A mobile horizontal continuous casting profile cutting equipment

Abstract

This invention relates to the field of cutting equipment technology, specifically to a mobile horizontal continuous casting profile cutting device. The cutting device includes a cutting equipment body and a detection device. The detection device includes an infrared sensor, a vibration sensor, and a detection processing module. The detection processing module is communicatively connected to the infrared sensor and the vibration sensor to acquire infrared image sequences and vibration data sequences for saw blade anomaly detection. The saw blade anomaly detection process includes: segmenting the vibration data sequence to obtain each vibration segment and determining the vibration anomaly assessment value for each vibration segment; simultaneously, performing temperature identification on each infrared image in the infrared image sequence to determine the abnormal high-temperature performance of the saw blade in each infrared image; combining the vibration anomaly assessment value and the abnormal high-temperature performance to determine the saw blade anomaly index, thereby determining whether a saw blade anomaly exists. This invention effectively improves the accuracy of saw blade anomaly detection in cutting equipment.

Description

Technical Field

[0001] This invention relates to the field of cutting equipment technology, and specifically to a mobile horizontal continuous casting profile cutting device. Background Technology

[0002] With the continuous improvement of industrial automation, cutting equipment is being used more and more widely in metal processing. Mobile horizontal continuous casting profile cutting equipment is a specialized device for cutting horizontal continuous casting profiles. It typically includes a cutting frame, a moving wheel system, a clamping device, and a cutting unit. Its mobility allows for flexible repositioning according to production needs, making it suitable for various production environments. The mobility and high efficiency of horizontal continuous casting profile cutting equipment make it an indispensable part of modern industrial production, especially in situations requiring frequent adjustments to production layout or increased production efficiency.

[0003] During the operation of horizontal continuous casting profile cutting equipment, saw blade wear and saw blade deformation caused by localized temperature increases directly affect the cutting quality of the horizontal continuous casting profiles. Therefore, it is necessary to monitor saw blade anomalies. In existing technologies, saw blade deformation is typically assessed manually based on experience while the machine is stopped. However, this method is often susceptible to subjective factors, its accuracy cannot be guaranteed, and it has a certain lag, usually only detecting the anomaly after severe deformation has occurred. Since saw blade wear and saw blade deformation caused by localized temperature increases both result in insufficient sharpness at the contact points during cutting, leading to abnormal vibrations in the cutting device. Therefore, by monitoring saw blade vibration online and using a threshold comparison method to judge the monitored saw blade vibration signals, saw blade anomaly detection can be achieved, effectively avoiding the lag inherent in manual saw blade anomaly detection. However, due to the possible differences in dimensions, hardness, and other parameters between different batches of continuously cast profiles, the monitored vibration signals may vary significantly. Additionally, when the pressure of the hydraulic module is inappropriate, it can cause the material being cut to vibrate during the cutting process, leading to additional vibration anomalies. Consequently, the accuracy of using the threshold comparison method to judge the monitored saw blade vibration signals for saw blade anomaly detection is poor. Summary of the Invention

[0004] The purpose of this invention is to provide a mobile horizontal continuous casting profile cutting device to solve the problem of inaccurate detection of saw blade abnormalities in existing cutting devices.

[0005] To solve the above-mentioned technical problems, in a first aspect, the present invention provides a movable horizontal continuous casting profile cutting device. The cutting device includes a cutting device body and a detection device. The detection device includes an infrared sensor for detecting the surface temperature of the saw blade, a vibration sensor for detecting saw blade vibration, and a detection processing module. The detection processing module is communicatively connected to the infrared sensor and the vibration sensor to acquire infrared image sequences and vibration data sequences, and performs saw blade anomaly detection based on the infrared image sequences and vibration data sequences. The saw blade anomaly detection process includes:

[0006] The vibration data sequence is segmented to obtain each vibration segment. Based on the fluctuation of each vibration segment, the vibration anomaly assessment value of each vibration segment is determined.

[0007] Temperature identification is performed on each infrared image in the infrared image sequence to determine the degree of abnormal high temperature performance of the saw blade in each infrared image;

[0008] Based on the growth and change of the vibration anomaly assessment value and the growth and change of the abnormal high temperature performance, and combined with the magnitude of the vibration anomaly assessment value and the abnormal high temperature performance close to the current moment, the saw blade anomaly index is determined.

