Abnormal identification method and device for track deviation of continuous casting slab and electronic equipment

Abstract

The invention discloses an abnormal recognition method and device for track deviation of a continuous casting slab and electronic equipment, and relates to the technical field of steel smelting. The abnormal recognition method for track deviation of the continuous casting slab comprises the steps that edge detection is conducted on the slab based on target image data of the slab to be produced on a continuous casting production line and a trained image recognition algorithm, and edge contour information of the slab is determined; the actual width of the slab is determined based on the edge contour information and a preset pixel size mapping relation; based on the actual width, the position of the slab on a track of the continuous casting production line is determined; and based on the position and a preset slab deviation threshold value range, whether the slab deviates from the track or not is abnormal is determined. According to the embodiment provided by the invention, the measurement error is reduced, the measurement precision of the plate blank is improved, the probability of occurrence of failure report abnormity is reduced, and the continuity of subsequent production of the plate blank and the stability of quality are improved.

Description

Technical Field

[0001] This application relates to the field of iron and steel smelting technology, and in particular to an abnormal identification method, device and electronic equipment for track deviation of continuously cast slabs. Background Technology

[0002] Currently, in the continuous casting steel production process, the precise control of slab size plays a crucial role in the subsequent slab processing. Therefore, accurately identifying the size of the cut slab and monitoring in real time whether the slab deviates from the specific threshold range of the track can promptly detect abnormalities in the production process and avoid production interruptions and equipment damage caused by slab size deviations or abnormal positions. This is of great significance for ensuring the continuity and stability of production, improving production efficiency and product quality.

[0003] However, continuous casting steelmaking faces numerous challenges in slab size identification and anomaly monitoring. Existing slab identification methods typically involve workers manually measuring and observing the slab in the cutting area using measuring tools and visually checking for deviations from the track. When slab size anomalies or positional deviations are detected, they are manually recorded and relevant personnel are notified for handling. This manual identification method is highly susceptible to human factors. It cannot obtain slab size data and positional information in real time and accurately, resulting in measurement errors and poor precision. Furthermore, manual identification is labor-intensive and sometimes it is difficult to detect and handle anomalies in a timely manner, easily leading to missed anomalies. In severe cases, this can affect the continuity of production and the stability of quality. Summary of the Invention

[0004] This application provides an anomaly identification method, device, and electronic device for track deviation of continuously cast slabs. The embodiments provided by this application solve the technical problems in the prior art that cause measurement errors and poor accuracy, which easily lead to missed anomalies and, in severe cases, affect the continuity of production and the stability of quality. The embodiments provided by this application reduce measurement errors, improve the measurement accuracy of slabs, reduce the probability of missed anomalies, and improve the continuity of subsequent slab production and the stability of quality.

[0005] Based on the edge contour information and the preset pixel size mapping relationship, the actual width of the slab is determined; Based on the actual width, the position of the slab on the track of the continuous casting production line is determined; Based on the position and the preset slab offset threshold range, it is determined whether the slab has deviated from the track and caused an anomaly.

[0006] In one feasible implementation, before performing edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and the trained image recognition algorithm to determine the edge contour information of the slab, the method further includes: Acquire initial image data of slabs to be produced on the continuous casting production line; The initial image data is subjected to sequential filtering and contrast enhancement processing to determine the candidate image data of the slab; The candidate image data is binarized to determine the target image data of the slab to be produced on the continuous casting production line.

[0007] In one feasible implementation, determining the actual width of the slab based on the edge contour information and the preset pixel size mapping relationship includes: Based on the edge contour information, the number of edge pixels of the slab is determined; Based on a preset pixel size mapping relationship, the number of edge pixels of the slab is converted into the actual length of the slab; The actual width of the slab is determined based on its actual length.

[0008] In one feasible implementation, the preset slab offset threshold range is determined by the following method: Based on the actual length, the center position of the track, and the preset offset threshold, the preset slab offset threshold range is determined.

