Converter lining thickness determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a converter lining thickness determination method and device, electronic equipment and a storage medium. According to the specific scheme, after smelting of a current heat is completed, smelting parameters and an infrared image of a target converter are obtained, and the infrared image is an image of a local area of the target converter; according to the infrared image, the target furnace lining temperature of the target converter is determined, and according to the target furnace lining temperature and the smelting parameters, the corresponding predicted furnace lining stripping amount of the target converter under the current heat is determined; according to the initial furnace lining thickness information and the predicted furnace lining stripping amount, the target furnace lining thickness corresponding to the target converter is determined; wherein the initial furnace lining thickness information is a furnace lining thickness parameter determined by the target converter before the current smelting. The method improves the efficiency and accuracy of determining the thickness of the converter lining.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method, device, electronic equipment and storage medium for determining the thickness of a converter lining. Background Art

[0002] A converter generally refers to a cylindrical oxygen-blowing steelmaking vessel that can be tilted. The lining of the converter is usually made of magnesia-carbon bricks. However, during the smelting process of the liquid in the converter, the addition of scrap steel and molten iron, the slag scouring during the oxygen-blowing steelmaking process, and the heat radiation of the molten steel will cause wear or erosion on the lining of the converter. Over time, the lining of the converter may be burned through, threatening the safety of on-site workers.

[0003] At present, the method for detecting the thickness of the converter lining is mainly to send out a laser signal through a 3D laser scanner, receive the laser reflection signal through a laser receiver, and determine the thickness of the converter lining through the time interval between the laser sending signal and the laser receiving signal. However, when measuring the thickness of the converter lining based on laser, there are positions of the converter lining that are inconvenient for laser measurement. For positions in the converter lining that are inconvenient for laser measurement, manual measurement of the lining thickness is usually adopted. However, measuring the lining thickness based on manual methods is not only time-consuming and labor-intensive, but also cannot guarantee the accuracy of the measurement results. Summary of the invention

[0004] The present invention provides a method, a device, an electronic device and a storage medium for determining the thickness of a converter lining, which can accurately and quickly determine the thickness of the lining of a target converter.

[0005] According to one aspect of the present invention, a method for determining the thickness of a converter lining is provided, the method comprising:

[0006] After the current heat smelting is completed, the smelting parameters and infrared image of the target converter are obtained, wherein the infrared image is an image of a local area of ​​the target converter;

[0007] According to the infrared image, the target lining temperature of the target converter is determined, and according to the target lining temperature and smelting parameters, the predicted lining spalling amount corresponding to the target converter in the current furnace is determined;

[0008] Determine the target lining thickness corresponding to the target converter according to the initial lining thickness information and the predicted lining spalling amount;

[0009] The initial lining thickness information is the lining thickness parameter determined before the current furnace smelting of the target converter.

[0010] According to another aspect of the present invention, a device for determining the thickness of a converter lining is provided, the device comprising:

[0011] A data acquisition module, used to acquire smelting parameters and infrared images of a target converter after the current heat smelting is completed, wherein the infrared image is an image of a local area of ​​the target converter;

[0012] The spalling amount determination module is used to determine the target lining temperature of the target converter according to the infrared image, and determine the predicted lining spalling amount corresponding to the target converter in the current heat according to the target lining temperature and smelting parameters;

[0013] A lining thickness determination module is used to determine the target lining thickness corresponding to the target converter according to the initial lining thickness information and the predicted lining spalling amount;

[0014] The initial lining thickness information is the lining thickness parameter determined before the current furnace smelting of the target converter.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program executable by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the method for determining the thickness of a converter lining according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for enabling a processor to implement the method for determining the thickness of a converter lining according to any embodiment of the present invention when the computer instructions are executed.

[0020] According to another aspect of the present invention, there is provided a computer program product, comprising a computer program, wherein the computer program, when executed by a processor, implements a method for determining the thickness of a converter lining according to any embodiment of the present invention.

[0021] The technical solution of the embodiment of the present invention provides data support for the subsequent determination of the lining thickness of the target converter by obtaining the smelting parameters and infrared images of the target converter after the current heat smelting is completed. According to the infrared image, the target lining temperature of the target converter is determined. And according to the target lining temperature and smelting parameters, the predicted lining spalling amount of the target converter under the current heat is determined, so as to determine the lining thickness of the target converter after the current heat smelting is completed according to the predicted lining spalling amount. According to the lining thickness parameters determined before the current heat smelting of the target converter and the predicted lining spalling amount, the target lining thickness of the target converter is determined. The present invention solves the problem of incomplete measurement caused by laser measurement of lining thickness and the problem of low efficiency and accuracy caused by manual measurement of lining thickness in the prior art, and quickly and accurately determines the target lining thickness of the target converter through infrared images and smelting parameters, which not only improves the efficiency and accuracy of determining the lining thickness, but also effectively avoids the situation where the lining is burned through, ensuring the safety of on-site workers.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a method for determining the thickness of a converter lining provided by an embodiment of the present invention;

[0025] Figure 2 is an example diagram of an original infrared image provided by an embodiment of the present invention;

[0026] Figure 3 is an example diagram of an original grayscale image provided by an embodiment of the present invention;

[0027] Figure 4 is an example diagram of processing a denoised grayscale image using a preset gradient operator provided in an embodiment of the present invention;

[0028] Figure 5 is an example diagram of a local area provided by an embodiment of the present invention;

[0029] Figure 6 is a flow chart of a method for determining the thickness of a converter lining provided by an embodiment of the present invention;

[0030] Figure 7 is an example diagram of training a furnace lining spalling amount prediction model to be trained provided by an embodiment of the present invention;

[0031] Figure 8 is a line graph of prediction error provided by an embodiment of the present invention;

[0032] Fig. 9 It is a line graph of the actual lining spalling amount and the spalling thickness corresponding to the theoretical lining spalling amount provided by the embodiment of the present invention;

[0033] Fig.10 It is a structural schematic diagram of a device for determining the thickness of a converter lining provided by an embodiment of the present invention;

[0034] Fig.11 It is a schematic diagram of the structure of an electronic device for implementing the method for determining the thickness of a converter lining according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] Embodiment 1

[0038] Figure 1This is a flow chart of a method for determining the thickness of a converter lining provided by the first embodiment of the present invention. This embodiment can be applied to determine the target lining thickness of a target converter after the current smelting is completed by using infrared images and the smelting parameters of the current heat. This method can be executed by a converter lining thickness determination device, which can be implemented in the form of hardware and / or software, and can be configured in electronic devices such as mobile phones, computers or servers. Figure 1 As shown, the method includes:

[0039] S110. After the current heat smelting is completed, the smelting parameters and infrared image of the target converter are obtained, wherein the infrared image is an image of a local area of ​​the target converter.

