effect of the factory products.

[0015] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0016] Embodiment 1

[0017] like Figure 1 As shown, an embodiment of the present application provides an anti-corrosion treatment method for a steel pipe to be processed, the method comprising:

[0018] S1: determining a steel pipe to be processed, performing image acquisition on the steel pipe to be processed, and obtaining initial image information of the steel pipe to be processed.

[0019] Specifically, the steel pipe to be processed refers to the original steel pipe that has not been treated with anti-corrosion. Through image acquisition, detailed information on the surface of the steel pipe can be obtained in a digital way, laying the foundation for subsequent feature extraction and defect analysis. Among them, image acquisition can use imaging equipment such as industrial cameras and linear array scanners to scan and image the outer surface of the steel pipe to be processed, so as to obtain panoramic initial image information covering the entire outer surface of the steel pipe. The image data contained in the initial image information reflects the original state of the steel pipe surface, providing the required visual input for subsequent defect identification and analysis.

[0020] S2: Extract features from the initial image information to determine surface defect features.

[0021] Specifically, first, a defect recognition model is constructed based on a deep convolutional neural network, which is used to directly perform end-to-end feature extraction and defect recognition on the initial image information. Among them, the defect recognition model consists of multiple convolutional layers, pooling layers, and fully connected layers. In order to build the defect recognition model, a large amount of steel pipe image data with different defects was first collected and labeled as a supervised training data set; then, based on the labeled defect type, defect level and other label information, the defect recognition model was trained using the back propagation algorithm and optimization strategy, so that it gradually learns the mapping relationship from steel pipe images to defect labels. Data enhancement, regularization and other technical means are introduced during the training process to further enhance the generalization ability of the model; after supervised training, the obtained defect recognition model has the ability to extract multi-level visual features from the initial image information and determine the surface defect features.

[0022] Then, the acquired initial image information is input into the defect recognition model. After feature extraction and transformation at the levels of convolution and pooling, the model will give the surface defect characteristics of the steel pipe to be processed at the output end, including defect type, defect location, defect grade and other information, laying the foundation for subsequent defect judgment and anti-corrosion process parameter generation.

[0023] S3: Determine whether the surface defect characteristics meet the steel pipe surface defect constraints.

[0024] Specifically, first, the extracted surface defect features are sorted out, and comprehensive information such as defect type, defect level, and specific location of defects on the steel pipe surface are integrated together to establish a defect feature description that fully describes the distribution state of surface defects on the steel pipe to be processed. At the same time, according to the actual application scenarios and production requirements of the steel pipe, defect limitation standards are formulated in advance as surface defect constraints for the steel pipe. The defect limitation standard specifies in detail the maximum defect level allowed at different positions on the surface of the steel pipe, the acceptable types of defects, and other constraints. Afterwards, the obtained defect feature descriptions are compared and analyzed one by one with the pre-set defect limitation standards. If the defect types of each defect actually existing on the surface of the steel pipe to be processed are all acceptable types, and the defect levels do not exceed the maximum allowable values ​​at that position, then it is determined that the actual defect state of the steel pipe surface meets the preset restriction standards.

[0025] By comparing the actual detected defects with the expected steel pipe surface defect constraints, it is confirmed that the existing defects on the steel pipe surface are within the controllable range and will not have a significant impact on subsequent applications, thereby providing a basis for deciding whether subsequent anti-corrosion treatment is needed and selecting anti-corrosion process parameters.

[0026] S4: If satisfied, anti-corrosion process parameters are generated based on the surface defect characteristics, and the steel pipe to be processed is subjected to anti-corrosion treatment using the anti-corrosion process parameters to obtain a target anti-corrosion pipe.

[0027] Specifically, first, obtain the basic parameters of the steel pipe to be processed, such as steel pipe size, material, use environment, etc., because steel pipes with different parameters have different requirements for anti-corrosion technology. Then, based on the extracted surface defect characteristics, including defect type, defect level, defect location, etc., combined with the basic parameters of the steel pipe, consult the anti-corrosion process parameter manual or expert experience database to obtain a variety of candidate anti-corrosion process solutions. Next, select the optimal anti-corrosion process parameters from the obtained candidate anti-corrosion process solutions based on the anti-corrosion effect. After that, according to the determined anti-corrosion process parameters, a series of anti-corrosion treatments such as spraying, curing, and baking of corresponding anti-corrosion materials are applied to the steel pipe to be processed, and the anti-corrosion treated steel pipe, that is, the target anti-corrosion pipe, can be obtained.

