Method for cleaning a strip, strip production system and storage medium

Abstract

The application provides a strip steel cleaning treatment method, a strip steel production system and a storage medium, and relates to the technical field of iron and steel production. The collected surface state data can be automatically identified. If it is identified that the surface of the Nth strip steel includes a pollution area, the area of the pollution area is determined. The area of the pollution area and corresponding cleaning process parameters are input into a tag function model to fit a new target function model. According to the new cleaning process parameters, the cleaning execution mechanism of the cleaning section is controlled, and the N+1th strip steel located in the cleaning section and in the transmission state is cleaned. The Nth strip steel cleaned by the cleaning execution mechanism can be automatically monitored online. The strip steel production system does not need to be stopped, the yield of the Nth strip steel is ensured, and the cleaning parameters of the cleaning execution mechanism are automatically optimized, so that the pollution area of the Nth strip steel cleaned by the cleaning section becomes smaller and smaller over time.

Description

Technical Field

[0001] The present application relates to the technical field of steel production, and in particular to a strip steel cleaning method, a strip steel production system, and a storage medium. Background Art

[0002] In cold rolling, hot-dip galvanizing, and continuous annealing lines, after the pickling process, the strip undergoes a cleaning process to remove residual rolling oil and other contaminants such as iron powder and dust. This prevents the high temperatures generated during the annealing process after pickling, which can cause residue on the strip surface to adhere to the strip and cause roll buildup, thus preventing roll mark defects on the strip surface. To control product quality, the strip is inspected for cleanliness after the cleaning process to determine if the strip surface is clean.

[0003] One current method for checking the cleanliness of cleaned steel strips involves periodically stopping the strips. A worker then crawls into a narrow area beneath the production line to apply a length of tape to the strip surface. After removing the tape, the worker checks to see if it is clean. This method frequently shuts down the strip production system, impacting strip production. Furthermore, the worker's crawling into the narrow area beneath the production line creates safety risks. Summary of the Invention

[0004] The present application provides a strip steel cleaning treatment method, a strip steel production system and a storage medium, which are used to solve the problem in the existing technology that the cleanliness detection of the strip steel after the cleaning process requires frequent control of the strip steel production system to shut down, affecting the output of the strip steel and posing a safety hazard.

[0005] In a first aspect, the present application provides a strip steel cleaning method, which is applied to a server, comprising:

[0006] Controlling a cleaning actuator of the cleaning section to clean the Nth section of the strip steel in a transmission state on the strip steel production line based on configured cleaning process parameters, wherein N is an integer greater than 2;

[0007] receiving surface condition data of an Nth section of steel strip in a transmission state on a steel strip production line collected by a surface condition data collection component, wherein the surface condition data collection component is located on one side of a surface of the Nth section of steel strip and, in a transmission direction of the steel strip production line, is located behind a cleaning actuator of a cleaning section of the steel strip production line;

[0008] Identifying surface condition data of the Nth section of the steel strip, and if it is identified that the surface of the Nth section of the steel strip includes a contaminated area, determining the area of ​​the contaminated area;

[0009] The area of ​​the contaminated area and the corresponding cleaning process parameters are input into a pre-fitted objective function model to obtain a new objective function model; wherein the objective function model is obtained by fitting based on multiple sets of historical cleaning process parameters and their corresponding areas of contaminated areas;

[0010] According to the objective function model, find the new cleaning process parameters corresponding to the minimum contaminated area;

[0011] According to the new cleaning process parameters, the cleaning actuator of the cleaning section is controlled to clean the N+1th section of strip steel that is located in the cleaning section and is in a transmission state.

[0012] In some embodiments, the contaminated area includes an oil-contaminated area and a residual iron-contaminated area, the surface state data includes spectral data and image data, the surface state data acquisition component includes a laser transceiver and a camera, and receiving the surface state data of the Nth section of steel strip in a transmission state on the steel strip production line acquired by the surface state data acquisition component includes: receiving the laser emitted by the laser transceiver to the surface of the Nth section of steel strip, obtaining spectral data of the reflected laser, and image data of the surface of the Nth section of steel strip acquired by the camera;

[0013] The surface state data of the Nth section of the strip is identified, and if it is identified that the surface of the Nth section of the strip includes a contaminated area, the area of ​​the contaminated area is determined, including: determining the oily area on the surface of the Nth section of the strip and the area of ​​the oily area based on the spectral data; and determining the residual iron contaminated area on the surface of the Nth section of the strip and the area of ​​the residual iron contaminated area based on the image data.