[0009] Based on the aforementioned saw blade abnormality indicators, determine whether a saw blade abnormality exists.

[0010] In conjunction with the first aspect mentioned above, among some possible implementation methods, the vibration anomaly assessment values ​​for each vibration segment are determined, including:

[0011] The average vibration value of each vibration segment is determined based on the average distribution of vibration data in each vibration segment.

[0012] A reference vibration segment is determined in the vibration segmentation, and the vibration deviation between each vibration segment and the reference vibration segment is determined based on the difference in the mean vibration value between each vibration segment and the reference vibration segment.

[0013] Based on the extreme value distribution of vibration data in each vibration segment, determine the extreme value sequence of each vibration segment;

[0014] The extreme value sequence of each vibration segment is matched with the extreme value sequence of the benchmark vibration segment. Based on the matching results and the difference between each extreme value in the extreme value sequence of each vibration segment and the vibration mean of the benchmark vibration segment, the matching similarity index between each vibration segment and the benchmark vibration segment is determined.

[0015] By integrating the vibration deviation and matching similarity index, a vibration anomaly assessment value is determined for each vibration segment.

[0016] In conjunction with the first aspect above, in some possible implementations, the extreme value sequence includes a maximum value sequence and a minimum value sequence, and the matching similarity index between each vibration segment and the reference vibration segment is determined, including:

[0017] Both the maximum and minimum value sequences are treated as a single target extreme value sequence.

[0018] The target extreme value sequence of each vibration segment is matched with the target extreme value sequence corresponding to the benchmark vibration segment to obtain each extreme value matching pair;

[0019] The sub-matching similarity index is determined by combining the matching distance between extreme points in each extreme matching pair and the difference between each extreme value in the target extreme value sequence of each vibration segment and the vibration mean of the benchmark vibration segment.

[0020] By combining the sub-matching similarity index corresponding to the maximum value sequence between each vibration segment and the reference vibration segment, and the sub-matching similarity index corresponding to the minimum value sequence between each vibration segment and the reference vibration segment, a matching similarity index between each vibration segment and the reference vibration segment is determined.

[0021] In conjunction with the first aspect above, in some possible implementations, the first vibration segment among all the vibration segments is determined as the reference vibration segment.

[0022] In conjunction with the first aspect mentioned above, among some possible implementations, the degree of abnormal high temperature performance of the saw blade in each infrared image is determined, including:

[0023] The saw blade region is segmented for each infrared image in the infrared image sequence to obtain the saw blade region;

[0024] The saw blade area is divided to identify each abnormally high temperature zone;

[0025] A reference infrared image is identified among all infrared images in the infrared image sequence. The uniformity of grayscale distribution in the saw blade area of ​​each infrared image in the infrared image sequence, the average distribution of grayscale values ​​in each abnormally high temperature area of ​​the saw blade area, the size of each abnormally high temperature area, and the difference in the average distribution of grayscale values ​​between the saw blade area of ​​each infrared image in the infrared image sequence and the saw blade area of ​​the reference infrared image are considered to determine the abnormal high temperature performance of the saw blade in each infrared image.

[0026] In conjunction with the first aspect mentioned above, among some possible implementations, the degree of abnormal high temperature performance of the saw blade in each infrared image is determined, including:

[0027] The average gray value of the saw blade region in each infrared image in the infrared image sequence is determined based on the average distribution of gray values ​​in the saw blade region of each infrared image in the infrared image sequence.

[0028] Based on the difference in the mean grayscale value between the saw blade area of ​​each infrared image in the infrared image sequence and the saw blade area of ​​the reference infrared image, the grayscale difference index corresponding to each infrared image in the infrared image sequence is determined.

[0029] The grayscale information entropy corresponding to each infrared image in the infrared image sequence is determined based on the number of pixels with the same grayscale level in the saw blade region of each infrared image in the infrared image sequence.