[0028] By fully considering the actual defect conditions of the steel pipe, appropriate anti-corrosion process parameters are selected for the steel pipe to be processed, thereby maximizing the protection effect and ensuring the quality of the target anti-corrosion pipe.

[0029] S5: performing an anti-corrosion quality test on the target anti-corrosion pipe according to the preset anti-corrosion index. If the test result meets the preset anti-corrosion index, the target anti-corrosion pipe is added to the finished product stacking area.

[0030] Specifically, first, a comprehensive anti-corrosion quality inspection standard is formulated, covering multiple dimensions such as appearance inspection, adhesion inspection, and coating thickness inspection, so as to comprehensively evaluate the quality and protective performance of the anti-corrosion layer. Then, according to the determined inspection dimensions, the target anti-corrosion pipe is subjected to corresponding quality inspection to obtain the actual inspection data results of each inspection dimension. At the same time, corresponding to each inspection dimension, the qualified index of anti-corrosion quality is pre-established, and the ideal range or limit value that the inspection data should reach is specified. Next, the actual inspection results of the target anti-corrosion pipe in each inspection dimension are compared and analyzed with the preset anti-corrosion quality index. Only when the result data of all inspection dimensions meet the corresponding qualified index can it be determined that the anti-corrosion quality of the target anti-corrosion pipe meets the requirements and meets the factory standards. For the initial anti-corrosion pipe with qualified anti-corrosion quality, it is transferred to the finished product stacking area, which means that the steel pipe has completed the anti-corrosion treatment of the whole process. On the contrary, if the result data of any inspection dimension fails to meet the corresponding qualified index requirements, the anti-corrosion quality of the initial anti-corrosion pipe is determined to be unqualified and the anti-corrosion treatment needs to be re-implemented.

[0031] Furthermore, the embodiment of the present application also includes:

[0032] The steel pipe to be processed is installed on a rotating bracket; the rotating bracket is controlled to drive the steel pipe to be processed to continuously rotate around its long axis; while the steel pipe to be processed is continuously rotating, a linear array scanning camera is used to scan and image the outer surface of the rotating steel pipe to be processed, and a full-circle image of the steel pipe to be processed is obtained as initial image information.

[0033] In a feasible implementation, in order to capture the image of the steel pipe to be processed and obtain its initial image information. First, the steel pipe to be processed is installed on a rotating bracket so that the steel pipe can rotate continuously around its long axis. Then, the rotating mechanism of the rotating bracket is controlled to work, driving the steel pipe to be processed installed thereon to rotate continuously around the long axis. At the same time, a linear array scanning camera is used to perform linear scanning imaging on the outer surface of the rotating steel pipe. Since the steel pipe is in a continuous rotation state, the scanning camera can gradually obtain full-circle image data of the steel pipe surface, thereby obtaining a full-circle image covering the entire outer surface of the steel pipe as the initial image information. This image acquisition method can effectively overcome the visual angle dead zone problem caused by the steel pipe column structure in ordinary imaging methods, and ensure the integrity and accuracy of the image information.

[0034] Furthermore, the embodiment of the present application also includes:

[0035] The initial image information is gridded and partitioned to obtain a plurality of segmentation unit images; based on the actual size information of the steel pipe to be processed, the actual position coordinates of each segmentation unit image on the surface of the steel pipe to be processed are determined; the plurality of segmentation unit images and the corresponding actual position coordinates are mapped and stored to obtain an image coordinate mapping relationship; a defect recognition model is retrieved, and surface defect features are obtained by combining the plurality of segmentation unit images and the image coordinate mapping relationship.

[0036] In a feasible implementation, first, for the acquired initial image information, a grid partitioning method is used to divide the initial image information into multiple segmentation unit images of the same size according to a preset number of rows and columns. Through grid partitioning, the visual distortion caused by the cylindrical surface of the steel pipe can be effectively overcome, ensuring that each segmentation unit image has a regular size and perspective, which is conducive to subsequent defect identification. Since the segmentation unit image is derived from the initial image information, it is necessary to establish a mapping relationship between the two. Based on the actual size information of the steel pipe to be processed, the actual position coordinates of each segmentation unit image on the surface of the steel pipe are determined, so that the defect features extracted from the segmentation unit image can be accurately mapped to the actual surface of the steel pipe, laying the foundation for further defect analysis and anti-corrosion process parameter determination.