[0040] Among them, the converter generally refers to a cylindrical oxygen-blowing steelmaking container that can be tilted. The converter lining is usually made of magnesia-carbon bricks. During the converter smelting process, the converter lining will be impacted or eroded, resulting in the lining peeling off, which may cause the converter to burn through over a long period of time. To avoid the above situation, the thickness of the converter lining can be determined to determine whether the converter has a risk of burn-through based on the thickness of the converter lining. The converter for which the lining thickness currently needs to be determined can be used as the target converter.

[0041] The smelting parameters of the target converter can be understood as the smelting data of the target converter during the current smelting process. Optionally, the smelting parameters may include at least two of the furnace liquid temperature after the current smelting is completed, the oxygen blowing time during the current smelting, the oxygen flow rate during the current smelting, the total iron content (TFe) of the slag after the current smelting, the content of magnesium oxide (MgO) in the slag after the current smelting, and the lining life parameters of the target converter. Among them, the lining life parameters can be determined by the following function.

[0042]

[0043] in, Represents the target converter lining life parameter, N i Indicates the number of heats that have been smelted in the target converter, N n Indicates the furnace number corresponding to the last lining replacement / repair of the target converter, N p It indicates the converter lining replacement cycle, that is, the interval between the heats at which the lining of the target converter is replaced.

[0044] The infrared image of the target converter is an infrared image of a local area in the target converter. Optionally, the local area may be an area in the target converter where the lining is relatively thin. The local area may be determined by edge detection of the complete infrared image corresponding to the target converter.

[0045] Specifically, after the current heat of the target converter is completed, a complete infrared image of the target converter is obtained based on an infrared thermal imaging device, or an infrared video frame corresponding to the moment when the target converter is completed is obtained through an infrared thermal imaging device. Grayscale processing is performed on the complete infrared image or infrared video frame to perform edge detection on the grayscale image to determine a local area of ​​the target converter. The infrared image corresponding to the local area is determined based on the complete infrared image or infrared video frame. In addition, the smelting parameters in the current heat smelting process are obtained from the corresponding database to determine the amount of lining spalling of the target converter in the current heat smelting process through the smelting parameters and the infrared image.

[0046] In an embodiment of the present invention, a method for obtaining an infrared image of a target converter may be: obtaining an original infrared image of the target converter, and performing grayscale processing on the original infrared image to determine an original grayscale image corresponding to the original infrared image; performing Gaussian filtering on the original grayscale image to obtain a denoised grayscale image, and processing the denoised grayscale image based on a preset gradient operator to determine at least one edge pixel point to be selected in the denoised grayscale image; based on a pixel value corresponding to at least one edge pixel point to be selected and a preset pixel range, determining at least one target edge pixel point and a local area corresponding to at least one target edge pixel point; and determining an infrared image of the target converter based on the original infrared image and the local area.

[0047] The original infrared image can be understood as a complete infrared image corresponding to the target converter. Optionally, the original infrared image can be an infrared image of the target converter taken by an infrared thermal imager deployed in front of the target converter, or an infrared video frame corresponding to the moment when the current heat of the target converter is completed. For example, the original infrared image can be as follows: Figure 2 The original grayscale image can be an image obtained by grayscale processing the original infrared image. For example, the original grayscale image can be as follows Figure 3 As shown. Gaussian filtering processing can be based on Gaussian high-frequency filtering to perform weighted averaging processing on each pixel in the original grayscale image to achieve the purpose of denoising. The denoised grayscale image can be understood as the image obtained after denoising the original grayscale image. The preset gradient operator can be used to determine the direction and amplitude of the pixel intensity change in the denoised grayscale image to determine the edge pixel points to be selected. The edge pixel points to be selected can be understood as pixels that may be the edge of the area in the denoised grayscale image. Optionally, the preset gradient operator can be a Sobel gradient operator or a Canny operator. The preset pixel range can be a pre-set standard range of pixel values ​​of pixel points in the local area. The target edge pixel point can be an edge pixel point to be selected whose pixel value meets the preset pixel range. The local area can be a lining area corresponding to at least one target edge pixel point. The local area is usually an area where the target converter lining is relatively thin.

[0048] Specifically, the target converter is imaged based on an infrared thermal imager to obtain an original infrared image of the target converter. The original infrared image is gray-scaled by a gray-scale conversion function to obtain an original gray-scale image. Optionally, the gray-scale conversion function can be expressed as follows:

[0049] Gray(i,j)=0.299×R(i,j)+0.587×G(i,j)+0.114×B(i,j)

[0050] Among them, Gray(i,j) represents the gray value of the pixel with pixel coordinates (i,j), R(i,j) represents the red channel value of the pixel with pixel coordinates (i,j) in the original infrared image, G(i,j) represents the green channel value of the pixel with pixel coordinates (i,j) in the original infrared image, and B(i,j) represents the blue channel value of the pixel with pixel coordinates (i,j) in the original infrared image. The original gray image is subjected to Gaussian filtering to remove noise in the original gray image to obtain a denoised gray image. The denoised gray image is processed according to a preset gradient operator, that is, the gray value of each pixel in the denoised gray image is compared with the gray values ​​of its surrounding adjacent pixels. When the gray value of the pixel is greater than the gray values ​​of all surrounding pixels, the pixel is determined to be an edge pixel to be selected. Based on this, at least one edge pixel to be selected is obtained. According to the pixel value of at least one edge pixel to be selected and the preset pixel range, the edge pixel to be selected whose pixel value belongs to the preset pixel range is used as the target edge pixel to obtain at least one target edge pixel and a local area corresponding to the at least one target edge pixel. According to the original infrared image and the local area, an infrared image of the local area is obtained, that is, an infrared image of the target converter.