[0030] By combining the average distribution of grayscale values ​​in each abnormally high temperature region within the saw blade area of ​​each infrared image in the infrared image sequence, as well as the size of each abnormally high temperature region, the abnormal temperature performance value corresponding to each infrared image in the infrared image sequence is determined.

[0031] By combining the grayscale difference index, grayscale information entropy, and abnormal temperature performance value corresponding to each infrared image in the infrared image sequence, the abnormal high temperature performance of the saw blade in each infrared image is determined.

[0032] In conjunction with the first aspect mentioned above, among some possible implementation methods, the abnormal indicators of the saw blade are determined, including:

[0033] Based on the growth and change of the vibration anomaly assessment value and the growth and change of the abnormal high temperature performance, the abnormal vibration growth value and the abnormal high temperature growth value are determined.

[0034] Based on the abnormal vibration growth value and abnormal high temperature growth value, combined with the vibration anomaly assessment value of the last vibration segment and the abnormal high temperature performance of the last infrared image in the infrared image sequence, the saw blade anomaly index is determined.

[0035] In conjunction with the first aspect mentioned above, among some possible implementation methods, the abnormal vibration growth value and the abnormal high temperature growth value are determined, including:

[0036] Determine the difference between the vibration anomaly assessment value of each subsequent vibration segment and the preceding vibration segment in all the vibration segments, and use the normalized result of the mean of all differences as the abnormal vibration growth value.

[0037] The difference in abnormal high temperature performance between each subsequent infrared image and its preceding infrared image in the infrared image sequence is determined, and the normalized result of the mean of all differences is taken as the abnormal high temperature growth value.

[0038] In conjunction with the first aspect mentioned above, among some possible implementation methods, the abnormal indicators of the saw blade are determined, including:

[0039] The first abnormality index is determined based on the vibration anomaly assessment value of the last vibration segment and the abnormal vibration growth value.

[0040] The second anomaly index is determined based on the abnormal high temperature performance of the last infrared image in the infrared image sequence and the abnormal high temperature growth value.

[0041] By combining the first and second abnormal indicators, the abnormal indicators of the saw blade are determined.

[0042] In conjunction with the first aspect above, in some possible implementations, the saw blade anomaly detection process further includes: if a saw blade anomaly is found, the current saw blade is replaced with a spare saw blade.

[0043] To address the aforementioned technical problems, in a second aspect, the present invention also provides a method for detecting abnormal saw blades in a mobile horizontal continuous casting profile cutting device, the method comprising:

[0044] Infrared image sequences and vibration data sequences are acquired. The infrared images in the infrared image sequence are obtained by detecting the surface temperature of the saw blade, and the vibration data in the vibration data sequence are obtained by detecting the vibration of the saw blade.

[0045] The vibration data sequence is segmented to obtain each vibration segment. Based on the fluctuation of each vibration segment, the vibration anomaly assessment value of each vibration segment is determined.

[0046] Temperature identification is performed on each infrared image in the infrared image sequence to determine the degree of abnormal high temperature performance of the saw blade in each infrared image;

[0047] Based on the growth and change of the vibration anomaly assessment value and the growth and change of the abnormal high temperature performance, and combined with the magnitude of the vibration anomaly assessment value and the abnormal high temperature performance close to the current moment, the saw blade anomaly index is determined.

[0048] Based on the aforementioned saw blade abnormality indicators, determine whether a saw blade abnormality exists.

[0049] To address the aforementioned technical problems, in a third aspect, the present invention also provides a saw blade anomaly detection system for a portable horizontal continuous casting profile cutting device, comprising a memory and a processor. The memory stores executable program code, and the processor retrieves and runs the executable program code from the memory, causing the device to perform the steps of the saw blade anomaly detection process described in the first aspect or any possible implementation thereof.

[0050] To address the aforementioned technical problems, in a fourth aspect, the present invention also provides a computer program product comprising: computer program code, which, when executed on a computer, causes the computer to perform the steps of the saw blade anomaly detection process described in the first aspect or any possible implementation thereof.

[0051] To address the aforementioned technical problems, in a fifth aspect, the present invention also provides a computer-readable storage medium storing computer program code that, when executed on a computer, causes the computer to perform the steps of the saw blade anomaly detection process described in the first aspect or any possible implementation thereof.