[0037] In order to achieve bidirectional mapping between the segmentation unit image and the actual surface position of the steel pipe, the determined segmentation unit image and its corresponding actual position coordinates are stored in order to obtain a complete image coordinate mapping relationship. The image coordinate mapping relationship can freely query the actual surface position corresponding to a segmentation unit image as needed, or vice versa, find the corresponding segmentation unit image according to a specific surface position. Then, a specially trained defect recognition model is called up, which can learn visual features from image data and intelligently identify defects. After that, the obtained multiple segmentation unit images and the established image coordinate mapping relationship are input into the defect recognition model. The model will perform feature extraction and defect recognition on each segmentation unit image, and combine the image coordinate mapping relationship to output a comprehensive defect feature description of the surface of the steel pipe to be processed, including key information such as defect type, defect level, and defect location, to obtain surface defect features. The output defect features are not only highly accurate, but also can accurately correspond to the actual surface of the steel pipe, laying the foundation for subsequent data processing.

[0038] Furthermore, the embodiment of the present application also includes:

[0039] The defect recognition model includes a unit defect recognition network and a defect position mapping network, wherein the unit defect recognition network includes a defect type recognition branch and a defect level recognition branch; the multiple segmented unit images are sequentially input into the configured defect type recognition branch to obtain multiple defect recognition results; multiple unit defect levels are obtained based on the multiple defect recognition results and the defect level recognition branch; the multiple defect recognition results and the multiple unit defect levels are input into the defect position mapping network, and the initial image information is defect mapped according to the image coordinate mapping relationship to obtain surface defect features.

[0040] In a feasible implementation, the defect recognition model is composed of two networks, namely, a unit defect recognition network and a defect position mapping network. Among them, the unit defect recognition network includes two branches, namely, a defect type recognition branch and a defect level recognition branch. The defect type recognition branch is responsible for identifying and classifying the defect types in the input image; the defect level recognition branch further evaluates the defect level of the identified defect type based on its size, distribution and other characteristics. Then, the obtained multiple segmented unit images are sequentially input into the configured defect type recognition branch, which will perform convolution, pooling and other operations on each input image, extract visual features, and output the corresponding defect recognition results, describing what type of defects exist in the current segmented unit image, thereby obtaining multiple defect recognition results.

[0041] The multiple defect recognition results obtained are combined with the defect level recognition branch to obtain the unit defect level corresponding to each recognition result, and obtain multiple unit defect levels. Specifically, the defect level recognition branch evaluates the level of defects in each defect recognition result according to factors such as defect type and defect size, and outputs a quantitative defect level to indicate the severity of the defect. Then, the output multiple defect recognition results and multiple unit defect levels are input into the defect position mapping network. The defect position mapping network combines the established image coordinate mapping relationship to accurately map the defect characteristics of the initial image information. The output is the surface defect characteristics of the steel pipe to be processed, including the defect type, defect level, and the accurate position information of the defect on the surface of the steel pipe.

[0042] Furthermore, the embodiment of the present application also includes:

[0043] Traverse multiple defect recognition results and extract a first defect recognition result; when the first defect recognition result has defects, retrieve and obtain the first segmentation unit image corresponding to the first defect recognition result; input the first defect recognition result and the first segmentation unit image into the defect level recognition branch to obtain the first unit defect level; bind the first defect recognition result and the first unit defect level and add them to the multiple unit defect levels; when the first defect recognition result is a null value, set the first unit defect level to a null value, bind the first defect recognition result and the first unit defect level and add them to the multiple unit defect levels.

[0044] In a preferred embodiment, in order to obtain the defect level corresponding to each segmentation unit image, first, all the output defect recognition results are traversed, and one defect recognition result is extracted each time as the first defect recognition result. Then, it is checked whether there is defect information in the extracted first defect recognition result. If so, the segmentation unit image corresponding to the first defect recognition result is retrieved and obtained as the first segmentation unit image, thereby associating the defect recognition result with the actual image data. Then, the first defect recognition result and the first segmentation unit image are input into the defect level recognition branch together. The branch will evaluate the specific level of the defect, such as slight, medium, severe, etc., based on the input defect type and image data, and output it as the first unit defect level. Afterwards, the output first unit defect level is bound and associated with the corresponding first defect recognition result, and added to multiple unit defect levels as a complete defect feature description.

[0045] If the inspection finds that the extracted first defect recognition result is a null value, that is, there is no defect in the segmented unit image, the corresponding first unit defect level is also set to a null value. Then the first defect recognition result (null value) and the first unit defect level (null value) are still bound and associated according to the specification, and added to the multiple unit defect levels to maintain integrity. Repeat the defect level judgment until all defect recognition results are traversed, and the defect level information corresponding to all segmented unit images can be obtained to form a complete multiple unit defect levels.