[0014] In some embodiments, determining the residual iron contamination area on the surface of the Nth section of strip steel based on the image data includes:

[0015] Convert the image data into a grayscale image and obtain the edge feature map corresponding to the grayscale image;

[0016] According to the edge detection algorithm, the grayscale image is segmented to obtain sub-image areas of different categories;

[0017] Determine the attention level of the corresponding pixel point based on the grayscale value of each pixel point in each grayscale image and the average grayscale value and grayscale entropy of each pixel point in the sub-image area to which the corresponding pixel point belongs;

[0018] Determine the probability that the corresponding sub-image area belongs to the residual iron contamination area based on the total number of pixel rows in each sub-image area, the average attention value of the pixels in the corresponding sub-image area, the grayscale difference value of each two adjacent pixels in each row of pixels in the corresponding sub-image area, and a preset correction factor;

[0019] When the probability that the corresponding sub-image area belongs to the residual iron contamination area is greater than a set probability threshold, the corresponding sub-image area is determined to be the residual iron contamination area.

[0020] In some embodiments, determining the probability that the corresponding sub-image area belongs to the residual iron contamination area based on the total number of rows of pixels in each sub-image area, the average attention value of the pixels in the corresponding sub-image area, the grayscale difference value between each two adjacent pixels in each row of pixels in the corresponding sub-image area, and a preset correction factor includes:

[0021] According to the formula Determine the probability that each sub-image area belongs to the residual iron pollution area, where P k is the probability of belonging to the residual iron pollution area, N k is the total number of rows of pixels in the K-th sub-image area, x is the total number of groups of two adjacent pixels in the m-th row of pixels in the K-th sub-image area, b k，m，n is the grayscale difference between the nth group of two adjacent pixels in the mth row of pixels in the Kth sub-image area, is the correction factor, is the mean attention value of the pixels in the K-th sub-image area.

[0022] In some embodiments, before receiving the surface condition data of the Nth section of the strip steel collected by the surface condition data collection component, the method provided by the present application further includes:

[0023] The control is to control the first purge component to purge one side of the surface of the Nth section of strip steel, and to control the second purge component to purge one side of the surface of the Nth section of strip steel, wherein, in the transmission direction of the strip steel production line, the first purge component is located between the cleaning actuator and the laser transceiver of the cleaning section of the strip steel production line, and the second purge component is located between the laser transceiver and the camera.

[0024] In some embodiments, determining the attention level of a corresponding pixel point based on the grayscale value of each pixel point in each grayscale image and the average grayscale value and grayscale entropy of each pixel point in the sub-image area to which the corresponding pixel point belongs includes:

[0025] According to the formula Determine the attention level of the corresponding pixel, where hi,j represents the grayscale value of the jth pixel in the i-th sub-image area. represents the average grayscale value of the ith sub-image area, Ti represents the average grayscale entropy value of the ith sub-image area, and Pij represents the attention level of the jth pixel in the ith sub-image area.

[0026] In some embodiments, after receiving the surface condition data of the Nth section of the strip steel in a transmission state on the strip steel production line collected by the surface condition data collection component, the method provided by the present application further includes:

[0027] Send surface status data to the monitoring terminal for display.

[0028] In the second aspect, the present application also provides a strip steel production system, including a strip steel production line, a surface state data acquisition component, and a server. The strip steel production line includes a cleaning section provided with a cleaning actuator, the cleaning actuator is used to clean the Nth section of strip steel transmitted on the strip steel production line, the surface state data acquisition component is located on one side of the surface of the Nth section of strip steel, and in the transmission direction of the strip steel production line, the surface state data acquisition component is located after the cleaning section of the strip steel production line, the surface state data acquisition component is used to collect surface state data of the Nth section of strip steel in a transmission state on the strip steel production line, and the server is used to execute the method provided in the first aspect of the present application.

[0029] In some embodiments, the strip steel production system further includes a first stabilizing roller and a second stabilizing roller arranged at intervals, the first stabilizing roller and the second stabilizing roller being used to transmit the Nth section of strip steel, and in the transmission direction of the strip steel production line, the surface state data acquisition component is located between the first stabilizing roller and the second stabilizing roller.