[0051] For example, an infrared thermal imager is used to obtain the original infrared image of the target converter, or an infrared thermal imager is used to obtain the video stream of the target converter after smelting is completed, and the video frame of the target converter at the time of smelting completion is used as the original infrared image. Figure 2 , the original infrared image can include 30700 pixels. The original infrared image is gray-scaled by gray-scale conversion function to obtain the original gray-scale image. Figure 3 Perform a two-dimensional discrete Fourier transform on each pixel in the original grayscale image to obtain a Fourier spectrum corresponding to the original grayscale image. The function of the two-dimensional discrete Fourier transform can be expressed as follows:

[0052]

[0053] Where f(x,y) represents the grayscale value of the pixel coordinate (x,y) in the original grayscale image, (u,v) represents the coordinates corresponding to the pixel coordinate (x,y) in the Fourier spectrum, M represents the number of horizontal pixels in the original grayscale image, N represents the number of vertical pixels in the original image, and -j is a calculated imaginary number representing the conversion direction.

[0054] The Fourier spectrum is a colored image, but it is different from a normal colored image in that the Fourier spectrum uses color changes to reflect the structural complexity, image details, and interference noise in the grayscale image. The closer the color is to yellow, the more concentrated the image information in the area is, and the more obvious the yellow area is, the more noise (i.e., invalid image data) there is. In order to improve the accuracy of subsequent processing, the Fourier spectrum corresponding to the original grayscale image can be processed by the Gaussian high-frequency filter corresponding to the OpenCV component in the AWT package of the Java programming language to remove noise and obtain the denoised Fourier spectrum. The denoised Fourier spectrum is converted into a grayscale image using a two-dimensional discrete Fourier inverse transform, i.e., a denoised grayscale image is obtained.

[0055] Taking the preset gradient operator as the Canny operator as an example, the denoised grayscale image is processed by the Canny operator, that is, the grayscale value of each pixel in the denoised grayscale image is compared with the grayscale values ​​of its adjacent pixels. When the grayscale value of the pixel is greater than the grayscale values ​​of all the surrounding adjacent pixels, the pixel is taken as the edge pixel to be selected. For example, see Figure 4 The central red area is the current pixel block in the denoised grayscale image, and the eight arrows surrounding it are the eight directions after the pixel gradient is discretized. Through traversal calculation, it is determined that point A is the maximum value of the pixel gradient. The gradient at point A is retained and a vertical line is made along the direction of point A with point A as the foot of perpendicular, i.e., the boundary.

[0056] By processing the denoised grayscale image through the above-mentioned preset gradient operator, the direction and amplitude of the pixel gradient change in the denoised grayscale image can be determined to obtain a gradient amplitude image. The gradient amplitude image is processed using a double threshold method to determine the local area. A specific method may be that after performing non-maximum suppression processing on the gradient amplitude image, the pixels corresponding to the gradient values ​​greater than the preset maximum gradient value, or the gradient values ​​greater than the preset maximum gradient value and less than the preset minimum gradient value in the gradient amplitude image are determined by preset maximum gradient values ​​and preset minimum gradient values, and these pixels are used as target edge pixels. The local area is determined by the target edge pixel points. It should be noted that there can be one or more local areas. Figure 2 The original infrared image is processed as above, and the following can be determined: Figure 5The corresponding local area in the red box. The infrared image of the target converter can be determined based on the local area and the original infrared image.

[0057] S120, determining a target lining temperature of a target converter according to the infrared image, and determining a predicted lining spalling amount corresponding to the target converter in the current furnace according to the target lining temperature and smelting parameters.

[0058] The target lining temperature can be understood as the average lining temperature corresponding to the local area. Optionally, the target lining temperature can be determined based on the temperature value of each pixel point in the local area and the number of pixels. The predicted lining spalling amount can be understood as the target converter lining spalling amount during the current furnace smelting process.

[0059] Specifically, determine the grayscale image corresponding to the infrared image of the local area, and determine the to-be-processed temperature value of each pixel point corresponding to the local area according to the grayscale image and the correspondence between the grayscale value and the temperature value. Determine the temperature mean value, i.e., the target lining temperature of the target converter, according to the to-be-processed temperature value of each pixel point and the number of pixels in the local area. Obtain the smelting parameters of the target converter in the current smelting process from the corresponding database. Determine the predicted lining spalling amount corresponding to the target converter in the current smelting process according to the smelting parameters and the target lining temperature.

[0060] In an embodiment of the present invention, a method for determining a target lining temperature may be: determining conversion parameters to be used based on the highest temperature value and the lowest temperature value in the original infrared image, and the maximum grayscale value and the minimum grayscale value in the original grayscale image; determining a grayscale image to be processed corresponding to the infrared image, and determining a temperature value to be processed for each pixel in the grayscale image to be processed, the maximum grayscale value, and the conversion parameters to be used; determining a target lining temperature of a target converter based on the number of pixels in a local area and the temperature value to be processed for each pixel.

[0061] Among them, the highest temperature value can be the temperature value corresponding to the pixel with the highest radiation intensity in the area corresponding to the target converter in the original infrared image. Correspondingly, the lowest temperature value can be the temperature value corresponding to the pixel with the lowest radiation intensity in the area corresponding to the target converter in the original infrared image. The maximum grayscale value can be understood as the maximum grayscale value in the original grayscale image. The minimum grayscale value can be the minimum grayscale value in the original grayscale image. The conversion parameters to be used can be determined based on the highest temperature value, the lowest temperature value, the maximum grayscale value and the minimum grayscale value. The conversion of grayscale value and temperature value can be achieved through the conversion parameters to be used. Optionally, the conversion parameters to be used can be determined by the following function.

[0062]

[0063] Among them, T max is the maximum temperature value, T min is the minimum temperature value, Y max is the maximum gray value, Y min is the minimum grayscale value, and k represents the conversion parameter to be used.

[0064] The grayscale image to be processed can be obtained after grayscale processing of the infrared image, or can be determined when the local area is determined based on the original grayscale image. The grayscale value of each pixel in the grayscale image to be processed is used as the grayscale value to be processed. The temperature value to be processed can be understood as the temperature value obtained after the grayscale value to be processed is converted. The target lining temperature can be a temperature mean value determined according to the number of pixels corresponding to the local area and the temperature value to be processed of each pixel. Optionally, the determination function of the temperature value to be processed can be as follows.