[0046] Furthermore, the embodiment of the present application also includes:

[0047] The defect type, defect level, and defect position in the surface defect feature are extracted to establish a surface defect matrix, wherein the matrix rows correspond to the axial positions of the steel pipe surface, the matrix columns correspond to the circumferential positions of the steel pipe surface, and the values ​​of the matrix elements represent the defect type and defect level of each defect position; according to the steel pipe production requirements of the steel pipe to be processed, a defect constraint matrix is ​​pre-established as the steel pipe surface defect constraint, wherein the matrix rows and columns of the defect constraint matrix correspond to the surface defect matrix, respectively, and the values ​​of the matrix elements represent the allowed defect type and the maximum defect level of each defect position; the surface defect matrix is ​​compared with the defect constraint matrix at the element level, and if the defect types in all elements in the surface defect matrix are allowed defect types, and the defect levels are all less than or equal to the corresponding maximum defect levels, it is determined that the surface defect feature satisfies the steel pipe surface defect constraint.

[0048] In a preferred embodiment, in order to determine whether the extracted surface defect features meet the steel pipe surface defect constraints. First, the surface defect features are orderly organized into a matrix form, the defect type, defect level and specific location information of the defect on the steel pipe surface are extracted from the surface defect features, and a surface defect matrix is ​​established. Among them, the rows of the surface defect matrix represent the axial position of the steel pipe surface, the columns represent the circumferential position of the steel pipe surface, and the values ​​of the matrix elements record the defect type and defect level information at the corresponding position. At the same time, according to the actual production requirements and application scenarios of the steel pipe to be processed, a defect constraint matrix is ​​pre-established as a steel pipe surface defect constraint for judging whether the surface defects of the steel pipe are acceptable. Among them, the matrix rows and columns of the defect constraint matrix correspond to the above-mentioned surface defect matrix one by one, and the values ​​of the matrix elements define the defect types allowed to exist at each surface position and the maximum acceptable defect level.

[0049] Next, the established surface defect matrix is ​​compared element by element with the defect constraint matrix. If the defect types at the corresponding positions of all elements in the surface defect matrix are the types allowed by the defect constraint matrix, and the defect levels do not exceed the maximum allowable level at the corresponding positions, then the surface defect feature is determined to meet the preset steel pipe surface defect constraint. On the contrary, if the defect type at any element position is an unallowable type, or the defect level is higher than the specified maximum defect level, then the surface defect feature is determined to not meet the steel pipe surface defect constraint.

[0050] Furthermore, the embodiment of the present application also includes:

[0051] Determine the pipe material parameters of the steel pipe to be processed; query the anti-corrosion process parameter database according to the surface defect characteristics and the pipe material parameters to obtain a matching anti-corrosion process parameter set; select the optimal anti-corrosion process parameter from the anti-corrosion process parameter set based on historical anti-corrosion effects; perform anti-corrosion treatment on the steel pipe to be processed according to the selected anti-corrosion process parameters to obtain a target anti-corrosion pipe.

[0052] In a preferred embodiment, first, the various pipe material parameters of the steel pipe to be processed are determined, such as the outer diameter, wall thickness, material and other geometric parameters of the steel pipe, as well as the working condition parameters such as the temperature, pressure, and medium of the steel pipe. Then, the extracted surface defect characteristics are combined with the determined steel pipe material parameters, and the pre-established anti-corrosion process parameter database is consulted. The database contains a large number of anti-corrosion process schemes, and each scheme specifies in detail the type of anti-corrosion material, application process, curing parameters, etc. By matching and searching in the database, a set of anti-corrosion process parameters that are consistent with the actual conditions of the steel pipe to be processed is obtained, including multiple candidate anti-corrosion process parameter schemes.

[0053] Then, the multiple candidate anti-corrosion process parameter schemes obtained are screened to determine the anti-corrosion process parameters to be finally used. Historical data is referred to during the screening to analyze the actual anti-corrosion effect achieved by each candidate anti-corrosion process parameter scheme in previous applications. Previously, similar steel pipes were treated with anti-corrosion using the candidate anti-corrosion process parameter scheme. If a good protection effect can be obtained, the parameter is considered to have a high score; conversely, if the anti-corrosion effect is not good, the score is reduced accordingly. By comparing the historical anti-corrosion effects of multiple candidate anti-corrosion process parameter schemes, the optimal anti-corrosion process parameters are determined. Afterwards, the steel pipe to be processed is treated with anti-corrosion according to the selected anti-corrosion process parameters, including spraying, curing, baking and other processes of anti-corrosion materials. After the selected anti-corrosion process parameters complete the anti-corrosion treatment, the target anti-corrosion steel pipe that has been treated with anti-corrosion can be obtained.

