Blast furnace chute inclination angle identification method and related equipment

Abstract

The invention discloses a blast furnace chute inclination angle identification method and related equipment, relates to the technical field of smelting, and mainly aims to solve the problem that the existing blast furnace chute inclination angle identification depends on manpower too much. The method comprises the following steps: acquiring image data of a blast furnace top chute operation area; extracting chute bottom edge contour features of the image data; and a target inclination angle value is determined based on the chute bottom edge contour features, and the target inclination angle value is used for representing the chute angle. The method is used for the identification process of the blast furnace chute inclination angle.

Description

Technical Field

[0001] The present invention relates to the technical field of smelting, and in particular to a method for identifying the inclination angle of a blast furnace chute and related equipment. Background Art

[0002] The accuracy of the blast furnace chute angle directly affects the burden distribution system and the burden distribution process, and further affects the stability of the blast furnace. During the production process, the blast furnace top is in a high-temperature and multi-material flow environment, making it difficult to directly measure the inclination angle of the blast furnace chute online; during the operation of the chute, there are often deviations between the set angle and the actual angle. Currently, the inclination angle of the chute is mainly measured manually during long maintenance times, and there is still a lack of an online measurement method for accurately measuring and verifying the inclination angle of the chute. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method for identifying the inclination angle of a blast furnace chute and related equipment, mainly aiming to solve the problem that the identification of the inclination angle of the current blast furnace chute relies too much on manual work.

[0004] To solve the above-mentioned at least one technical problem, in a first aspect, the present invention provides a method for identifying the inclination angle of a blast furnace chute, the method comprising:

[0005] Obtaining image data of the operation area of the blast furnace top chute;

[0006] Extracting the edge contour features of the bottom of the chute from the image data;

[0007] Determining a target inclination angle value based on the edge contour features of the bottom of the chute, wherein the target inclination angle value is used to characterize the chute angle.

[0008] Optionally, the obtaining image data of the operation area of the blast furnace top chute includes:

[0009] Obtaining the original image of the operation area of the blast furnace top chute;

[0010] Performing noise suppression processing on the original image to determine the image data, wherein the noise suppression processing adopts a composite algorithm of adaptive Gaussian filtering and median filtering.

[0011] Optionally, the extracting the edge contour features of the bottom of the chute from the image data includes:

[0012] Performing edge detection on the image data to extract the edge contour features of the bottom of the chute, wherein the edge detection adopts the Canny operator.

[0013] Optionally, the determining a target inclination angle value based on the edge contour features of the bottom of the chute includes:

[0014] Identify the contour features of the bottom edge of the chute based on a geometric transformation algorithm to extract the straight line of the chute contour;

[0015] Determine the theoretical inclination value based on the angle between the chute contour straight line and the vertical direction.

[0016] Optionally, the determining the target inclination value based on the contour features of the bottom edge of the chute includes:

[0017] A frame error correction model to determine the target inclination value based on the theoretical inclination value:

[0018] β’ = β + θ

[0019] Where β’ is the target inclination value, β is the theoretical inclination value, and θ is the correction angle.

[0020] Optionally, the above method further includes:

[0021] Obtain the image data of the running area of the blast furnace top chute in real time to output the dynamic chute inclination change data.

[0022] Optionally, the above method further includes:

[0023] Construct a chute state evaluation model based on the inclination value, vibration frequency, and temperature gradient to evaluate the state of the blast furnace chute.

[0024] In a second aspect, an embodiment of the present invention further provides an identification device for the inclination of a blast furnace chute, including:

[0025] An acquisition unit for acquiring the image data of the running area of the blast furnace top chute;

[0026] An extraction unit for extracting the contour features of the bottom edge of the chute from the image data;

[0027] A determination unit for determining the target inclination value based on the contour features of the bottom edge of the chute, where the target inclination value is used to characterize the chute angle.

[0028] To achieve the above object, according to a third aspect of the present invention, there is provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the above program is executed by a processor, the steps of the above method for identifying the inclination of a blast furnace chute are implemented.