[0065] T (i，j) =(Y (i,j) -Y max )×k+Y max

[0066] Among them, T (i，j) Indicates the temperature value to be processed at the pixel coordinate (i, j), Y (i,j) Indicates the grayscale value of the pixel with pixel coordinates (i, j) to be processed, Y max is the maximum grayscale value, and k represents the conversion parameter to be used.

[0067] Specifically, the original infrared image is analyzed to determine the highest temperature value and the lowest temperature value corresponding to the area where the target converter is located in the original infrared image. The temperature difference between the highest temperature value and the lowest temperature value is determined. According to the maximum grayscale value and the minimum grayscale value in the original grayscale image, the grayscale difference between the maximum grayscale value and the minimum grayscale value is determined. The ratio of the temperature difference to the grayscale difference is used as the conversion parameter to be used. The grayscale image to be processed corresponding to the infrared image is determined, and according to the conversion parameter to be used and the maximum grayscale value, the grayscale value to be processed corresponding to each pixel in the grayscale image to be processed is converted and processed to determine the temperature value to be processed corresponding to each pixel. According to the number of pixels in the local area of ​​the grayscale image to be processed and the temperature value to be processed of each pixel, the temperature mean is determined, and the temperature mean is used as the target lining temperature of the target converter.

[0068] S130, determining a target lining thickness corresponding to a target converter according to the initial lining thickness information and the predicted lining spalling amount.

[0069] The initial lining thickness information is the lining thickness parameter of the target converter determined before the current heat smelting. The lining thickness parameter can be understood as the thickness of the target converter before the current heat smelting. The target lining thickness can be understood as the thickness of the lining of the target converter after the current heat smelting is completed. The target lining thickness can be used to determine whether the target converter has a risk of burn-through.

[0070] Specifically, the lining thickness parameter of the target converter before the current smelting, that is, the initial lining thickness information, is obtained. The initial lining thickness information is subtracted from the predicted lining spalling amount to obtain the target lining thickness corresponding to the target converter.

[0071] In an embodiment of the present invention, the target lining thickness can be determined by performing a difference processing on the initial lining thickness information and the predicted lining spalling amount to obtain the target lining thickness corresponding to the target converter, so that when the target lining thickness does not reach a preset lining thickness threshold, based on the target lining thickness, an early warning prompt information is generated and an early warning process is performed.

[0072] The preset lining thickness threshold may be a pre-set standard value of the target converter lining thickness. The early warning prompt information may be information for prompting the corresponding staff that the target lining thickness of the target converter does not reach the preset lining thickness threshold and the target converter lining needs to be replaced or repaired.

[0073] Specifically, the initial lining thickness information of the target converter before the current smelting is obtained. The difference between the initial lining thickness information and the predicted lining spalling amount is determined, and the difference is used as the target lining thickness. When the target lining thickness does not reach the preset lining thickness threshold, it means that the lining of the target converter is at risk of being burned through, and an early warning prompt information can be generated according to the target lining thickness, and an early warning process can be performed.

[0074] Exemplarily, the preset lining thickness threshold can be 60% of the original lining thickness of the target converter, and the original lining thickness can be understood as the thickness of a brand new lining that has not been smelted. After determining the target lining thickness based on the difference between the initial lining thickness information and the predicted lining spalling amount, determine whether the target lining thickness exceeds 60% of the original lining thickness. If so, no early warning processing is required. If not, an early warning prompt message is generated based on the target lining thickness, and an early warning processing is performed so that the corresponding staff can repair or replace the lining of the target converter in a timely manner to avoid burn-through leakage.

[0075] The technical solution of this embodiment provides data support for the subsequent determination of the lining thickness of the target converter by obtaining the smelting parameters and infrared images of the target converter after the current heat smelting is completed. According to the infrared image, the target lining temperature of the target converter is determined. And according to the target lining temperature and smelting parameters, the predicted lining peeling amount of the target converter under the current heat is determined, so as to determine the lining thickness of the target converter after the current heat smelting is completed according to the predicted lining peeling amount. According to the lining thickness parameters determined before the current heat smelting of the target converter and the predicted lining peeling amount, the target lining thickness of the target converter is determined. The present invention solves the problem of incomplete measurement caused by laser measurement of lining thickness and the problem of low efficiency and accuracy caused by manual measurement of lining thickness in the prior art, and quickly and accurately determines the target lining thickness of the target converter through infrared images and smelting parameters, which not only improves the efficiency and accuracy of determining the lining thickness, but also effectively avoids the situation where the lining is burned through, ensuring the safety of on-site workers.

[0076] Embodiment 2

[0077] Figure 6 This is a flow chart of a method for determining the thickness of a converter lining provided in the second embodiment of the present invention. This embodiment is based on the above embodiment and refines the step of "determining the predicted lining spalling amount corresponding to the target converter under the current furnace according to the target lining temperature and smelting parameters". The specific implementation method can be found in the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here. Figure 6 As shown, the method includes:

[0078] S210. After the current heat smelting is completed, the smelting parameters and infrared image of the target converter are obtained, wherein the infrared image is an image of a local area of ​​the target converter.

[0079] S220, determining a target lining temperature of a target converter according to the infrared image, and processing the smelting parameters based on a plurality of common factors to determine a function to determine a plurality of parameters to be input corresponding to the smelting parameters.

[0080] The common factor determination function may be a function determined based on factor analysis and historical smelting parameters and used to characterize the correlation between smelting parameters. The input parameters may be common factor parameters obtained by substituting the smelting parameters into the common factor determination function.

[0081] Specifically, the grayscale image corresponding to the infrared image of the local area is determined, and the temperature value to be processed for each pixel corresponding to the local area is determined according to the grayscale image and the conversion parameters to be used between the grayscale value and the temperature value. The temperature mean, i.e., the target lining temperature of the target converter, is determined according to the temperature value to be processed for each pixel and the number of pixels in the local area. The smelting parameters are processed respectively by multiple common factor determination functions, and multiple parameters to be input corresponding to the smelting parameters are determined, so as to determine the predicted lining spalling amount based on the parameters to be input and the target lining thickness.