[0054] Furthermore, the embodiment of the present application also includes:

[0055] An anti-corrosion quality inspection dimension is set, and the anti-corrosion quality inspection dimension includes appearance inspection, adhesion inspection, coating thickness inspection, pinhole inspection, and electrical property inspection, and a preset anti-corrosion index is configured according to the anti-corrosion quality inspection dimension; the target anti-corrosion pipe is monitored based on the anti-corrosion quality inspection dimension, and the inspection result is obtained, and the inspection result is compared with the preset anti-corrosion index. When the inspection result meets the preset anti-corrosion index, the anti-corrosion quality of the target anti-corrosion pipe is determined to be qualified, and the pipe is added to the finished product stacking area.

[0056] In a feasible implementation, in order to comprehensively evaluate the anti-corrosion quality of the target anti-corrosion pipe, multiple anti-corrosion quality detection dimensions are set, aiming to conduct all-round detection and control of the anti-corrosion quality of the target anti-corrosion pipe from different angles. The anti-corrosion quality detection dimensions include appearance detection, adhesion detection, coating thickness detection, pinhole detection, and electrical performance detection. Among them, appearance detection is used to check the appearance quality of the target anti-corrosion pipe, such as whether there are obvious defects such as blistering, cracking, shedding, and color change; adhesion detection is used to test the adhesion strength between the anti-corrosion coating and the steel pipe substrate to check whether there is a risk of shedding; coating thickness detection is used to detect whether the anti-corrosion coating of the target anti-corrosion pipe reaches the ideal protective thickness; pinhole detection is used to check whether there are tiny pinholes, pores and other defects in the anti-corrosion coating, which may cause leakage; electrical performance detection is used to test the insulation, anti-static and other electrical performance indicators of the anti-corrosion layer. At the same time, for each detection dimension, a quantitative anti-corrosion index standard is pre-established, which stipulates the ideal range or limit value that the corresponding detection data should reach. For example, the anti-corrosion index in the appearance inspection dimension may not allow any obvious appearance defects to exist; the anti-corrosion index in the adhesion inspection dimension may require the adhesion value to be no less than 4MPa; the index in the pinhole inspection dimension may limit the pinhole diameter to no more than 0.5mm, etc.

[0057] After that, according to the set anti-corrosion quality inspection dimensions, the obtained target anti-corrosion pipe is subjected to comprehensive quality inspection, and the actual inspection result data of each dimension is obtained respectively. Then, the actual inspection data is compared one by one with the anti-corrosion index standards pre-set in the corresponding dimensions. Only when the inspection results of all dimensions meet the corresponding index requirements, the overall anti-corrosion quality of the target anti-corrosion pipe is evaluated to reach the qualified level and meet the factory standards. For example, the anti-corrosion quality can be evaluated as qualified only when there are no obvious defects in the appearance inspection, the adhesion value is higher than 4MPa, the coating thickness is within the specification range, there are no excessive pinholes and the electrical insulation performance is good. Once it is determined to be qualified, the target anti-corrosion pipe can be transported to the finished product stacking area, indicating that it has passed the anti-corrosion treatment and quality inspection. On the contrary, if the result data of any inspection dimension fails to meet the anti-corrosion index requirements, such as the existence of large-area blistering, excessively thick coating or substandard adhesion, the anti-corrosion quality of the target anti-corrosion pipe is judged to be unqualified and needs to be returned for anti-corrosion treatment until all inspection dimensions are qualified before entering the finished product stacking.