[0030] In some embodiments, a protective cover is provided outside the surface state data acquisition component.

[0031] In a third aspect, the present application further provides a storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method provided in the first aspect of the present application.

[0032] The present application provides a strip steel cleaning processing method, a strip steel production system, and a storage medium. Since the surface state data acquisition component is located after the cleaning actuator of the cleaning section of the strip steel production line in the transmission direction of the strip steel production line, the surface state data acquisition component collects the surface state data of the Nth section of strip steel after being cleaned by the cleaning actuator. The collected surface state data can then be automatically identified. If the surface state data of the Nth section of strip steel is identified, and if it is determined that the surface of the Nth section of strip steel includes a contaminated area, the area of ​​the contaminated area is determined. The area of ​​the contaminated area and the corresponding cleaning process parameters are input into a pre-fitted objective function model to fit a new objective function model. Based on the new cleaning process parameters, the cleaning actuator of the cleaning section is controlled to clean the N+1th section of strip steel located in the cleaning section and in a transmission state. In this way, it is possible to automatically monitor online whether the Nth section of strip steel after being cleaned by the cleaning actuator is clean, without having to control the strip steel production system to shut down, thereby ensuring the output of the Nth section of strip steel and reducing safety hazards. Furthermore, the cleaning parameters of the cleaning actuator can be automatically optimized so that the contaminated area of ​​the Nth strip steel after cleaning becomes smaller and smaller over time, so that the strip steel can be cleaned completely. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0034] Figure 1 A schematic diagram of the structure of a strip steel production system provided in an embodiment of the present application;

[0035] Figure 2 A flow chart of a strip steel cleaning method provided in an embodiment of the present application;

[0036] Figure 3 A specific flow chart for determining the residual iron contamination area on the surface of the Nth section of strip steel based on image data provided in an embodiment of the present application;

[0037] Figure 4 This is a functional module block diagram of the strip cleaning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely illustrative and are not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0039] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments of the present disclosure. These figures are not drawn to scale, and for the purpose of clarity, certain details are exaggerated and certain details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0040] In the context of the present disclosure, when a layer / element is referred to as being "on" another layer / element, it can be directly on the other layer / element or an intervening layer / element may be present therebetween. In addition, if a layer / element is "on" another layer / element in one orientation, it may be "below" the other layer / element when the orientation is reversed.

[0041] The following describes in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0042] The embodiment of the present application provides a strip steel cleaning method, which is applied to a server 10, which belongs to a strip steel production system. Figure 1 As shown, the strip steel production system also includes a strip steel production line, a surface condition data collection component, a server 10, and a monitoring terminal 9. The strip steel production line includes a cleaning section 11 equipped with a cleaning actuator. The cleaning actuator is used to clean the Nth section of steel strip 1 being transported on the strip steel production line. The surface condition data collection component is located on one side of the surface of the Nth section of steel strip 1 and, in the direction of transport of the strip steel production line, is located after the cleaning section of the strip steel production line. The surface condition data collection component is used to collect surface condition data of the Nth section of steel strip 1 being transported on the strip steel production line. It should be noted that, in the direction of transport of the strip steel production line, the cleaning section 11 of the strip steel production line is preceded by a pickling section, which is used to pickle the Nth section of steel strip 1. The pickling process may include pickling the Nth section of steel strip 1 using shallow turbulent hydrochloric acid to remove oxide scale and impurities on the surface of the Nth section of steel strip 1 and improve the quality of the Nth section of steel strip 1. The Nth section of steel strip 1 is then rolled using a rolling mill. In the transmission direction of the strip steel production line, the annealing section is located after the surface state data acquisition component, and the annealing section is used to anneal the Nth section of strip steel 1. Specifically, Figure 2As shown, the strip steel cleaning method provided in the embodiment of the present application includes S201-S204, wherein:

[0043] S200: Controlling the cleaning actuator of the cleaning section 11 to clean the Nth section of the steel strip 1 in a transmission state on the steel strip production line based on the configured cleaning process parameters, wherein N is an integer greater than 2.

[0044] S201: Receive surface state data of the Nth section of the strip steel 1 in a transmission state on the strip steel production line collected by a surface state data collection component.