[0052] This invention offers the following advantages: Infrared image sequences are obtained by detecting the surface temperature of the saw blade, and vibration data sequences are obtained by detecting saw blade vibration. To facilitate subsequent joint analysis with the infrared image sequences, the vibration data sequences are segmented to obtain various vibration segments. When the saw blade experiences significant wear and deformation, the contact area during cutting becomes less sharp, and the deformation causes uneven force distribution on the contact area. This results in abnormal vibration signals generated during the cutting process. Specifically, these abnormalities typically manifest as high vibration amplitude and uneven timing of the high amplitude. Therefore, based on the fluctuation characteristics of each vibration segment, an abnormal vibration assessment value for each segment is determined. Meanwhile, during normal cutting, the temperature of a saw blade rises significantly at its edge, gradually decreasing towards the center, and the temperature at the edge is relatively uniform. Therefore, the pixel values ​​in the acquired infrared images are usually quite regular when there is no wear or deformation. However, when the saw blade edge has gaps, wear, or deformation, the change in the contact area leads to a relatively higher local temperature compared to the normal area, which is reflected in the converted grayscale image as locally bright areas. Therefore, by identifying the temperature of the saw blade area in each infrared image in the infrared image sequence, the degree of abnormally high temperature performance of the saw blade in each infrared image can be determined. Considering that inappropriate hydraulic module pressure can cause material vibration during cutting, leading to additional vibration anomalies, and given that the abnormality of the saw blade intensifies with continuous cutting, it is necessary to further analyze the vibration anomaly assessment value and abnormal high temperature performance based on the growth of these values ​​to obtain a more reliable and optimized saw blade anomaly index. This index is then used to determine whether a saw blade anomaly exists. This invention detects the saw blade surface temperature and vibration during the cutting process, acquiring infrared image sequences and vibration data sequences. Based on these sequences, an accurate saw blade anomaly index can be adaptively determined, effectively improving the accuracy of saw blade anomaly detection in cutting equipment. Attached Figure Description

[0053] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic diagram of the structure of a movable horizontal continuous casting profile cutting device according to an embodiment of the present invention;

[0055] Figure 2 This is a first isometric schematic diagram of the overall structure of the cutting device according to an embodiment of the present invention;

[0056] Figure 3 This is a second isometric schematic diagram of the overall structure of the cutting device according to an embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram showing the state of the third cylinder during cutting using the cutting device according to an embodiment of the present invention;

[0058] Figure 5 This is a schematic diagram of the suction cup structure of the cutting device according to an embodiment of the present invention;

[0059] Figure 6 This is a schematic diagram of the structure of the first motor of the cutting device according to an embodiment of the present invention;

[0060] Figure 7 Embodiments of the present invention Figure 6 Enlarged structural diagram at point A;

[0061] Figure 8 This is a flowchart of the saw blade anomaly detection process according to an embodiment of the present invention;

[0062] Figure 9 This is a flowchart illustrating the steps for determining the vibration anomaly assessment value of each vibration segment according to an embodiment of the present invention.

[0063] Figure 10 This is a flowchart illustrating the steps of determining the abnormal high temperature performance of a saw blade in each infrared image according to an embodiment of the present invention.

[0064] Figure 11 This is a schematic diagram of the saw blade anomaly detection system of a portable horizontal continuous casting profile cutting equipment according to an embodiment of the present invention;

[0065] Wherein: 1 is the workbench; 2 is the slide rail; 3 is the moving frame; 4 is the chain drive structure; 5 is the material conveying support frame; 6 is the first motor; 7 is the screw; 8 is the support rod; 9 is the first cylinder; 10 is the second motor; 11 is the saw blade; 12 is the support arm; 13 is the second cylinder; 14 is the fixing plate; 15 is the third motor; 16 is the sleeve; 17 is the suction cup; 18 is the mounting bolt; 19 is the third cylinder; 20 is the pressure plate; 21 is the infrared sensor; 22 is the vibration sensor. Detailed Implementation

[0066] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0067] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0068] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0069] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0070] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0071] Although operations or steps are described in a specific order in the accompanying drawings in the embodiments of the present invention, this should not be construed as requiring these operations or steps to be performed in the specific order or serial order shown, or requiring all of the shown operations or steps to be performed to obtain the desired result. In the embodiments of the present invention, these operations or steps may be performed serially; they may be performed in parallel; or a portion of these operations or steps may be performed.