[0058] In summary, the anti-corrosion treatment method for a steel pipe to be processed provided in the embodiment of the present application has the following technical effects:

[0059] Determine the steel pipe to be processed, collect images of the steel pipe to be processed, obtain the initial image information of the steel pipe to be processed, and lay the foundation for subsequent defect detection and analysis. Extract features from the initial image information and determine the surface defect characteristics to provide support for the implementation of personalized anti-corrosion. Determine whether the surface defect characteristics meet the surface defect constraints of the steel pipe to provide a basis for whether to perform anti-corrosion treatment. If so, generate anti-corrosion process parameters based on the surface defect characteristics, and perform anti-corrosion treatment on the steel pipe to be processed according to the anti-corrosion process parameters to obtain the target anti-corrosion pipe. Improve the anti-corrosion quality by implementing personalized anti-corrosion treatment on the steel pipe. Perform anti-corrosion quality inspection on the target anti-corrosion pipe according to the preset anti-corrosion indicators. If the inspection results meet the preset anti-corrosion indicators, add the target anti-corrosion pipe to the finished product stacking area. Only steel pipes that meet the quality requirements can be stored as qualified products, thereby improving the anti-corrosion quality and anti-corrosion effect.

[0060] Embodiment 2

[0061] Based on the same inventive concept as the anti-corrosion treatment method for a steel pipe to be processed in the aforementioned embodiment, Figure 2 As shown, an embodiment of the present application provides an anti-corrosion treatment system for a steel pipe to be processed, the system comprising:

[0062] The initial image acquisition module 11 is used to determine the steel pipe to be processed, collect images of the steel pipe to be processed, and obtain initial image information of the steel pipe to be processed;

[0063] The defect feature extraction module 12 is used to extract features from the initial image information to determine surface defect features;

[0064] A defect constraint judgment module 13 is used to judge whether the surface defect characteristics meet the steel pipe surface defect constraints;

[0065] The anti-corrosion treatment module 14 is used to generate anti-corrosion process parameters based on the surface defect characteristics if the conditions are met, and to perform anti-corrosion treatment on the steel pipe to be processed by using the anti-corrosion process parameters to obtain a target anti-corrosion pipe;

[0066] The anti-corrosion quality detection module 15 is used to perform an anti-corrosion quality detection on the target anti-corrosion pipe according to a preset anti-corrosion index. If the detection result meets the preset anti-corrosion index, the target anti-corrosion pipe is added to the finished product stacking area.

[0067] Furthermore, the initial image acquisition module 11 includes the following execution steps:

[0068] Installing the steel pipe to be processed on a rotating bracket;

[0069] Controlling the rotating bracket to drive the steel pipe to be processed to continuously rotate around its long axis;

[0070] While the steel pipe to be processed is continuously rotating, a linear array scanning camera is used to scan and image the outer surface of the rotating steel pipe to be processed, and a full-circle image of the steel pipe to be processed is obtained as initial image information.

[0071] Furthermore, the defect feature extraction module 12 includes the following execution steps:

[0072] Performing grid partitioning on the initial image information to obtain a plurality of segmentation unit images;

[0073] Based on the actual size information of the steel pipe to be processed, determining the actual position coordinates of each segmentation unit image on the surface of the steel pipe to be processed;

[0074] Mapping and storing a plurality of segmentation unit images and corresponding actual position coordinates to obtain an image coordinate mapping relationship;

[0075] The defect recognition model is retrieved, and the surface defect features are acquired by combining the plurality of segmentation unit images with the image coordinate mapping relationship.

[0076] Furthermore, the defect feature extraction module 12 further includes the following execution steps:

[0077] The defect recognition model includes a unit defect recognition network and a defect position mapping network, wherein the unit defect recognition network includes a defect type recognition branch and a defect level recognition branch;

[0078] Inputting the plurality of segmentation unit images into the configured defect type recognition branch in sequence to obtain a plurality of defect recognition results;

[0079] Acquire a plurality of unit defect levels according to the plurality of defect identification results and the defect level identification branches;

[0080] The multiple defect recognition results and multiple unit defect levels are input into the defect position mapping network, and defect mapping is performed on the initial image information according to the image coordinate mapping relationship to obtain surface defect features.

[0081] Furthermore, the defect feature extraction module 12 further includes the following execution steps:

[0082] Traversing multiple defect recognition results and extracting a first defect recognition result;

[0083] When the first defect recognition result has a defect, retrieving and acquiring a first segmentation unit image corresponding to the first defect recognition result;

[0084] Inputting the first defect recognition result and the first segmented unit image into a defect level recognition branch to obtain a first unit defect level;

[0085] Binding the first defect identification result and the first unit defect level to add to the multiple unit defect levels;

[0086] When the first defect recognition result is a null value, the first unit defect level is set to a null value, and the first defect recognition result and the first unit defect level are bound and added to the multiple unit defect levels.