[0045] The surface condition data acquisition component is located on one side of the surface of the Nth section of steel strip 1 and, in the transmission direction of the steel strip production line, is located behind the cleaning actuator of the cleaning section of the steel strip production line. The server 10 may include a PCI (Peripheral Component Interconnection, PCI) slot, in which a data acquisition card is installed. The data acquisition card can be used to receive surface condition data collected by the surface condition data acquisition component.

[0046] Specifically, the surface state data may include spectral data and image data. The surface state data acquisition component includes a laser transceiver and a camera 5. For example, the camera 5 may be, but is not limited to, a high-speed industrial CCD camera. The laser transceiver is used to emit laser light onto the surface of the Nth section of the steel strip 1, acquire spectral data of the reflected laser light, and transmit it to the server 10. The laser transceiver includes an excitation transmitter 7 for emitting laser light onto the surface of the Nth section of the steel strip 1, and an excitation receiver 8 for receiving and acquiring spectral data of the reflected laser light. The camera 5 is used to acquire image data of the surface of the Nth section of the steel strip 1 and transmit it to the server 10.

[0047] It should be noted that, in the transmission direction of the strip production line, the first purge assembly 4 is located between the cleaning actuator and the laser transceiver of the cleaning section of the strip production line, and the second purge assembly 12 is located between the laser transceiver and the camera 5. The server 10 can control the first purge assembly 4 to purge one side of the surface of the Nth section of steel strip 1, and control the second purge assembly 5 to purge one side of the surface of the Nth section of steel strip 1. In this way, the first purge assembly 4 can purge the Nth section of steel strip 1 to remove dust and other impurities from its surface before it is scanned by the laser transceiver, and the second purge assembly 12 can purge the Nth section of steel strip 1 to remove dust and other impurities from its surface before it is captured by the camera 5, thereby improving the reliability of the subsequently collected spectral data and image data.

[0048] It should be noted that the surface state data can be sent to the monitoring terminal 9 for display for the monitoring personnel to browse.

[0049] S202: identifying the surface state data of the Nth strip steel 1, and if it is identified that the surface of the Nth strip steel 1 includes a pollution area, determining the area of the pollution area.

[0050] Specifically, the pollution area on the surface of the Nth strip steel 1 can include an oil pollution area and a residual iron pollution area. S201 can be specifically implemented as receiving the spectrum data of the reflected laser after the laser transceiver emits laser to the surface of the Nth strip steel 1, and the image data of the surface of the Nth strip steel 1 collected by the camera 5, then S202 can be specifically implemented as: determining the oil pollution area on the surface of the Nth strip steel 1 and the area of the oil pollution area according to the spectrum data; and determining the residual iron pollution area on the surface of the Nth strip steel 1 and the area of the residual iron pollution area according to the image data.

[0051] It can be understood that the reflectivity of the oil pollution area to the laser is obviously higher than that of other materials, so the accuracy of determining the oil pollution area on the surface of the Nth strip steel 1 according to the spectrum data is high. The reflectivity of the residual iron pollution area to light is obviously lower than that of other materials, so the reflectivity of the residual iron pollution area to the camera flash is low, which ensures the reliability of the collected image data, and the reliability of the residual iron pollution area identified by the image data is also high.

[0052] Specifically, as shown in Figure 3 , determining the residual iron pollution area on the surface of the Nth strip steel 1 according to the image data can be specifically implemented as S301-S305. Among them,

[0053] S301: converting the image data into a gray-scale image and obtaining an edge feature map corresponding to the gray-scale image.

[0054] S302: segmenting the gray-scale image according to an edge detection algorithm to obtain sub-image regions of different categories.

[0055] Exemplarily, the first principal component direction of the edge feature map can be obtained by using a principal component analysis (PCA) algorithm, and the first principal component direction is taken as a texture direction to obtain all textures in the gray-scale image, wherein each texture is used to represent a category of sub-image region. Exemplarily, the gray-scale image can be segmented into three categories of sub-regions, and the three categories of sub-regions are an oil pollution area, a residual iron pollution area and an un-polluted area.

[0056] S303: Determine the attention level of the corresponding pixel point according to the grayscale value of each pixel point in each grayscale image, and the average grayscale value and grayscale entropy of each pixel point in the sub-image area to which the corresponding pixel point belongs.