[0009] In one feasible implementation, determining whether the slab has deviated abnormally from the track based on the position and a preset slab offset threshold range includes: If the position of the slab is within the preset slab offset threshold range, it is determined that the slab has not deviated from the track and that the slab has not experienced any abnormality. If the position of the slab in multiple consecutive frames of target image data is not within the preset slab offset threshold range, it is determined that the slab has deviated abnormally from the track.

[0010] In one feasible implementation, after determining whether the slab has deviated abnormally from the track based on the position and a preset slab offset threshold range, the method further includes: After determining that the slab has deviated abnormally from the track, an alarm level is determined based on the degree of deviation of the slab. The alarm includes at least one of the following: interface prompt, audible and visual alarm, and SMS notification.

[0011] In one feasible implementation, the graded alarm level is determined based on the degree of deviation of the slab: If the deviation of the slab is within the first preset offset range, a first-level warning is triggered. If the deviation of the slab is within the second preset offset range, a level two warning is triggered. If the deviation of the slab is within the third preset offset range, a level 3 warning is triggered, and the continuous casting production line is controlled to slow down or stop. The third preset offset range is greater than the second preset offset range, and the second preset offset range is greater than the first preset offset range.

[0012] In a second aspect, this application provides an anomaly identification device for track deviation of continuously cast slabs, wherein the anomaly identification of track deviation of continuously cast slabs includes: The first determining module is used to perform edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and the trained image recognition algorithm, and to determine the edge contour information of the slab. The second determining module is used to determine the actual width of the slab based on the edge contour information and the preset pixel size mapping relationship; The third determining module is used to determine the position of the slab on the track of the continuous casting production line based on the actual width; The fourth determining module is used to determine whether the slab has deviated from the track and caused an abnormality based on the position and a preset slab offset threshold range.

[0013] In a third aspect of this application, an electronic device is provided, comprising: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the machine-readable instructions are executed by the processor to perform the steps of the above-described abnormal identification method for continuous casting slab track deviation.

[0014] In a fourth aspect of this application, an embodiment of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the above-described method for identifying abnormal deviations in the trajectory of a continuously cast slab.

[0015] Compared with the prior art, the method, apparatus, and electronic equipment for identifying abnormal deviations in the continuous casting slab track provided in this application can perform edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and a trained image recognition algorithm, determine the edge contour information of the slab, then determine the actual width of the slab based on the edge contour information and a preset pixel size mapping relationship, and determine the position of the slab on the track of the continuous casting production line based on the actual width, and then determine whether the slab has deviated from the track and caused an abnormality based on the position and a preset slab offset threshold range. This application reduces measurement errors, improves the measurement accuracy of the slab, reduces the probability of missed anomalies, and improves the continuity and quality stability of subsequent slab production. Attached Figure Description

[0016] Figure 1 A flowchart illustrating an anomaly identification method for track deviation in continuous casting slabs provided in an embodiment of this application is shown. Figure 2 This paper shows a structural block diagram of an anomaly identification method and apparatus for track deviation of a continuously cast slab provided in an embodiment of this application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0017] Figure 2 and Figure 3 The correspondence between the figure labels and figure titles in the accompanying drawings is as follows: 200 Abnormal identification device for deviation of continuous casting slab track; 210 Acquisition module; 220 First preprocessing module; 230 Second preprocessing module; 240 First determination module; 250 Second determination module; 260 Third determination module; 270 Fourth determination module; 280 Fifth determination module; 300 Electronic device; 310 Processor; 320 Memory; 330 Bus. Detailed Implementation

[0018] To better understand the technical solutions provided in the embodiments of this specification, the technical solutions of the embodiments of this specification will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.

[0019] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The term "two or more" includes two or more cases.

[0020] First, the applicable application scenarios of this application will be introduced. The embodiments provided in this application are applicable to the field of steel smelting technology, and in particular, they relate to an abnormal identification method, device and electronic equipment for track deviation of continuous casting slabs.