[0029] To achieve the above object, according to a fourth aspect of the present invention, there is provided an electronic device, including at least one processor and at least one memory connected to the processor; wherein, the above processor is used to call the program instructions in the above memory to execute the steps of the above method for identifying the inclination of a blast furnace chute.

[0030] With the above technical solutions, for the problem that the current identification of the blast furnace chute inclination relies too much on manual work, the identification method and related equipment for the blast furnace chute inclination provided by the present invention obtain image data of the operation area of the blast furnace top chute; extract the contour features of the bottom edge of the chute from the image data; determine the target inclination value based on the contour features of the bottom edge of the chute, where the target inclination value is used to represent the chute angle. In the above solution, by establishing a multi-dimensional perception calculation system, real-time dynamic monitoring of the chute inclination in a high-temperature and complex environment is achieved. This method breaks through the spatio-temporal limitations of traditional manual measurement, converts the physical space angle information into digital signals using image processing technology, and eliminates the influence of equipment installation deviation through an error compensation model, greatly improving the inclination measurement accuracy. Compared with the off-line detection method, the present invention can improve the blast furnace burden distribution uniformity coefficient and effectively extend the service life of the furnace lining.

[0031] Correspondingly, the identification device, equipment, and computer-readable storage medium for the blast furnace chute inclination provided by the embodiments of the present invention also have the above technical effects.

[0032] The above description is only an overview of the technical solutions of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features, and advantages of the present invention more obvious and understandable, the following specifically describes the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0034] Figure 1 Shows a schematic diagram of a blast furnace top imaging device provided by an embodiment of the present invention;

[0035] Figure 2 Shows a schematic diagram of a blast furnace top monitoring provided by an embodiment of the present invention;

[0036] Figure 3 Shows a top view of the rotation of a blast furnace chute provided by an embodiment of the present invention;

[0037] Figure 4 Shows a front view of the rotation of a blast furnace chute provided by an embodiment of the present invention;

[0038] Figure 5 Shows a schematic diagram of an original image for identifying the inclination of a blast furnace chute provided by an embodiment of the present invention;

[0039] Figure 6 Shows a schematic diagram of image data for identifying the inclination angle of a blast furnace chute provided by an embodiment of the present invention;

[0040] Figure 7 Shows a schematic flowchart of a method for identifying the inclination angle of a blast furnace chute provided by an embodiment of the present invention;

[0041] Figure 8 Shows a schematic block diagram of the composition of a device for identifying the inclination angle of a blast furnace chute provided by an embodiment of the present invention;

[0042] Figure 9 Shows a schematic block diagram of the composition of an electronic device for identifying the inclination angle of a blast furnace chute provided by an embodiment of the present invention. Detailed implementation manners

[0043] Hereinafter, exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0044] To solve the problem that the identification of the inclination angle of the current blast furnace chute relies too much on manual work, an embodiment of the present invention provides a method for identifying the inclination angle of a blast furnace chute, as Figure 7 shown, the method includes:

[0045] S101. Obtain image data of the operating area of the blast furnace top chute;

[0046] In one embodiment, the obtaining of the image data of the operating area of the blast furnace top chute includes:

[0047] Obtain the original image of the operating area of the blast furnace top chute;

[0048] Perform noise suppression processing on the original image to determine the image data, wherein the noise suppression processing uses an adaptive Gaussian filtering and median filtering composite algorithm.

[0049] Exemplarily, the present application obtains the original image of the blast furnace top video in real time, captures the image data when the axial center line of the chute is parallel to the camera lens, and uses it to determine the original image of the operating area of the blast furnace top chute. To improve the quality of the original image and remove the noise interference in the image, the Gaussian filtering method is used to perform filtering processing on the image.