[0082] In an embodiment of the present invention, before processing the smelting parameters based on multiple common factor determination functions, a common factor determination function may be obtained first. The common factor determination function may be determined by: obtaining the historical smelting parameters of the target converter in at least two dimensions under historical furnaces, wherein the at least two dimensions include at least two of the furnace liquid temperature dimension, the smelting time dimension after filling with a preset gas, the flow dimension of the preset gas, the ratio dimension of the preset component in the slag, and the use time dimension of the target converter; performing data dimensionality reduction processing on the historical smelting parameters in at least two dimensions based on the factor analysis method, so as to obtain multiple common factor determination functions when the cumulative contribution rate corresponding to the factor analysis method meets the preset conditions.

[0083] Among them, the historical heat can be understood as the historical heat of the current heat of the target converter. The historical smelting parameters are the smelting parameters in the smelting process of the historical heat. The historical smelting parameters may include smelting parameters in at least two dimensions. The at least two dimensions include at least two of the furnace liquid temperature dimension, the smelting time dimension after filling with a preset gas, the flow dimension of the preset gas, the ratio dimension of the preset component in the slag, and the use time dimension of the target converter.

[0084] The historical smelting parameters under the dimension of furnace liquid temperature may be the furnace liquid temperature after the completion of the historical furnace smelting. The preset gas may be oxygen. The historical smelting parameters under the dimension of smelting time after filling the preset gas may be the oxygen blowing time during the historical furnace smelting process. The historical smelting parameters under the dimension of the flow rate of the preset gas may be understood as the oxygen flow rate during the historical furnace smelting process. The historical smelting parameters under the dimension of the preset component ratio in the slag may be the total iron content, magnesium oxide content and slag basicity in the slag after the completion of the historical furnace smelting. The historical smelting parameters under the dimension of the service life of the target converter may be the lining life parameters of the target converter corresponding to the historical furnace. The lining life parameters can be used to characterize the length of time that the target converter can be used after the current furnace.

[0085] The factor analysis method can reduce the historical smelting parameters under at least two dimensions to a few common factor parameters to characterize the correlation between the historical smelting parameters under at least two dimensions. The cumulative contribution rate can be used to characterize the representativeness of the common factor parameters extracted based on the factor analysis method to the historical smelting parameters under at least two dimensions. It can be understood that the higher the cumulative contribution rate, the stronger the representativeness of the corresponding common factor parameters. The preset condition can be a pre-set numerical condition that the cumulative contribution rate needs to reach. Optionally, the preset condition can be a condition that the cumulative contribution rate is greater than 90%.

[0086] Specifically, the historical smelting parameters of the target converter under multiple historical furnaces of the current furnace are obtained. Among them, the historical smelting parameters include smelting parameters under at least two dimensions of furnace liquid temperature dimension, smelting time dimension after filling with preset gas, flow dimension of preset gas, preset component ratio dimension in slag, and use time dimension of the target converter. According to the factor analysis method, data dimensionality reduction processing is performed on the historical smelting parameters under the above at least two dimensions. In order to prevent excessive feature loss, when the cumulative contribution rate corresponding to the factor analysis method meets the preset conditions, multiple common factor determination functions can be obtained, so as to process the smelting parameters of the current furnace of the target converter through multiple common factor determination functions, and obtain the common factor parameters corresponding to the smelting parameters, that is, the parameters to be input.

[0087] Exemplarily, the historical smelting parameters under the dimension of furnace liquid temperature are the endpoint temperature, the historical smelting parameters under the dimension of smelting time after filling the preset gas are the blowing time, the historical smelting parameters under the dimension of preset gas flow rate are the oxygen flow rate, the historical smelting parameters under the dimension of preset component ratio in the slag are TFe in the slag, MgO in the slag and slag basicity, and the historical smelting parameters under the dimension of the service life of the target converter are the lining life parameters as examples for explanation.

[0088] The original smelting parameters of the 269th to 1169th heats were obtained. The original smelting parameters were preprocessed to obtain the historical smelting parameters of 500 heats. The historical smelting parameters include the endpoint temperature, blowing time, oxygen flow rate, TFe in slag, MgO in slag, slag basicity and furnace lining life parameters corresponding to each historical heat. Table 1 below is an information table of historical smelting parameters.

[0089] Table 1 Information table of historical smelting parameters

[0090]

[0091] The factor analysis method is used to standardize the 7 historical smelting parameters of the above 500 heats, and determine the correlation coefficient matrix corresponding to the historical smelting parameters. The correlation coefficient matrix is ​​processed to determine the common factor variables corresponding to the correlation coefficient matrix and the coefficients corresponding to the common factor variables. When the cumulative contribution rate is greater than 90%, 3 common factor determination functions are obtained. For example, the common factor determination function can be shown in Table 2 below.

[0092]

[0093]

[0094] Based on this, the smelting parameters of the current heat of the target converter can be substituted into the above common factor determination function to obtain the common factor parameters F1, F2 and F3 corresponding to the smelting parameters of the current heat, that is, the parameters to be input.

[0095] S230, inputting the parameters to be input and the target lining temperature into a pre-trained lining spalling amount prediction model to determine the predicted lining spalling amount corresponding to the target converter in the current heat.

[0096] The lining spalling amount prediction model may be a pre-trained model used to predict the lining spalling amount of a target converter during the current smelting process.

[0097] Specifically, the input target lining temperature and the parameters to be input are processed based on the pre-trained lining spalling amount prediction model to obtain the predicted lining spalling amount corresponding to the target converter in the current furnace, so as to determine the target lining thickness of the target converter after the current lining smelting is completed based on the predicted lining spalling amount.

[0098] In an embodiment of the present invention, before processing the input parameters and target lining temperature based on the pre-trained lining spalling prediction model, a lining spalling prediction model can be constructed and trained first. The lining spalling prediction model is determined based on a particle swarm optimization algorithm and a back propagation algorithm, and the initial model parameters of the lining spalling prediction model are obtained by updating and iterating the original model parameters based on the particle swarm optimization algorithm. The training method of the lining spalling prediction model can be: obtaining multiple training samples, wherein the training samples include sample lining temperature, sample input parameters, and theoretical lining spalling corresponding to the sample lining temperature and sample input parameters; inputting the sample lining temperature and sample input parameters in the training samples into the lining spalling prediction model to be trained to obtain the actual lining spalling; determining the loss value based on the theoretical lining spalling and the actual lining spalling; and modifying the initial model parameters of the lining spalling prediction model to be trained based on the loss value to obtain a trained lining spalling prediction model.