[0021] Currently, continuous casting steelmaking faces numerous challenges in slab size identification and anomaly monitoring. Existing slab identification methods typically involve workers manually measuring and observing the slab in the cutting area using measuring tools and visually checking for deviations from the track. When slab size anomalies or positional deviations are detected, they are manually recorded and relevant personnel are notified for handling. This manual identification method is highly susceptible to human factors. It cannot obtain slab size data and positional information in real time and accurately, resulting in measurement errors and poor precision. Furthermore, manual identification is labor-intensive and sometimes it is difficult to detect and handle anomalies in a timely manner, easily leading to missed anomalies. In severe cases, this can affect the continuity of production and the stability of quality.

[0022] Based on this, the embodiments of this application provide an anomaly identification method, device and electronic equipment for track deviation of continuously cast slabs. The embodiments provided by this application solve the technical problems in the prior art that cause measurement errors and poor accuracy, which easily lead to missed anomalies and, in severe cases, affect the continuity of production and quality stability. The embodiments provided by this application reduce measurement errors, improve the measurement accuracy of slabs, reduce the probability of missed anomalies, and improve the continuity of subsequent slab production and quality stability.

[0023] Figure 1 A flowchart illustrating an anomaly identification method for track deviation in continuously cast slabs provided in an embodiment of this application is shown. Figure 1 As shown, the method for identifying abnormal deviations in the trajectory of continuously cast slabs includes the following steps: S101. Based on the target image data of the slab to be produced on the continuous casting production line and the trained image recognition algorithm, perform edge detection on the slab to determine the edge contour information of the slab.

[0024] In this step, the embodiment provided in this application installs an industrial camera above the slab cutting area on the continuous casting production line for continuous casting steel pouring, and connects the industrial camera to an external computer control device. When the slab is cut and moves on the track, the industrial camera continuously takes pictures of the slab at a set frame rate to obtain target image data about the slab, and transmits the target image data to the computer in real time. The computer uses a pre-trained image recognition algorithm to perform edge processing on the image and recognize the edge contour information of the slab.

[0025] It is understood that the installation position of the industrial camera in the embodiments provided in this application can be customized and selected according to different application scenarios and usage environments. The industrial camera in the embodiments provided in this application can be specifically installed at a position 45°±5° above the slab cutting area of ​​the continuous casting steel production line to ensure that the field of view of the industrial camera can completely cover the movement trajectory of the slab.

[0026] It should be noted that the embodiments provided in this application can adjust the shooting frame rate and resolution of the industrial camera according to the two influencing factors of the slab size range and movement speed, so that it can clearly capture the target image data of the slab and support the acquisition of the target area.

[0027] The shooting frame rate of the industrial camera in the embodiments provided in this application can be set to 1500 frames / second, and the resolution of the industrial camera in the embodiments provided in this application is 1920px×1080px.

[0028] In the embodiments provided in this application, the industrial camera is connected to the computer via a high-speed data cable to ensure rapid transmission of target image data.

[0029] For example, before performing edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and a trained image recognition algorithm to determine the edge contour information of the slab, the method further includes: Acquire initial image data of the slab to be produced on the continuous casting production line; perform filtering and contrast enhancement processing on the initial image data to determine candidate image data of the slab; perform binarization processing on the candidate image data to determine the target image data of the slab to be produced on the continuous casting production line.

[0030] It is understood that the embodiments provided in this application first acquire the initial image data of the slab to be produced on the continuous casting production line, and then preprocess the initial image data. First, the initial image data is filtered to remove noise interference, smooth the image pixel values, and reduce noise caused by equipment noise and environmental factors. Then, the image data after noise processing is contrast-enhanced to determine the candidate image data of the slab.

[0031] Here, the formula for the filtered output is: g(x,y)=med{f(xk,yl),(k,l∈W); Where f(x,y) represents the initial image data; g(x,y) represents the filtered image; and W represents the two-dimensional template. It should be noted that, after determining the candidate image data of the slab, the embodiments provided in this application begin to perform binarization processing on the candidate image data of the slab to determine the target image data of the slab to be produced on the continuous casting production line. Specifically, pixels with gray values ​​exceeding a set threshold are set to white, and pixels with gray values ​​below the threshold are set to black, highlighting the difference between the slab and the background, which facilitates subsequent edge recognition.