[0087] Furthermore, the defect constraint judgment module 13 includes the following execution steps:

[0088] Extract the defect type, defect grade, and defect position from the surface defect characteristics, and establish a surface defect matrix, wherein the matrix rows correspond to the axial positions of the steel pipe surface, the matrix columns correspond to the circumferential positions of the steel pipe surface, and the values ​​of the matrix elements represent the defect type and defect grade of each defect position;

[0089] According to the steel pipe production requirements of the steel pipe to be processed, a defect constraint matrix is ​​pre-established as a steel pipe surface defect constraint, wherein the matrix rows and columns of the defect constraint matrix correspond to the surface defect matrix respectively, and the values ​​of the matrix elements represent the defect type and the maximum defect level allowed at each defect position;

[0090] The surface defect matrix is ​​compared with the defect constraint matrix at the element level. If the defect types in all elements in the surface defect matrix are allowable defect types and the defect levels are less than or equal to the corresponding maximum defect levels, it is determined that the surface defect characteristics meet the steel pipe surface defect constraints.

[0091] Furthermore, the anti-corrosion treatment module 14 includes the following execution steps:

[0092] Determining the pipe material parameters of the steel pipe to be processed;

[0093] According to the surface defect characteristics and the pipe parameters, query the anti-corrosion process parameter database to obtain a matching anti-corrosion process parameter set;

[0094] Selecting the optimal anti-corrosion process parameter from the anti-corrosion process parameter set based on historical anti-corrosion effects;

[0095] According to the selected anti-corrosion process parameters, the steel pipe to be processed is subjected to anti-corrosion treatment to obtain a target anti-corrosion pipe.

[0096] Furthermore, the anti-corrosion quality detection module 15 includes the following execution steps:

[0097] Setting anti-corrosion quality inspection dimensions, which include appearance inspection, adhesion inspection, coating thickness inspection, pinhole inspection, and electrical performance inspection, and configuring preset anti-corrosion indicators according to the anti-corrosion quality inspection dimensions;

[0098] The target anti-corrosion pipe is monitored based on the anti-corrosion quality detection dimension to obtain the detection result, and the detection result is compared with the preset anti-corrosion index. When the detection result meets the preset anti-corrosion index, the anti-corrosion quality of the target anti-corrosion pipe is determined to be qualified and added to the finished product stacking area.

[0099] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0100] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for anti-corrosion treatment of a steel pipe to be processed, characterized in that: The method comprises: Determine a steel pipe to be processed, perform image acquisition on the steel pipe to be processed, and obtain initial image information of the steel pipe to be processed; Extracting features from the initial image information to determine surface defect features; Determining whether the surface defect characteristics satisfy the steel pipe surface defect constraints; If the conditions are met, anti-corrosion process parameters are generated based on the surface defect characteristics, and the steel pipe to be processed is subjected to anti-corrosion treatment by using the anti-corrosion process parameters to obtain a target anti-corrosion pipe; Performing an anti-corrosion quality test on the target anti-corrosion pipe according to the preset anti-corrosion index, and if the test result meets the preset anti-corrosion index, adding the target anti-corrosion pipe into the finished product stacking area; Extracting features from the initial image information to determine surface defect features includes: Acquire multiple segmentation unit images, map and store the multiple segmentation unit images and corresponding actual position coordinates, and acquire image coordinate mapping relationships; Retrieving a defect recognition model, combining the plurality of segmentation unit images and the image coordinate mapping relationship, and acquiring surface defect features; Determining whether the surface defect characteristics meet the steel pipe surface defect constraints includes: Extract the defect type, defect grade, and defect position from the surface defect characteristics, and establish a surface defect matrix, wherein the matrix rows correspond to the axial positions of the steel pipe surface, the matrix columns correspond to the circumferential positions of the steel pipe surface, and the values ​​of the matrix elements represent the defect type and defect grade of each defect position; According to the steel pipe production requirements of the steel pipe to be processed, a defect constraint matrix is ​​pre-established as a steel pipe surface defect constraint, wherein the matrix rows and columns of the defect constraint matrix correspond to the surface defect matrix respectively, and the values ​​of the matrix elements represent the defect type and the maximum defect level allowed at each defect position; The surface defect matrix is ​​compared with the defect constraint matrix at the element level. If the defect types in all elements in the surface defect matrix are allowable defect types and the defect levels are less than or equal to the corresponding maximum defect levels, it is determined that the surface defect characteristics meet the steel pipe surface defect constraints.