[0072] Furthermore, it is understood that the data involved in the technical solutions of this invention (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and all parameters or indicators in the formulas involved in this invention are normalized values ​​that have eliminated the influence of dimensions.

[0073] To address the inaccurate detection of saw blade anomalies in existing cutting equipment, this invention provides a portable horizontal continuous casting profile cutting device. The device includes a cutting body and a detection unit. The detection unit comprises an infrared sensor, a vibration sensor, and a detection processing module. The infrared sensor and vibration sensor are used to detect infrared images of the saw blade surface temperature and vibration data of the saw blade during operation, respectively. The detection processing module communicates with the infrared sensor and vibration sensor to acquire infrared image sequences and vibration data sequences. It analyzes these sequences to assess saw blade deformation and edge wear caused by high temperatures during continuous cutting, determining saw blade anomaly indicators. Ultimately, based on these indicators, it determines whether a saw blade anomaly exists, effectively improving the accuracy of saw blade anomaly detection and ensuring the cutting quality of the profiles.

[0074] The following is a detailed description of a mobile horizontal continuous casting profile cutting device provided by an embodiment of the present invention, with reference to the accompanying drawings.

[0075] Figure 1 This diagram illustrates the structure of a movable horizontal continuous casting profile cutting device according to an embodiment of the present invention. Figure 1 As shown, the cutting device 0 includes a cutting device body 01 and a detection device 02. The detection device 02 includes an infrared sensor 21, a vibration sensor 22, and a detection processing module 05. The detection processing module 05 is connected to the infrared sensor 21 and the vibration sensor 22 via wired or wireless means. In this embodiment of the invention, a wireless communication connection is used.

[0076] Among them, the cutting equipment body 01 refers to the existing mobile horizontal continuous casting profile cutting equipment. The mobile horizontal continuous casting profile cutting equipment provided in this embodiment of the invention adds a detection device 02 to the existing mobile horizontal continuous casting profile cutting equipment to detect abnormalities in the saw blade in the cutting device of the existing mobile horizontal continuous casting profile cutting equipment.

[0077] Figure 2 and Figure 3 The first and second axonometric schematic diagrams of the overall structure of the movable horizontal continuous casting profile cutting equipment provided in the embodiments of the present invention are given respectively. Figures 4-6 The following diagrams illustrate the state of the third cylinder, the structure of the suction cup, and the structure of the first motor in the movable horizontal continuous casting profile cutting equipment provided in this embodiment of the invention. Figure 7 express Figure 6 A magnified schematic diagram of the structure at point A in the middle.

[0078] In combination with the above Figures 2-7For the movable horizontal continuous casting profile cutting equipment provided in this embodiment of the invention, a slide rail 2 and a material conveying support frame 5 are installed on the worktable 1. The continuous casting profile is placed on the material conveying support frame 5, and the cutting equipment is moved by the moving frame 3 and the chain drive structure 4. The positioning is done manually. The first cylinder 9 and the third cylinder 15 are supported by the screw 7 and the support rod 8. The third cylinder 19 is controlled by the first motor 6 to press down the pressure plate 20 to fix the continuous casting profile. The first cylinder 9 is then controlled by the first motor 6 to move horizontally and vertically. The saw blade 11 is controlled by the second motor 10. The working status of the saw blade 11 is obtained by the infrared sensor 21 and the vibration sensor 22 and the wear is determined.

[0079] When the saw blade 11 is determined to need replacement, the first motor 6 controls the third cylinder 19 to move the saw blade 11 to the second cylinder 13. Two suction cups 17 are installed through the support arm 12 and the fixing plate 14. The third motor 15 controls the second cylinder 13 to move the fixing plate forward, and the sleeve 16 is inserted into the mounting bolt 18 of the saw blade 11 to disassemble the saw blade 11. The saw blade is then fixed by the suction cups 17. The second cylinder is then controlled to move backward again to complete the saw blade determination and replacement.