[0032] The embodiments provided in this application have been verified through experiments, and the threshold value can be set to 123.

[0033] It should be noted that the embodiments provided in this application perform edge detection on the slab in the target image data. Specifically, the actual 3×3 image pixel matrix is ​​convolved with a trained multi-gradient image recognition model to obtain eight gradient values: Gi(x, y) (i=1,2,……,8). Here, to prevent the optimal gradient value from being too large, the gradient value is multiplied by an influence factor, which is 0.25 in this method. Then, the output value of the above pixel is determined according to the following formula, thereby obtaining the edge contour information of the slab.

[0034] The following formula is specifically: ; M(x,y) is used to characterize the edge contour information of the slab.

[0035] S102. Determine the actual width of the slab based on the edge contour information and the preset pixel size mapping relationship.

[0036] In this step, after determining the edge contour information of the slab, the embodiments provided in this application need to further determine the actual width of the slab based on a preset pixel size mapping relationship.

[0037] For example, the number of edge pixels of the slab is determined based on the edge contour information; the number of edge pixels of the slab is converted into the actual length of the slab based on a preset pixel size mapping relationship; and the actual width of the slab is determined based on the actual length of the slab.

[0038] It is understood that the embodiments provided in this application pre-calibrate the images captured by the camera to determine the actual length represented by each pixel, i.e., the preset pixel size mapping relationship. Then, by counting the number of edge pixels of the slab, the actual length of the slab is calculated, and the actual width of the slab is determined based on the actual length of the slab. This achieves high-precision identification of the width of the slab cut during the continuous casting process and reduces the degree of influence of the environment on the identification of the slab width.

[0039] S103. Based on the actual width, determine the position of the slab on the track of the continuous casting production line.

[0040] In this step, the embodiment provided in this application determines the position of the slab on the track of the continuous casting production line based on the track center position after determining the actual width of the slab.

[0041] It is understood that the track center position in the embodiments provided in this application is represented by H.

[0042] S104. Based on the position and the preset slab offset threshold range, determine whether the slab has deviated from the track and caused an abnormality.

[0043] In this step, the embodiments provided in this application need to compare the position of the slab on the track with the preset slab offset threshold range to determine whether the slab has deviated from the track, that is, to determine whether the slab has deviated from the track.

[0044] It is understandable that if the position of the slab is within the preset slab offset threshold range, it is determined that the slab has not deviated from the track and the slab has not experienced any abnormality; if the position of the slab in multiple consecutive frames of target image data is not within the preset slab offset threshold range, it is determined that the slab has deviated from the track and experienced an abnormality.

[0045] In the above-mentioned embodiments provided in this application, the multi-frame voting mechanism for continuously using multiple frames of target image data is a five-out-of-four mechanism. When the position of the slab exceeding the slab in at least four of the five consecutive frames of target image data is not within the preset slab offset threshold range, it is determined that the slab has deviated from the track abnormally.

[0046] It should be noted that the preset slab offset threshold range in the embodiments provided in this application can be customized and used according to different application scenarios and usage conditions.

[0047] For example, the preset slab offset threshold range is determined in the following way: Based on the actual length, the center position of the track, and the preset offset threshold, the preset slab offset threshold range is determined.

[0048] In the above-described embodiments, the sum of the actual length, the center position of the track, and a preset offset threshold is determined as the preset slab offset threshold range. The formula for the preset slab offset threshold range is: R = H ± L + U; Wherein, R is used to characterize the preset slab offset threshold range; H is used to characterize the center position of the track; L is used to characterize the actual length of the slab; and U is used to characterize the preset slab offset threshold. The preset slab offset threshold in the embodiments provided in this application can be customized and used according to different application scenarios and usage conditions. In the embodiments provided in this application, U is set to 100mm.