[0057] For example, corresponding to pixel A, when pixel A is located in an oil-polluted area, the attention level of pixel A is determined based on the grayscale value of pixel A, the average grayscale value and grayscale entropy of each pixel in the oil-polluted area to which pixel A belongs.

[0058] Specifically, according to the formula Determine the attention level of the corresponding pixel, where hi,j represents the grayscale value of the jth pixel in the i-th sub-image area. represents the average grayscale value of the ith sub-image area, Ti represents the average grayscale entropy value of the ith sub-image area, and Pij represents the attention level of the jth pixel in the ith sub-image area.

[0059] S304: Determine the probability that the corresponding sub-image area belongs to the residual iron contamination area based on the total number of rows of pixels in each sub-image area, the average attention value of the pixels in the corresponding sub-image area, the grayscale difference value of each two adjacent pixels in each row of pixels in the corresponding sub-image area, and the preset correction factor.

[0060] Specifically, S304 can be implemented as follows: Determine the probability that each sub-image area belongs to the residual iron pollution area. k is the probability of belonging to the residual iron pollution area, N k is the total number of rows of pixels in the K-th sub-image area, x is the total number of groups of two adjacent pixels in the m-th row of pixels in the K-th sub-image area, b k，m，n is the grayscale difference between the nth group of two adjacent pixels in the mth row of pixels in the Kth sub-image area, is the correction factor, is the mean attention value of the pixels in the K-th sub-image area.

[0061] S305: When the probability that the corresponding sub-image area belongs to the residual iron contamination area is greater than a set probability threshold, determine that the corresponding sub-image area is the residual iron contamination area.

[0062] For example, it may be but not limited to 70%, 80% or 85%, etc., which is not limited here.

[0063] S203: Inputting the area of ​​the contaminated region and the corresponding cleaning process parameters into the pre-fitted objective function model to obtain a new objective function model.

[0064] The objective function model is obtained by fitting multiple historical sets of cleaning process parameters and their corresponding contaminated area areas. For example, the objective function model can be obtained by fitting multiple sets of cleaning process parameters and their corresponding contaminated area areas using a BP neural network. It is understood that as the value of N increases, the more contaminated area areas and corresponding cleaning process parameter sets are used to fit the objective function model, and the more reliable the resulting objective function model becomes.

[0065] For example, the cleaning parameters of the cleaning execution structure such as the conductivity of the alkali washing tank, the temperature of the alkali solution, the current density of the electrolytic cell, the temperature of the electrolytic cell solution, and the conductivity of the electrolytic cell may be updated.

[0066] S204: Finding new cleaning process parameters corresponding to the minimum contaminated area according to the objective function model.

[0067] Specifically, the ant colony algorithm can be used to find new cleaning process parameters corresponding to the minimum contaminated area based on the objective function model. The objective function model can be understood as a function curve with both high and low points. The cleaning process parameters corresponding to the lowest point (i.e., the point with the minimum contaminated area) can be found as the new cleaning process parameters.

[0068] S205: According to the new cleaning process parameters, the cleaning actuator of the cleaning section 11 is controlled to clean the N+1th section of the strip steel located in the cleaning section 11 and in a transmission state.

[0069] In summary, the embodiment of the present application provides a strip cleaning method. Since the surface state data acquisition component is located after the cleaning actuator of the cleaning section of the strip production line in the transmission direction of the strip production line, the surface state data acquisition component collects the surface state data of the Nth section of strip after being cleaned by the cleaning actuator. The collected surface state data can then be automatically identified. If the surface state data of the Nth section of strip is identified, if it is identified that the surface of the Nth section of strip includes a contaminated area, the area of ​​the contaminated area and the corresponding cleaning process parameters are input into the pre-fitted objective function model to fit a new objective function model. According to the new cleaning process parameters, the cleaning actuator of the cleaning section is controlled to clean the N+1th section of strip located in the cleaning section and in the transmission state. In this way, it is possible to automatically monitor online whether the Nth section of strip cleaned by the cleaning actuator is clean, without the need to control the strip production system to shut down, thereby ensuring the output of the Nth section of strip and reducing safety hazards. Furthermore, the cleaning parameters of the cleaning actuator can be automatically optimized so that the contaminated area of ​​the Nth strip steel after cleaning becomes smaller and smaller over time, so that the strip steel can be cleaned completely.