[0099] Among them, the particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. In the particle swarm optimization algorithm, each potential solution is abstracted into a "particle", which shuttles in the high-dimensional solution space and finds the optimal solution through information sharing and collaborative cooperation. In an embodiment of the present invention, the particle swarm optimization algorithm is used to adjust and optimize the original model parameters of the lining spalling prediction model. The back propagation algorithm can be used to adjust the weights and bias values ​​of the lining spalling prediction model to be trained so that the output of the lining spalling prediction model to be trained is closer to the theoretical lining spalling. The lining spalling prediction model to be trained can be a neural network model determined based on the particle swarm optimization algorithm and the back propagation algorithm. The original model parameters can be understood as the default parameters of the lining spalling prediction model to be trained. The initial model parameters can be the model parameters obtained after optimizing and adjusting the original model parameters based on the particle swarm optimization algorithm.

[0100] The sample lining temperature may be the average lining temperature corresponding to the local area in the historical furnace. The sample input parameter may be a common factor parameter determined according to the historical smelting parameters of the historical furnace. The theoretical lining spalling amount may be determined according to the lining thickness information before the historical furnace smelting and the lining thickness information after the smelting. The actual lining spalling amount may be the lining spalling amount predicted by the lining spalling amount prediction model to be trained. The loss value may be understood as the difference between the actual lining spalling amount and the theoretical lining spalling amount.

[0101] Specifically, before training the lining spalling amount prediction model to be trained based on the training samples, multiple training samples can be obtained first to train the model based on the training samples. In order to provide the accuracy of the lining spalling amount prediction model to be trained, as many and rich training samples as possible can be obtained. Sample lining temperatures and sample input parameters corresponding to multiple historical furnaces are obtained. And the theoretical lining spalling amount corresponding to the historical furnaces is determined. The sample lining temperature and sample input parameters in the training samples are input into the lining spalling amount prediction model to be trained to obtain the actual lining spalling amount. The loss value is determined according to the actual lining spalling amount and the theoretical lining spalling amount, so as to use the loss value to correct the model parameters of the lining spalling amount prediction model to be trained. When using the loss value to correct the model parameters in the lining spalling amount prediction model to be trained, the convergence of the loss function can be used as a training target, such as whether the training error is less than the preset error, or whether the error change tends to be stable, or whether the current number of iterations is equal to the preset number. If the convergence condition is reached, such as the training error of the loss function is less than the preset error, or the error change trend tends to be stable, it indicates that the training of the lining spalling amount prediction model to be trained is completed, and the iterative training can be stopped at this time. If it is detected that the convergence condition is not reached at present, other training samples can be further obtained to continue training the lining spalling amount prediction model to be trained until the training error of the loss function is within the preset range. When the training error of the loss function reaches convergence, the trained lining spalling amount prediction model is obtained.

[0102] Exemplarily, a PSO-BP model is determined based on a particle swarm optimization algorithm (PSO) and a backpropagation algorithm (BP), and the PSO-BP model is used as a lining spalling prediction model to be trained. Forward iteration is performed through the particle swarm optimization algorithm, and backpropagation training is performed through the backpropagation algorithm to obtain a trained lining spalling prediction model. Among them, in the trained lining spalling prediction model, the BP neural network includes a three-layer structure of an input layer, a hidden layer, and an output layer. The number of hidden layer nodes is 9, the activation function is a Sigmod activation function, and the PSO optimization parameters are selected as a particle swarm parameter of 50, a learning factor of 1.5, and an inertia weight of 0.8.

[0103] In conjunction with the above examples, see Figure 7, the PSO-BP model is used as the lining spalling prediction model to be trained for explanation. The PSO-BP model can be determined based on MATLAB programming. And 490 groups of training samples and 10 groups of test samples are set. Before training the PSO-BP model, the PSO-BP model can be determined based on the particle swarm optimization algorithm and the back propagation algorithm. And the original model parameters are optimized based on the PSO algorithm to obtain the initial model parameters. That is, the optimization target of the PSO algorithm is the hyperparameter of the BP neural network, and the input is also the training sample.

[0104] The specific method may be to set the particle swarm parameters and initialize the particle position information and speed information. Calculate the fitness of each particle, that is, the value of the distance between the position information of each particle and the target. Determine the individual optimal fitness of each particle and the population optimal fitness of the entire particle swarm. According to the individual optimal fitness and the population optimal fitness, each particle updates the individual speed information and position information, calculates the fitness corresponding to each particle again, and updates the individual optimal fitness and the population optimal fitness. Repeat the above process until the preset error condition is met, and output the initial model parameters.

[0105] The PSO-BP model based on the initial model parameters processes the training samples and determines the loss value to update the initial model parameters based on the loss value. If the detection reaches the convergence condition, that is, when the loss function converges, the trained lining spalling prediction model is obtained.

[0106] For example, in the process of training the lining spalling prediction model, the prediction error is as follows: Figure 8 The prediction error can be understood as the difference between the actual lining spalling amount and the theoretical lining spalling amount. The spalling thickness corresponding to the actual lining spalling amount and the theoretical lining spalling amount is shown as Fig. 9 shown. Fig. 9 In the above example, the actual value is the spalling thickness corresponding to the theoretical predicted spalling amount. The predicted value is the spalling thickness corresponding to the actual spalling amount of the lining. The prediction error range is concentrated in [-0.6, 0.27]. If the theoretical error is set to ±0.3mm, the prediction accuracy of the lining spalling prediction model to be trained is 88%.

[0107] S240: Determine a target lining thickness corresponding to a target converter according to the initial lining thickness information and the predicted lining spalling amount.

[0108] The initial lining thickness information is the lining thickness parameter determined before the current furnace smelting of the target converter.