[0049] For example, after determining whether the slab has deviated abnormally from the track based on its position and a preset slab offset threshold range, the method further includes: After determining that the slab has deviated from the track, an alarm level is determined based on the degree of deviation. The alarm includes at least one of the following: interface prompts, audible and visual alarms, and SMS notifications.

[0050] In the above, if the deviation of the slab is within the first preset offset range, a first-level warning is triggered; if the deviation of the slab is within the second preset offset range, a second-level warning is triggered; if the deviation of the slab is within the third preset offset range, a third-level warning is triggered, and the continuous casting production line is controlled to reduce speed or stop. The third preset offset range is greater than the second preset offset range, and the second preset offset range is greater than the first preset offset range.

[0051] It should be noted that if the deviation of the slab is within the first preset offset range (e.g., 20-50mm), a level one warning is triggered, such as a flashing prompt on the HMI interface; if the deviation of the slab is within the second preset offset range (e.g., 50-80mm), a level two warning is triggered, such as an audible and visual alarm plus an SMS notification; if the deviation of the slab is within the third preset offset range (e.g., offset > 80mm), a level three warning is triggered, such as an audible and visual alarm issuing an alarm signal, while simultaneously displaying the abnormal position information and dimensional data of the slab, reminding the operator to handle the situation promptly, and controlling the continuous casting production line to slow down or stop.

[0052] This application applies machine vision technology to the field of continuous casting slab size recognition and abnormal alarm, which is used to promptly alarm slabs that deviate from a specific threshold range of the track, realizing the transformation from traditional contact or manual monitoring methods to non-contact, automated and high-precision monitoring, providing reliable data support and safety assurance for continuous casting production.

[0053] Compared with the prior art, the abnormal identification method for continuous casting slab track deviation provided in this application can perform edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and a trained image recognition algorithm, determine the edge contour information of the slab, then determine the actual width of the slab based on the edge contour information and a preset pixel size mapping relationship, and determine the position of the slab on the track of the continuous casting production line based on the actual width, and then determine whether the slab has deviated from the track and caused an abnormality based on the position and a preset slab offset threshold range. This application reduces measurement error, improves the measurement accuracy of the slab, reduces the probability of missed anomalies, and improves the continuity and quality stability of subsequent slab production.

[0054] This application utilizes machine vision technology to accurately identify the edge region of a slab and obtain the edge contour information of the slab to be produced. Then, based on the preset pixel size mapping relationship between pixels and actual dimensions, the width of the slab is accurately calculated, which greatly improves the accuracy of slab edge recognition.

[0055] The industrial camera in this application continuously captures images of the slab at a set frame rate and transmits them in real time, enabling real-time monitoring of the slab's position and size information. When the slab deviates from the preset slab offset threshold range or its size becomes abnormal, this application can quickly trigger an alarm to promptly remind operators, ensuring the continuity of production. It effectively avoids production interruptions and equipment damage caused by untimely detection of abnormalities, reducing production costs and downtime.

[0056] This application can accurately identify slight positional deviations in slabs, which can reduce missed anomalies compared to the human eye, providing a more reliable safety guarantee for continuous casting production.

[0057] Figure 2 This is a structural block diagram of an anomaly identification device for track deviation in continuous casting slabs provided in an embodiment of this application. Figure 2 As shown, the abnormal identification device 200 for continuous casting slab track deviation includes: The acquisition module 210 is used to acquire the initial image data of the slabs to be produced on the continuous casting production line.

[0058] The first preprocessing module 220 is used to perform filtering and contrast enhancement processing on the initial image data to determine the candidate image data of the slab.

[0059] The second preprocessing module 230 is used to perform binarization processing on the candidate image data to determine the target image data of the slab to be produced on the continuous casting production line.

[0060] The first determining module 240 is used to perform edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and the trained image recognition algorithm, and to determine the edge contour information of the slab.

[0061] The second determining module 250 is used to determine the actual width of the slab based on edge contour information and a preset pixel size mapping relationship.