[0070] In addition, the present application also provides a strip steel production system, including a strip steel production line, a surface state data acquisition component, and a server 10. The strip steel production line includes a cleaning section 11 provided with a cleaning actuator. It should be noted that the basic principle and technical effects of the strip steel production system provided in the embodiment of the present application are the same as those in the above-mentioned embodiment. For the sake of brief description, for parts not mentioned in the embodiment of the present application, reference can be made to the corresponding contents in the above-mentioned embodiment. For example, the cleaning actuator is used to clean the Nth section of strip steel 1 transmitted on the strip steel production line, the surface state data acquisition component is located on one side of the surface of the Nth section of strip steel 1, and in the transmission direction of the strip steel production line, the surface state data acquisition component is located after the cleaning section of the strip steel production line, the surface state data acquisition component is used to collect the surface state data of the Nth section of strip steel 1 in the transmission state on the strip steel production line, and the server 10 is used to execute the method provided in the above-mentioned embodiment of the present application.

[0071] In some embodiments, the strip production system further includes a first stabilizing roller 2 and a second stabilizing roller 3 spaced apart from each other, configured to convey the Nth section of the strip 1. The surface condition data collection assembly is located between the first stabilizing roller 2 and the second stabilizing roller 3 in the conveying direction of the strip production line. The first stabilizing roller 2 and the second stabilizing roller 3 suppress vibration of the Nth section of the strip 1 while in motion, thereby ensuring the reliability of the surface condition data collected for the Nth section of the strip 1. Optionally, a protective cover 6 is provided over the surface condition data collection assembly to protect it from external corrosion.

[0072] In addition, if Figure 4 As shown, the embodiment of the present application also provides a strip steel cleaning processing device, which is configured on the server 10. It should be noted that the basic principle and technical effects of the strip steel cleaning processing device provided in the embodiment of the present application are the same as those of the above embodiment. For the sake of brief description, for parts not mentioned in the embodiment of the present application, reference can be made to the corresponding content in the above embodiment. Specifically, the strip steel cleaning processing device provided in the embodiment of the present application includes an information receiving unit, a data recognition unit, a model optimization unit, a parameter search unit, and a strip steel cleaning unit, wherein,

[0073] The strip cleaning unit is used to control the cleaning actuator of the cleaning section to clean the Nth strip in the transmission state on the strip production line based on the configured cleaning process parameters, where N is an integer greater than 2.

[0074] The information receiving unit is used to receive the surface state data of the Nth section of the strip steel 1 in the transmission state on the strip steel production line collected by the surface state data collection component.

[0075] The surface state data acquisition component is located on one side of the surface of the Nth section of strip steel 1, and in the transmission direction of the strip steel production line, the surface state data acquisition component is located behind the cleaning actuator of the cleaning section of the strip steel production line.

[0076] a data recognition unit for recognizing surface state data of the Nth section of the steel strip 1, and determining the area of ​​the contaminated area if it is recognized that the surface of the Nth section of the steel strip 1 includes a contaminated area;

[0077] The model optimization unit is used to input the area of ​​the contaminated area and the corresponding cleaning process parameters into a pre-fitted objective function model to fit a new objective function model; wherein the objective function model is fitted based on multiple sets of historical cleaning process parameters and their corresponding areas of contaminated areas.

[0078] The parameter search unit is used to find the new cleaning process parameters corresponding to the minimum contaminated area according to the objective function model.

[0079] The strip cleaning unit is also used to control the cleaning actuator of the cleaning section according to new cleaning process parameters to clean the N+1th strip located in the cleaning section and in a transmission state.

[0080] In some embodiments, the contaminated area includes an oil-contaminated area and a residual iron-contaminated area, the surface state data includes spectral data and image data, and the surface state data acquisition component includes a laser transceiver and a camera 5. The information receiving unit is specifically configured to receive the spectral data of the reflected laser light after the laser transceiver transmits the laser light to the surface of the Nth section of the steel strip 1, and obtain the image data of the surface of the Nth section of the steel strip 1 acquired by the camera 5.