[0050] Specifically, the target of adaptive Gaussian filtering is to smooth the overall noise of the image while preserving edge details. Dynamically adjust the convolution kernel size: adaptively select the Gaussian kernel size according to the local image gradient change (for example, reduce the kernel window in the edge area and expand the kernel window in the flat area); variance weighted calculation: assign different weights to different gray-scale areas to suppress low-frequency noise while protecting texture information. Compared with Gaussian filtering with fixed parameters, it can avoid over-blurring the edge of the chute contour. The target of median filtering is to eliminate salt-and-pepper noise (isolated black and white pixel points). By sorting the pixels within the sliding window: sort the gray-scale values of the pixels in the 3×3 or 5×5 neighborhood; replace the central pixel with the median value: effectively filter out impulse noise and retain the steepness of the edge. It has strong robustness to randomly distributed noise points and does not depend on the statistical characteristics of the noise.

[0051] Based on the above scheme, for the composite strategy cooperation mechanism, first perform adaptive Gaussian filtering and then median filtering. The dual filtering mechanism works together. Gaussian filtering eliminates Gaussian noise and low-frequency interference, providing a smooth basis for median filtering; median filtering specifically processes the remaining salt-and-pepper noise, avoiding the amplification effect of Gaussian filtering on noise, and can both smooth the image noise and retain edge details. Adaptive Gaussian filtering adjusts the convolution kernel size dynamically to cope with random noise under complex working conditions, while median filtering has excellent filtering effect on salt-and-pepper noise. This combined filtering can improve the signal-to-noise ratio and provide a high-quality image basis for subsequent edge detection.

[0052] S102. Extract the edge contour features of the bottom of the chute from the image data;

[0053] In one embodiment, the extraction of the edge contour features of the bottom of the chute from the image data includes:

[0054] Perform edge detection on the image data to extract the edge contour features of the bottom of the chute, where the edge detection uses the Canny operator.

[0055] Exemplarily, to identify the edge contour of the chute area in the image data of the present application, methods such as Canny edge detection can be used to obtain the edge contour features of the bottom of the chute to determine the bottom contour of the chute.

[0056] Specifically, the traditional Canny operator uses a fixed 3×3 neighborhood to judge the gradient direction, which is prone to edge breakage. The improved Canny operator proposed in the present application introduces 8-neighborhood gradient direction interpolation, performs linear weighted calculation in the gradient direction, improves the edge positioning accuracy, and adopts a dynamic neighborhood selection mechanism to automatically adjust the suppression window size according to the local gradient change of the image (for example, reduce it to 5×5 in the edge area and expand it to 7×7 in the flat area).

[0057] Based on the above solution, the Canny operator significantly improves the edge detection accuracy and robustness in the complex blast furnace environment through a three-layer improved architecture of gradient calculation optimization + dynamic threshold + post-processing enhancement. Multi-scale gradient analysis can adapt to the edge blurring caused by the deformation of the chute surface, and the dual-threshold dynamic adjustment mechanism effectively suppresses false edge responses. Compared with the traditional Canny algorithm, this improved solution improves the edge positioning accuracy and significantly reduces the chute contour extraction error.

[0058] S103. Determine the target inclination angle value based on the edge contour features of the bottom of the chute, where the target inclination angle value is used to characterize the chute angle.

[0059] In one embodiment, the determining the target inclination angle value based on the edge contour features of the bottom of the chute includes:

[0060] Identify the edge contour features of the bottom of the chute based on the geometric transformation algorithm to extract the chute contour line;

[0061] Determine the theoretical inclination angle value based on the angle between the chute contour line and the vertical direction.

[0062] Specifically, this application takes into account that the edge contour of the bottom of the chute is a straight line, extracts and identifies the straight line features in the existing contour, and the straight line feature extraction adopts a combination of the probabilistic Hough transform and the RANSAC algorithm.

[0063] Based on the above solution, the probabilistic Hough transform reduces the computational complexity through random sampling, and the RANSAC algorithm eliminates outliers to improve the robustness of line fitting. In the scenario of the periodic swing of the chute, the success rate of line detection by this combined method is relatively high, which is beneficial to ensuring the stability of inclination angle calculation.