2. The anticorrosion treatment method according to claim 1, characterized in that: Capturing an image of the steel pipe to be processed to obtain initial image information of the steel pipe to be processed includes: Installing the steel pipe to be processed on a rotating bracket; Controlling the rotating bracket to drive the steel pipe to be processed to continuously rotate around its long axis; While the steel pipe to be processed is continuously rotating, a linear array scanning camera is used to scan and image the outer surface of the rotating steel pipe to be processed, and a full-circle image of the steel pipe to be processed is obtained as initial image information.

3. The anticorrosion treatment method according to claim 1, characterized in that: Retrieve the defect recognition model, combine the multiple segmentation unit images and the image coordinate mapping relationship, and obtain the surface defect features: The defect recognition model includes a unit defect recognition network and a defect position mapping network, wherein the unit defect recognition network includes a defect type recognition branch and a defect level recognition branch; Inputting the plurality of segmentation unit images into the configured defect type recognition branch in sequence to obtain a plurality of defect recognition results; Acquire a plurality of unit defect levels according to the plurality of defect identification results and the defect level identification branches; The multiple defect recognition results and multiple unit defect levels are input into the defect position mapping network, and defect mapping is performed on the initial image information according to the image coordinate mapping relationship to obtain surface defect features.

4. The anticorrosion treatment method according to claim 3, characterized in that: Acquiring a plurality of unit defect levels according to the plurality of defect identification results and the defect level identification branch, including: Traversing multiple defect recognition results and extracting a first defect recognition result; When the first defect recognition result has a defect, retrieving and acquiring a first segmentation unit image corresponding to the first defect recognition result; Inputting the first defect recognition result and the first segmented unit image into a defect level recognition branch to obtain a first unit defect level; Binding the first defect identification result and the first unit defect level to add to the multiple unit defect levels; When the first defect recognition result is a null value, the first unit defect level is set to a null value, and the first defect recognition result and the first unit defect level are bound and added to the multiple unit defect levels.

5. The anticorrosion treatment method according to claim 1, characterized in that: Generate anti-corrosion process parameters based on the surface defect characteristics, perform anti-corrosion treatment on the steel pipe to be processed by using the anti-corrosion process parameters to obtain a target anti-corrosion pipe, including: Determining the pipe material parameters of the steel pipe to be processed; According to the surface defect characteristics and the pipe parameters, query the anti-corrosion process parameter database to obtain a matching anti-corrosion process parameter set; Selecting the optimal anti-corrosion process parameter from the anti-corrosion process parameter set based on historical anti-corrosion effects; According to the selected anti-corrosion process parameters, the steel pipe to be processed is subjected to anti-corrosion treatment to obtain a target anti-corrosion pipe.

6. The anticorrosion treatment method according to claim 1, characterized in that: The anti-corrosion quality of the target anti-corrosion pipe is tested according to the preset anti-corrosion index. If the test result meets the preset anti-corrosion index, the target anti-corrosion pipe is added to the finished product stacking area, including: Setting anti-corrosion quality inspection dimensions, which include appearance inspection, adhesion inspection, coating thickness inspection, pinhole inspection, and electrical performance inspection, and configuring preset anti-corrosion indicators according to the anti-corrosion quality inspection dimensions; The target anti-corrosion pipe is monitored based on the anti-corrosion quality detection dimension to obtain the detection result, and the detection result is compared with the preset anti-corrosion index. When the detection result meets the preset anti-corrosion index, the anti-corrosion quality of the target anti-corrosion pipe is determined to be qualified and added to the finished product stacking area.

7. An anti-corrosion treatment system for a steel pipe to be processed, characterized in that: For implementing the anticorrosion treatment method according to any one of claims 1 to 6, the system comprises: An initial image acquisition module, the initial image acquisition module is used to determine the steel pipe to be processed, perform image acquisition on the steel pipe to be processed, and obtain initial image information of the steel pipe to be processed; A defect feature extraction module, the defect feature extraction module is used to extract features from the initial image information to determine surface defect features; A defect constraint judgment module, wherein the defect constraint judgment module is used to judge whether the surface defect characteristics meet the steel pipe surface defect constraints; An anti-corrosion treatment module, wherein the anti-corrosion treatment module is used to generate anti-corrosion process parameters based on the surface defect characteristics if the conditions are met, and to perform anti-corrosion treatment on the steel pipe to be processed by using the anti-corrosion process parameters to obtain a target anti-corrosion pipe; The anti-corrosion quality detection module is used to perform an anti-corrosion quality detection on the target anti-corrosion pipe according to a preset anti-corrosion index. If the detection result meets the preset anti-corrosion index, the target anti-corrosion pipe is added to the finished product stacking area.

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