[0081] The data recognition unit is specifically used to determine the oil pollution area and the area of ​​the oil pollution area on the surface of the Nth section of strip 1 based on spectral data; and to determine the residual iron pollution area and the area of ​​the residual iron pollution area on the surface of the Nth section of strip 1 based on image data.

[0082] Furthermore, the data recognition unit is specifically used to convert image data into a grayscale image and obtain an edge feature map corresponding to the grayscale image; segment the grayscale image according to the edge detection algorithm to obtain sub-image areas of different categories; determine the attention degree of the corresponding pixel point according to the grayscale value of each pixel point in each grayscale image, and the average grayscale value and grayscale entropy of each pixel point in the sub-image area to which the corresponding pixel point belongs; determine the probability that the corresponding sub-image area belongs to the residual iron contamination area according to the total number of rows of pixels in each sub-image area, the average attention degree of the pixels in the corresponding sub-image area, the grayscale difference value of each adjacent two pixels in each row of pixels in the corresponding sub-image area, and a preset correction factor; when the probability that the corresponding sub-image area belongs to the residual iron contamination area is greater than the set probability threshold, determine that the corresponding sub-image area is a residual iron contamination area.

[0083] In some embodiments, the data identification unit is specifically configured to: Determine the probability that each sub-image area belongs to the residual iron pollution area. k is the probability of belonging to the residual iron pollution area, N k is the total number of rows of pixels in the K-th sub-image area, x is the total number of groups of two adjacent pixels in the m-th row of pixels in the K-th sub-image area, b k，m，n is the grayscale difference between the nth group of two adjacent pixels in the mth row of pixels in the Kth sub-image area, is the correction factor, is the mean attention value of the pixels in the K-th sub-image area.

[0084] In addition, an embodiment of the present application further provides a storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method provided in the above embodiment of the present application.

[0085] In addition, an embodiment of the present application also provides a computer program product, including a computer program, which, when executed, enables the server to execute the method provided in the above embodiment of the present application.

[0086] While the above description does not provide detailed technical details regarding the patterning of each layer, those skilled in the art will appreciate that various technical means can be employed to form layers, regions, and the like in desired shapes. Furthermore, those skilled in the art may devise methods that differ from those described above to achieve the same structure. Furthermore, while each embodiment has been described separately, this does not mean that the measures in each embodiment cannot be advantageously combined.

[0087] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0088] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A strip steel cleaning method, characterized in that: Applied to a server, the method includes: Controlling a cleaning actuator of the cleaning section to clean the Nth section of the strip steel in a transmission state on the strip steel production line based on configured cleaning process parameters, wherein N is an integer greater than 2; receiving surface condition data of an Nth section of steel strip in a transmission state on a steel strip production line collected by a surface condition data collection component, wherein the surface condition data collection component is located on one side of the surface of the Nth section of steel strip and, in the transmission direction of the steel strip production line, the surface condition data collection component is located behind a cleaning actuator of a cleaning section of the steel strip production line; Identifying surface condition data of the Nth section of the steel strip, and if it is identified that the surface of the Nth section of the steel strip includes a contaminated area, determining the area of ​​the contaminated area; Inputting the area of ​​the contaminated area and the corresponding cleaning process parameters into a pre-fitted objective function model to obtain a new objective function model; wherein the objective function model is obtained by fitting based on multiple sets of historical cleaning process parameters and their corresponding areas of contaminated areas; According to the objective function model, new cleaning process parameters corresponding to the minimum contaminated area are found; According to the new cleaning process parameters, the cleaning execution mechanism of the cleaning section is controlled to clean the N+1th section of strip steel located in the cleaning section and in a transmission state.

2. The method according to claim 1, characterized in that The contaminated area includes an oil contaminated area and a residual iron contaminated area, the surface state data includes spectral data and image data, the surface state data acquisition component includes a laser transceiver and a camera, and the surface state data of the Nth section of strip steel in a transmission state on the strip steel production line collected by the surface state data acquisition component includes: After receiving the laser emitted by the laser transceiver to the surface of the Nth section of the strip steel, spectrum data of the reflected laser and image data of the surface of the Nth section of the strip steel captured by the camera are obtained; The identifying of the surface state data of the Nth section of the steel strip, and if it is identified that the surface of the Nth section of the steel strip includes a contaminated area, determining the area of ​​the contaminated area, includes: Based on the spectral data, the oily contaminated area on the surface of the Nth section of the strip and the area of ​​the oily contaminated area are determined; and based on the image data, the residual iron contaminated area on the surface of the Nth section of the strip and the area of ​​the residual iron contaminated area are determined.