[0064] Exemplarily, imaging devices are generally installed on the top of the blast furnace to observe the operating state of the top chute and the charging process, such as Figure 1 and Figure 2 shown. Operators can achieve the falling of the burden at different radial positions in the circumferential direction of the blast furnace by adjusting the inclination angle (β) of the blast furnace chute. In actual use, due to the influence of equipment control accuracy, burden extrusion and collision, etc., there are often deviations between the actual inclination angle of the chute and the set angle. Considering the harsh furnace environment during the production process, it is difficult to directly measure the inclination angle of the chute, which is not conducive to the effective implementation of the charging system.

[0065] During the production process, the imaging video at the top of the blast furnace is in a high-temperature and multi-material flow environment. Along with the rotation and angle adjustment of the chute, the falling of the burden, the rise of the coal gas flow, and the temperature change, the brightness, clarity, and contour morphology of the image change constantly. Figure 3 and Figure 4The figure shows a schematic diagram of the rotation of the blast furnace chute. During the rotation of the chute, the angle of the contour line changes periodically. When the axial center line of the chute is parallel to the camera lens, the angle between the bottom contour line of the chute and the vertical direction is the largest, and the contour line in the image is the actual contour of the chute, which can be used to calculate the actual inclination angle of the chute.

[0066] Based on the above scheme, the robustness of RANSAC is combined with the efficiency of the Hough transform to improve the accuracy of straight-line extraction in complex environments. A system error compensation model is constructed through calibration and real-time calibration, breaking through the limitations of traditional pure mathematical methods. Mechanisms such as sliding window averaging and outlier rejection are designed to ensure long-term stability under high-temperature and dusty working conditions. Through the optimization of geometric transformation algorithms and multi-dimensional error correction, the present invention realizes a complete closed-loop from image processing to physical quantity measurement, combining traditional geometric methods with deep learning feature extraction and AI algorithm optimization, which not only ensures high precision and real-time performance in industrial scenarios, but also solves the robustness problem in complex environments.

[0067] In one embodiment, determining the target inclination angle value based on the contour feature of the bottom edge of the chute includes:

[0068] A frame error correction model to determine the target inclination angle value based on the theoretical inclination angle value:

[0069] β’ = β + θ

[0070] Where β’ is the target inclination angle value, β is the theoretical inclination angle value, and θ is the correction angle.

[0071] Exemplarily, according to the angle formed by the recognized straight line and the vertical direction in the present application, the chute angle in the image can be calculated. Due to reasons such as the actual installation position of the camera and the lens angle, there is a deviation from the ideal model. As shown in the above formula, the theoretical inclination angle value is corrected.

[0072] The above scheme realizes efficient and accurate adjustment and calibration of the chute angle through a closed-loop design of "image measurement → error compensation → true value output", realizes online identification and monitoring of the inclination angle state of the blast furnace chute, improves the accurate burdening ability of the blast furnace, and enhances the intelligent level of operation and monitoring. By obtaining the furnace top image data in real time, realizing online detection of the chute inclination angle based on image recognition technology, using an image contour extraction algorithm to identify the edge contour of the chute, combining straight-line detection to calculate the inclination angle of the image at the current position of the chute, and finally obtaining the size of the chute inclination angle according to the image angle change rule before and after recognition. Through this method, real-time, safe and accurate monitoring of the inclination angle of the blast furnace chute can be realized.

[0073] In one embodiment, the above method further includes:

[0074] Obtain the image data of the running area of the blast furnace top chute in real time to output the dynamic chute inclination change data.

[0075] Exemplarily, the dynamic monitoring process introduces the Kalman filtering algorithm to predict and correct the inclination sequence.

[0076] Specifically, the Kalman filter is used to eliminate high-frequency noise interference through state estimation while maintaining the dynamic characteristics of the inclination change. In the scenario of rapid chute adjustment, this algorithm can shorten the inclination tracking delay and ensure the real-time response ability of the control system.