[0062] The third determining module 260 is used to determine the position of the slab on the track of the continuous casting production line based on the actual width.

[0063] The fourth determining module 270 is used to determine whether the slab has deviated from the track and caused an abnormality based on its position and a preset slab offset threshold range.

[0064] The fifth determining module 280 is used to determine the graded alarm level based on the degree of deviation of the slab after determining that the slab has deviated from the track abnormally. The alarm includes at least one of the following: interface prompt, audible and visual alarm and SMS notification.

[0065] For example, the second determining module 250 is specifically used for: The number of edge pixels of the slab is determined based on the edge contour information.

[0066] Based on a preset pixel size mapping relationship, the number of edge pixels of the slab is converted into the actual length of the slab.

[0067] The actual width of the slab is determined based on its actual length.

[0068] For example, the preset slab offset threshold range is determined in the following way: Based on the actual length, the center position of the track, and the preset offset threshold, the preset slab offset threshold range is determined.

[0069] For example, the fourth determining module 270 is specifically used for: If the position of the slab is within the preset slab offset threshold range, it is determined that the slab has not deviated from the track and that the slab has not experienced any abnormalities.

[0070] If the position of the slab in multiple consecutive frames of target image data is not within the preset slab offset threshold range, it is determined that the slab has deviated from the track abnormally.

[0071] For example, the fifth determining module 280 is specifically used for: If the deviation of the slab is within the first preset offset range, a first-level warning will be triggered.

[0072] If the deviation of the slab is within the second preset offset range, a level two warning will be triggered.

[0073] If the deviation of the slab is within the third preset offset range, a level 3 warning is triggered, and the continuous casting production line is controlled to slow down or stop. The third preset offset range is greater than the second preset offset range, and the second preset offset range is greater than the first preset offset range.

[0074] The abnormal identification device 200 for continuous casting slab track deviation provided in this application embodiment, compared with the prior art, can perform edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and a trained image recognition algorithm, determine the edge contour information of the slab, then determine the actual width of the slab based on the edge contour information and a preset pixel size mapping relationship, and determine the position of the slab on the track of the continuous casting production line based on the actual width, and then determine whether the slab has deviated from the track and caused an abnormality based on the position and a preset slab offset threshold range. This application reduces measurement error, improves the measurement accuracy of the slab, reduces the probability of missed anomalies, and improves the continuity and quality stability of subsequent slab production.

[0075] This application utilizes machine vision technology to accurately identify the edge region of the slab and obtain the edge contour information of the slab to be produced. Then, based on the preset pixel size mapping relationship between pixels and actual size calibration, the width of the slab is accurately calculated, which greatly improves the accuracy of slab edge recognition.

[0076] The industrial camera in this application continuously captures images of the slab at a set frame rate and transmits them in real time, enabling real-time monitoring of the slab's position and size information. When the slab deviates from the preset slab offset threshold range or its size becomes abnormal, this application can quickly trigger an alarm to promptly remind operators, ensuring the continuity of production. It effectively avoids production interruptions and equipment damage caused by untimely detection of abnormalities, reducing production costs and downtime.

[0077] This application can accurately identify slight positional deviations in slabs, which can reduce missed anomalies compared to the human eye, providing a more reliable safety guarantee for continuous casting production.

[0078] Please see Figure 3 , Figure 3 This application provides a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 3 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0079] Memory 320 stores machine-readable instructions executable by processor 310. When electronic device 300 is running, processor 310 and memory 320 communicate via bus 330. When the machine-readable instructions are executed by processor 310, they can perform the operations described above. Figure 1The steps of the abnormal identification method for continuous casting slab track deviation in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0080] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the abnormal identification method for continuous casting slab track deviation in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0081] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0082] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0083] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0084] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to execute a process for identifying anomalies in the trajectory deviation of a continuously cast slab.

[0088] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0089] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0091] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0092] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0093] If the integrated unit is implemented as 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 this 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0095] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this specification.

[0096] Obviously, those skilled in the art can make various modifications and variations to this specification without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, this specification is also intended to include such modifications and variations.