3. The method according to claim 2, characterized in that Determining the residual iron contamination area on the surface of the Nth section of strip steel based on the image data includes: Converting the image data into a grayscale image and obtaining an edge feature map corresponding to the grayscale image; Segmenting the grayscale image according to an edge detection algorithm to obtain sub-image regions of different categories; Determine the attention level of the corresponding pixel point based on the grayscale value of each pixel point in each grayscale image and the average grayscale value and grayscale entropy of each pixel point in the sub-image area to which the corresponding pixel point belongs; Determining the probability that the corresponding sub-image area belongs to the residual iron contamination area based on the total number of rows of pixels in each sub-image area, the average attention value of the pixels in the corresponding sub-image area, the grayscale difference value of each two adjacent pixels in each row of pixels in the corresponding sub-image area, and a preset correction factor; When the probability that the corresponding sub-image area belongs to the residual iron contamination area is greater than a set probability threshold, the corresponding sub-image area is determined to be the residual iron contamination area.

4. The method according to claim 3, characterized in that The method of determining the probability that the corresponding sub-image area belongs to the residual iron contamination area according to the total number of rows of pixels in each sub-image area, the average attention value of the pixels in the corresponding sub-image area, the grayscale difference value of each two adjacent pixels in each row of pixels in the corresponding sub-image area, and a preset correction factor includes: According to the formula Determine the probability that each sub-image area belongs to the residual iron pollution area, where P k is the probability of belonging to the residual iron pollution area, N k is the total number of rows of pixels in the K-th sub-image area, x is the total number of groups of two adjacent pixels in the m-th row of pixels in the K-th sub-image area, b k，m，n is the grayscale difference between the nth group of two adjacent pixels in the mth row of pixels in the Kth sub-image area, is the correction factor, is the mean attention value of the pixels in the K-th sub-image area.

5. The method according to claim 3, characterized in that Before receiving the surface condition data of the Nth section of the strip steel collected by the surface condition data collection component, the method further includes: The first purge component is controlled to purge one side of the surface of the Nth section of strip steel, and the second purge component is controlled to purge one side of the surface of the Nth section of strip steel, wherein, in the transmission direction of the strip steel production line, the first purge component is located between the cleaning actuator of the cleaning section of the strip steel production line and the laser transceiver, and the second purge component is located between the laser transceiver and the camera.

6. The method according to claim 3, characterized in that The determining of the attention level of the corresponding pixel point according to the grayscale value of each pixel point in each grayscale image and the average grayscale value and grayscale entropy of each pixel point in the sub-image area to which the corresponding pixel point belongs includes: According to the formula Determine the attention of the corresponding pixel, where h i,j represents the gray value of the jth pixel in the i-th sub-image area, represents the average gray value of the i-th sub-image area, T i represents the average grayscale entropy value of the i-th sub-image area, P ij Indicates the attention level of the jth pixel in the i-th sub-image area.

7. A strip steel production system, characterized in that: The strip steel production system includes a strip steel production line, a surface state data acquisition component, and a server. The strip steel production line includes a cleaning section provided with a cleaning actuator. The cleaning actuator is used to clean the Nth section of strip steel transmitted on the strip steel production line. The surface state data acquisition component is located on one side of the surface of the Nth section of strip steel, and in the transmission direction of the strip steel production line, the surface state data acquisition component is located after the cleaning section of the strip steel production line. The surface state data acquisition component is used to collect surface state data of the Nth section of strip steel in a transmission state on the strip steel production line. The server is used to execute any method described in claims 1-6.

8. The system according to claim 7, characterized in that The strip steel production system also includes a first stabilizing roller and a second stabilizing roller arranged at intervals, the first stabilizing roller and the second stabilizing roller are used to transmit the Nth section of strip steel, and in the transmission direction of the strip steel production line, the surface state data acquisition component is located between the first stabilizing roller and the second stabilizing roller.

9. The system according to claim 7, wherein: A protective cover is provided outside the surface state data acquisition component.

10. A storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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