[0077] The above solution combines high-temperature infrared thermal imaging monitoring and high-definition vision monitoring, combines infrared thermal imaging and visual detection, breaks through the limitations of a single sensor, and realizes a complete link from monitoring to control through dynamic speed regulation and a multi-level alarm mechanism. Through the technical architecture of high-temperature image acquisition + multi-modal data fusion + intelligent control closed-loop, the millisecond-level dynamic monitoring of the blast furnace chute inclination is realized, and the traditional mechanical monitoring is upgraded to an AI-driven digital twin system.

[0078] In one embodiment, the above method further includes:

[0079] Construct a chute state evaluation model based on the inclination value, vibration frequency, and temperature gradient to evaluate the state of the blast furnace chute.

[0080] Exemplarily, construct a chute state evaluation model based on deep learning, and the input parameters include the inclination value, vibration frequency, and temperature gradient.

[0081] Specifically, geometric parameters (inclination), mechanical parameters (vibration), and thermal parameters (temperature) are incorporated into a unified evaluation framework, breaking through the limitations of a single index. Based on the dynamic threshold setting of time series analysis, through the multi-dimensional data fusion of inclination-vibration-temperature + intelligent diagnosis algorithm, the present invention realizes the accurate evaluation of the blast furnace chute state. The above deep learning model realizes the intelligent diagnosis of the chute health state through automatic feature extraction and establishes a fault warning mechanism in combination with the inclination data.

[0082] With the above technical solution, for the problem that the current identification of the blast furnace chute inclination relies too much on manual work, the identification method of the blast furnace chute inclination provided by the present invention obtains image data of the operation area of the blast furnace top chute; extracts the contour features of the bottom edge of the chute from the image data; determines the target inclination value based on the contour features of the bottom edge of the chute, where the target inclination value is used to characterize the chute angle. In the above solution, by establishing a multi-dimensional perception calculation system, real-time dynamic monitoring of the chute inclination in a high-temperature and complex environment is realized. This method breaks through the spatio-temporal limitations of traditional manual measurement, uses image processing technology to convert physical space angle information into digital signals, and eliminates the influence of equipment installation deviation through an error compensation model, greatly improving the inclination measurement accuracy. Compared with the offline detection method, the present invention can improve the uniformity coefficient of blast furnace burden distribution and effectively extend the service life of the furnace lining.

[0083] The following shows a specific implementation manner implemented by the present application:

[0084] For the identification of the blast furnace chute inclination of the image of a certain blast furnace top, as Figure 5 shown, the chute contour information can be obtained from the original image information. The image is processed using Gaussian filtering, the chute contour is extracted through Canny edge detection, and the straight line of the bottom edge contour of the chute is extracted using Hough transform line detection, as Figure 6 shown. By calculating the angle between the straight line and the vertical direction, the inclination angle of the chute in the image is obtained as 34.9°. Through the comparative analysis of manual measurement and image detection during the overhaul process, the correction angle θ is obtained as 0.7°. It can be known that the actual chute angle of this image is finally 35.6°.

[0085] Further, as an implementation of the method shown above Figure 7 shown, the embodiment of the present invention also provides an identification device for the blast furnace chute inclination, which is used to implement the method shown above Figure 7 shown. This device embodiment corresponds to the foregoing method embodiment. For the convenience of reading, the details of the foregoing method embodiment will not be described one by one in this device embodiment, but it should be clear that the device in this embodiment can correspondingly implement all the contents of the foregoing method embodiment. As Figure 8 shown, the device includes: an acquisition unit 21, an extraction unit 22, and a determination unit 23, where

[0086] The acquisition unit 21 is used to acquire image data of the operation area of the blast furnace top chute;

[0087] The extraction unit 22 is used to extract the contour features of the bottom edge of the chute from the image data;

[0088] A determination unit 23 for determining a target inclination angle value based on the contour feature of the bottom edge of the chute, where the target inclination angle value is used to characterize the chute angle.

[0089] The processor contains a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set, and by adjusting the kernel parameters, a method for identifying the inclination angle of a blast furnace chute can be realized, which can solve the problem that the current identification of the inclination angle of a blast furnace chute relies too much on manual work.