Claims

1. A method for identifying anomalies in the trajectory deviation of continuously cast slabs, characterized in that, The method for identifying abnormal deviations in the continuous casting slab track includes: Based on the target image data of the slab to be produced on the continuous casting production line and the trained image recognition algorithm, edge detection is performed on the slab to determine the edge contour information of the slab. Based on the edge contour information and the preset pixel size mapping relationship, the actual width of the slab is determined; Based on the actual width, the position of the slab on the track of the continuous casting production line is determined; Based on the position and the preset slab offset threshold range, it is determined whether the slab has deviated from the track and caused an anomaly.

2. The method for identifying abnormal deviations in the trajectory of continuously cast slabs according to claim 1, characterized in that, Before performing edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and the trained image recognition algorithm to determine the edge contour information of the slab, the method further includes: Acquire initial image data of slabs to be produced on the continuous casting production line; The initial image data is subjected to sequential filtering and contrast enhancement processing to determine the candidate image data of the slab; The candidate image data is binarized to determine the target image data of the slab to be produced on the continuous casting production line.

3. The method for identifying abnormal deviations in the trajectory of continuously cast slabs according to claim 1, characterized in that, Determining the actual width of the slab based on the edge contour information and the preset pixel size mapping relationship includes: Based on the edge contour information, the number of edge pixels of the slab is determined; Based on a preset pixel size mapping relationship, the number of edge pixels of the slab is converted into the actual length of the slab; The actual width of the slab is determined based on its actual length.

4. The method for identifying abnormal deviations in the trajectory of continuously cast slabs according to claim 3, characterized in that, The preset slab offset threshold range is determined using the following method: Based on the actual length, the center position of the track, and the preset offset threshold, the preset slab offset threshold range is determined.

5. The method for identifying abnormal deviations in the trajectory of continuously cast slabs according to claim 1, characterized in that, The step of determining whether the slab has deviated abnormally from the track based on the position and a preset slab offset threshold range includes: If the position of the slab is within the preset slab offset threshold range, it is determined that the slab has not deviated from the track and that the slab has not experienced any abnormality. If the position of the slab in multiple consecutive frames of target image data is not within the preset slab offset threshold range, it is determined that the slab has deviated abnormally from the track.

6. The method for identifying abnormal deviations in the trajectory of continuously cast slabs according to claim 1, characterized in that, After determining whether the slab has deviated abnormally from the track based on the position and a preset slab offset threshold range, the method further includes: After determining that the slab has deviated abnormally from the track, an alarm level is determined based on the degree of deviation of the slab. The alarm includes at least one of the following: interface prompt, audible and visual alarm, and SMS notification.

7. The method for identifying abnormal deviations in the trajectory of continuously cast slabs according to claim 6, characterized in that, The alarm level is determined based on the degree of deviation of the slab. If the deviation of the slab is within the first preset offset range, a first-level warning is triggered. If the deviation of the slab is within the second preset offset range, a level two warning is triggered. If the deviation of the slab is within the third preset offset range, a level 3 warning is triggered, and the continuous casting production line is controlled to slow down or stop. The third preset offset range is greater than the second preset offset range, and the second preset offset range is greater than the first preset offset range.

8. An abnormal identification device for track deviation of continuously cast slabs, characterized in that, The abnormal identification of the continuous casting slab track deviation includes: The first determining module is used to perform edge detection on the slab based on the target image data of the slab to be produced on the continuous casting production line and the trained image recognition algorithm, and to determine the edge contour information of the slab. The second determining module is used to determine the actual width of the slab based on the edge contour information and the preset pixel size mapping relationship; The third determining module is used to determine the position of the slab on the track of the continuous casting production line based on the actual width; The fourth determining module is used to determine whether the slab has deviated from the track and caused an abnormality based on the position and a preset slab offset threshold range.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the abnormal identification method for deviation of continuous casting slab track as described in any of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the abnormal identification method for deviation of the continuous casting slab track as described in any one of claims 1-7.

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