[0109] The technical solution of this embodiment provides data support for the subsequent determination of the lining thickness of the target converter by obtaining the smelting parameters and infrared images of the target converter after the current smelting is completed. According to the infrared image, the target lining temperature of the target converter is determined. The smelting parameters are processed based on multiple common factors to determine the function, and multiple parameters to be input corresponding to the smelting parameters are determined. The parameters to be input and the target lining temperature are input into the pre-trained lining spalling amount prediction model to determine the predicted lining spalling amount corresponding to the target converter in the current furnace. The lining thickness of the target converter after the current smelting is completed is determined based on the predicted lining spalling amount. The target lining thickness of the target converter is determined based on the lining thickness parameters determined before the current smelting of the target converter and the predicted lining spalling amount. The present invention solves the problems of incomplete measurement caused by measuring the lining thickness by laser and low efficiency and accuracy caused by measuring the lining thickness manually in the prior art. The infrared image and the parameters to be input are processed by a lining spalling amount prediction model, thereby realizing non-contact continuous prediction of the target converter lining thickness. This not only improves the efficiency and accuracy of determining the lining thickness, but also effectively avoids the target converter lining being burned through, thereby ensuring the safety of on-site workers.

[0110] Embodiment 3

[0111] Fig.10 Schematic diagram of the structure of a device for determining the thickness of a converter lining provided by the third embodiment of the present invention. Fig.10 As shown, the device includes: a data acquisition module 310, a spalling amount determination module 320 and a furnace lining thickness determination module 330.

[0112] The data acquisition module 310 is used to obtain the smelting parameters and infrared image of the target converter after the current smelting is completed, wherein the infrared image is an image of a local area of ​​the target converter; the spalling amount determination module 320 is used to determine the target lining temperature of the target converter based on the infrared image, and determine the predicted lining spalling amount corresponding to the target converter in the current heat based on the target lining temperature and the smelting parameters; the lining thickness determination module 330 is used to determine the target lining thickness corresponding to the target converter based on the initial lining thickness information and the predicted lining spalling amount; wherein the initial lining thickness information is the lining thickness parameter determined for the target converter before the current smelting.

[0113] The technical solution of this embodiment provides data support for the subsequent determination of the lining thickness of the target converter by obtaining the smelting parameters and infrared images of the target converter after the current heat smelting is completed. According to the infrared image, the target lining temperature of the target converter is determined. And according to the target lining temperature and smelting parameters, the predicted lining peeling amount of the target converter under the current heat is determined, so as to determine the lining thickness of the target converter after the current heat smelting is completed according to the predicted lining peeling amount. According to the lining thickness parameters determined before the current heat smelting of the target converter and the predicted lining peeling amount, the target lining thickness of the target converter is determined. The present invention solves the problem of incomplete measurement caused by laser measurement of lining thickness and the problem of low efficiency and accuracy caused by manual measurement of lining thickness in the prior art, and quickly and accurately determines the target lining thickness of the target converter through infrared images and smelting parameters, which not only improves the efficiency and accuracy of determining the lining thickness, but also effectively avoids the situation where the lining is burned through, ensuring the safety of on-site workers.

[0114] On the basis of the above embodiment, optionally, the data acquisition module includes an image acquisition unit, which is used to acquire the original infrared image of the target converter, and grayscale process the original infrared image to determine the original grayscale image corresponding to the original infrared image; Gaussian filter the original grayscale image to obtain a denoised grayscale image, and process the denoised grayscale image based on a preset gradient operator to determine at least one edge pixel to be selected in the denoised grayscale image; based on the pixel value corresponding to the at least one edge pixel to be selected and the preset pixel range, determine at least one target edge pixel and a local area corresponding to the at least one target edge pixel; based on the original infrared image and the local area, determine the infrared image of the target converter.

[0115] Optionally, the spalling amount determination module includes a lining temperature determination unit, which is used to determine the conversion parameters to be used based on the highest temperature value and the lowest temperature value in the original infrared image, and the maximum grayscale value and the minimum grayscale value in the original grayscale image; determine the grayscale image to be processed corresponding to the infrared image, and determine the temperature value to be processed for each pixel in the grayscale image to be processed, the maximum grayscale value and the conversion parameters to be used; determine the target lining temperature of the target converter based on the number of pixels in the local area and the temperature value to be processed for each pixel.

[0116] Optionally, the spalling amount determination module includes a predicted lining spalling amount determination unit, which includes: a subunit for determining parameters to be input, used to process the smelting parameters based on multiple common factors to determine the function, and determine multiple parameters to be input corresponding to the smelting parameters; a model processing subunit, used to input the parameters to be input and the target lining temperature into a pre-trained lining spalling amount prediction model, and determine the predicted lining spalling amount corresponding to the target converter in the current furnace.

[0117] Optionally, the unit for determining the predicted lining spalling amount also includes: a common factor determination function determination subunit, which is used to obtain historical smelting parameters of the target converter in at least two dimensions under historical furnaces, wherein the at least two dimensions include a furnace liquid temperature dimension, a smelting time dimension after filling with a preset gas, a flow dimension of a preset gas, a dimension of a preset component ratio in the slag, and at least two of the target converter's usage time dimension; based on the factor analysis method, data dimensionality reduction processing is performed on the historical smelting parameters in at least two dimensions to obtain multiple common factor determination functions when the cumulative contribution rate corresponding to the factor analysis method meets the preset conditions.

[0118] Optionally, the lining spalling amount prediction model is determined based on a particle swarm optimization algorithm and a back propagation algorithm, and the initial model parameters of the lining spalling amount prediction model are obtained by iteratively updating the original model parameters based on the particle swarm optimization algorithm. The predicted lining spalling amount determination unit also includes: a model training subunit, used to obtain multiple training samples, wherein the training samples include sample lining temperature, sample input parameters, and theoretical lining spalling amount corresponding to the sample lining temperature and the sample input parameters; the sample lining temperature and the sample input parameters in the training samples are input into the lining spalling amount prediction model to be trained to obtain the actual lining spalling amount; based on the theoretical lining spalling amount and the actual lining spalling amount, a loss value is determined; based on the loss value, the initial model parameters of the lining spalling amount prediction model to be trained are corrected to obtain a trained lining spalling amount prediction model.

[0119] Optionally, a lining thickness determination module is used to perform a difference processing between the initial lining thickness information and the predicted lining spalling amount to obtain a target lining thickness corresponding to the target converter, so as to generate a warning prompt message and perform a warning process based on the target lining thickness when the target lining thickness does not reach a preset lining thickness threshold.