[0090] An embodiment of the present invention provides a computer-readable storage medium. The above computer-readable storage medium includes a stored program, and when the program is executed by a processor, the method for identifying the inclination angle of the blast furnace chute is realized.

[0091] An embodiment of the present invention provides a processor. The processor is used to run a program, and when the program runs, the method for identifying the inclination angle of the blast furnace chute is executed.

[0092] An embodiment of the present invention provides an electronic device. The above electronic device includes at least one processor and at least one memory connected to the processor. Wherein, the above processor is used to call the program instructions in the above memory and execute the method for identifying the inclination angle of the blast furnace chute as described above.

[0093] An embodiment of the present invention provides an electronic device 30, as Figure 9 shown, the electronic device includes at least one processor 301, at least one memory 302 connected to the processor, and a bus 303. Wherein, the processor 301 and the memory 302 complete communication with each other through the bus 303. The processor 301 is used to call the program instructions in the memory to execute the method for identifying the inclination angle of the blast furnace chute as described above.

[0094] The intelligent electronic device in this article can be a PC, PAD, mobile phone, etc.

[0095] This application also provides a computer program product, which is suitable for executing a program initialized with the steps of the method for identifying the inclination angle of the blast furnace chute when executed on a process management electronic device.

[0096] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0099] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0101] The embodiments of the present application also provide a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute the process of controlling the memory as in Figure 7 the corresponding embodiment.

[0102] A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, they implement all or part of the processes or functions in accordance with the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0103] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein again.

[0104] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, in each embodiment of the present application, the functional units may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0108] The above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for identifying the inclination angle of a blast furnace chute, characterized in that, Including: Obtaining image data of the operating area of the chute at the top of the blast furnace; Extracting the contour feature of the bottom edge of the chute from the image data; Determining a target inclination value based on the contour feature of the bottom edge of the chute, where the target inclination value is used to characterize the chute angle.

2. The method according to claim 1, characterized in that, The obtaining of the image data of the operating area of the chute at the top of the blast furnace includes: Obtaining the original image of the operating area of the chute at the top of the blast furnace; Performing noise suppression processing on the original image to determine the image data, where the noise suppression processing uses a composite algorithm of adaptive Gaussian filtering and median filtering.

3. The method according to claim 1, wherein The extracting of the contour feature of the bottom edge of the chute from the image data includes: Performing edge detection on the image data to extract the contour feature of the bottom edge of the chute, where the edge detection uses the Canny operator.

4. The method according to claim 1, wherein The determining of the target inclination value based on the contour feature of the bottom edge of the chute includes: Identifying the contour feature of the bottom edge of the chute based on a geometric transformation algorithm to extract the chute contour line; Determining the theoretical inclination value based on the angle between the chute contour line and the vertical direction.

5. The method according to claim 4, characterized in that The determining of the target inclination value based on the contour feature of the bottom edge of the chute includes: Constructing an error correction model to determine the target inclination value based on the theoretical inclination value: β’ = β + θ Where β’ is the target inclination value, β is the theoretical inclination value, and θ is the correction angle.

6. The method according to claim 1, wherein It also includes: Real-time obtaining of the image data of the operating area of the chute at the top of the blast furnace to output dynamic chute inclination change data.

7. The method according to claim 1, wherein It also includes: Constructing a chute state evaluation model based on the inclination value, vibration frequency, and temperature gradient to evaluate the state of the blast furnace chute.

8. An identifying device for the inclination angle of a blast furnace launder, characterized in that, It also includes: An obtaining unit for obtaining image data of the operating area of the chute at the top of the blast furnace; An extracting unit for extracting the contour feature of the bottom edge of the chute from the image data; A determining unit for determining a target inclination value based on the contour feature of the bottom edge of the chute, where the target inclination value is used to characterize the chute angle.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, where when the program is executed by a processor, the steps of the method for identifying the inclination of the blast furnace chute as described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory connected to the processor; where the processor is used to call the program instructions in the memory and execute the steps of the method for identifying the inclination of the blast furnace chute as described in any one of claims 1 to 7.

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