[0120] The converter lining thickness determination device provided in the embodiment of the present invention can execute the converter lining thickness determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0121] Embodiment 4

[0122] Fig.111 is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0123] like Fig.11 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0125] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining the thickness of a converter lining.

[0126] In some embodiments, the method for determining the thickness of the converter lining may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the thickness of the converter lining described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the thickness of the converter lining by any other appropriate means (e.g., by means of firmware).

[0127] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] The computer program for implementing the converter lining thickness determination method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0129] Embodiment 5

[0130] Embodiment 5 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute a method for determining the thickness of a converter lining, the method comprising:

[0131] After the current heat smelting is completed, the smelting parameters and infrared image of the target converter are obtained, wherein the infrared image is an image of a local area of ​​the target converter; the target lining temperature of the target converter is determined according to the infrared image, and the predicted lining spalling amount corresponding to the target converter in the current heat is determined according to the target lining temperature and the smelting parameters; the target lining thickness corresponding to the target converter is determined according to the initial lining thickness information and the predicted lining spalling amount; wherein the initial lining thickness information is the lining thickness parameter determined for the target converter before the current heat smelting.

[0132] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0135] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining the thickness of a converter lining, characterized in that: include: After the current heat smelting is completed, the smelting parameters and infrared image of the target converter are obtained, wherein the infrared image is an image of a local area of ​​the target converter; Determine a target lining temperature of the target converter according to the infrared image, and determine a predicted lining spalling amount corresponding to the target converter in the current heat according to the target lining temperature and the smelting parameters; Determining a target lining thickness corresponding to the target converter according to the initial lining thickness information and the predicted lining spalling amount; The initial lining thickness information is the lining thickness parameter determined before the current smelting of the target converter.

2. The method according to claim 1, characterized in that Acquire infrared images of the target converter, including: Acquire an original infrared image of the target converter, and perform grayscale processing on the original infrared image to determine an original grayscale image corresponding to the original infrared image; Performing Gaussian filtering on the original grayscale image to obtain a denoised grayscale image, and processing the denoised grayscale image based on a preset gradient operator to determine at least one edge pixel to be selected in the denoised grayscale image; Determine at least one target edge pixel point and a local area corresponding to the at least one target edge pixel point based on a pixel value corresponding to the at least one edge pixel point to be selected and a preset pixel range; Based on the original infrared image and the local area, an infrared image of the target converter is determined.

3. The method according to claim 1, characterized in that Determining a target lining temperature of the target converter according to the infrared image includes: Determine the conversion parameters to be used based on the highest temperature value and the lowest temperature value in the original infrared image, and the maximum grayscale value and the minimum grayscale value in the original grayscale image; Determine a grayscale image to be processed corresponding to the infrared image, and determine a temperature value to be processed for each pixel in the grayscale image to be processed according to the grayscale value to be processed of each pixel, the maximum grayscale value, and the conversion parameter to be used; Based on the number of pixels in the local area and the temperature value to be processed of each pixel point, a target lining temperature of the target converter is determined.

4. The method according to claim 1, characterized in that The step of determining the predicted lining spalling amount corresponding to the target converter in the current furnace according to the target lining temperature and the smelting parameters includes: Determine a function based on multiple common factors to process the smelting parameters and determine multiple parameters to be input corresponding to the smelting parameters; The parameters to be input and the target lining temperature are input into a pre-trained lining spalling amount prediction model to determine the predicted lining spalling amount corresponding to the target converter in the current heat.

5. The method according to claim 4, characterized in that Before processing the smelting parameters based on the multiple common factor determination function, the method further includes: Obtaining historical smelting parameters of the target converter in at least two dimensions under historical furnaces, wherein the at least two dimensions include at least two of a furnace liquid temperature dimension, a smelting time dimension after filling with a preset gas, a flow dimension of the preset gas, a preset component ratio dimension in the slag, and a use time dimension of the target converter; Based on the factor analysis method, data dimension reduction processing is performed on the historical smelting parameters in the at least two dimensions, so as to obtain multiple common factor determination functions when the cumulative contribution rate corresponding to the factor analysis method meets the preset conditions.

6. The method according to claim 4, characterized in that The lining spalling amount prediction model is determined based on a particle swarm optimization algorithm and a back propagation algorithm, and the initial model parameters of the lining spalling amount prediction model are obtained by updating and iterating the original model parameters based on the particle swarm optimization algorithm. The method further includes: Acquire a plurality of training samples, wherein the training samples include a sample lining temperature, a sample input parameter, and a theoretical lining spalling amount corresponding to the sample lining temperature and the sample input parameter; Inputting the sample lining temperature and sample input parameters in the training sample into the lining spalling amount prediction model to be trained to obtain the actual lining spalling amount; Determining a loss value based on the theoretical lining spalling amount and the actual lining spalling amount; The initial model parameters of the furnace lining spalling amount prediction model to be trained are corrected based on the loss value to obtain a trained furnace lining spalling amount prediction model.

7. The method according to claim 1, characterized in that Determining the target lining thickness corresponding to the target converter according to the initial lining thickness information and the predicted lining spalling amount includes: The initial lining thickness information and the predicted lining spalling amount are subtracted to obtain a target lining thickness corresponding to the target converter, so that when the target lining thickness does not reach a preset lining thickness threshold, an early warning prompt information is generated and an early warning process is performed based on the target lining thickness.

8. A device for determining the thickness of a converter lining, characterized in that: include: A data acquisition module, used for acquiring smelting parameters and infrared images of a target converter after the current heat smelting is completed, wherein the infrared image is an image of a local area of ​​the target converter; a spalling amount determination module, for determining a target lining temperature of the target converter according to the infrared image, and determining a predicted lining spalling amount corresponding to the target converter in the current heat according to the target lining temperature and the smelting parameters; A lining thickness determination module, used to determine a target lining thickness corresponding to the target converter according to the initial lining thickness information and the predicted lining spalling amount; The initial lining thickness information is the lining thickness parameter determined before the current smelting of the target converter.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the thickness of a converter lining according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the thickness of a converter lining according to any one of claims 1 to 7 when the processor executes the instructions.

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