[0080] Since the key feature of the movable horizontal continuous casting profile cutting equipment provided in this embodiment of the invention is the detection equipment 02, the detection equipment 02 will be described in detail below with reference to the accompanying drawings.

[0081] The detection device 02 includes an infrared sensor 21 used to acquire infrared images of the saw blade surface during saw cutting (hereinafter referred to as infrared images), and sends the acquired infrared images to the detection processing module 05. For example... Figure 7 As shown, the infrared sensor 21 is installed directly in front of the saw blade, thereby acquiring a complete infrared image of the saw blade surface while avoiding sparks from cutting. The infrared sensor 21 is set to acquire an infrared image of the saw blade approximately every 0.5 to 1 second. In this embodiment, the acquisition time interval is set to 1 second. It should be understood that the acquisition time interval needs to be determined based on the saw blade's rotation speed, ensuring that the saw blade completes a full number of revolutions per second to ensure that the same part of the saw blade is in the same position in different images. For example, here the cutting speed is set to 60 revolutions per second.

[0082] like Figure 7As shown, the detection device 02 includes a vibration sensor 22 installed at the servo motor connecting the saw blade to the existing portable horizontal continuous casting profile cutting equipment. This sensor collects vibration data generated by the saw blade during cutting; the vibration data refers to amplitude data. The collected vibrations are then sent to the detection and processing module 05. The sampling frequency of the vibration sensor 22 is 10Hz-100Hz, meaning it collects 10-100 vibration data points per second. In this embodiment of the invention, the sampling frequency of the vibration sensor 22 is set to 100Hz.

[0083] The detection device 02 includes a detection processing module 05 that receives infrared images and vibration data collected by infrared sensor 21 and vibration sensor 22. Starting from the moment of cutting, the acquired infrared images are arranged in chronological order to obtain an infrared image sequence, and the acquired vibration data is also arranged in chronological order to obtain a vibration data sequence. It should be understood that both the infrared image sequence and the vibration data sequence are composed of infrared images and vibration data acquired during a single continuous cutting process. The detection processing module 05 analyzes the infrared image sequence and the vibration data sequence to detect saw blade anomalies. When a saw blade anomaly is detected, the current saw blade is replaced with a spare saw blade.

[0084] The following section will introduce the steps for detecting saw blade anomalies, with specific details provided.

[0085] like Figure 8 As shown, the specific steps for detecting saw blade anomalies include:

[0086] Step S100: The vibration data sequence is segmented to obtain each vibration segment. Based on the fluctuation of each vibration segment, the vibration anomaly assessment value of each vibration segment is determined.

[0087] The vibration data sequence is segmented according to the set acquisition time interval for each infrared image in the infrared image sequence to obtain each vibration segment. Since the set acquisition time interval is 1 second in this embodiment of the invention, a vibration segment and a corresponding infrared image can be obtained every 1 second.

[0088] When a saw blade experiences significant wear and deformation, the contact area during cutting becomes less sharp, and the deformation causes uneven stress on the contact area. This results in abnormal vibration signals generated during the cutting process, typically manifested as higher vibration amplitudes and uneven timing of these higher amplitudes. Therefore, by analyzing the fluctuations in each vibration segment, the abnormal vibration values ​​for each segment can be assessed.

[0089] When analyzing the fluctuations of each vibration segment, considering that the saw blade's heating and wear are relatively weakest at the beginning of the saw blade cutting stage, by comparing each subsequent vibration segment with the initial vibration segment, when the average distribution of vibration data in each subsequent vibration segment is higher than that in the initial vibration segment, and the matching degree of the vibration segments is lower, it indicates that the subsequent vibration segments are more likely to have experienced vibration abnormalities. Thus, the vibration abnormality assessment value of each vibration segment can be determined.

[0090] Furthermore, in embodiments of the present invention, such as Figure 9 As shown, the steps to determine the vibration anomaly assessment value for each vibration segment include:

[0091] Step S101: Determine the average vibration value of each vibration segment based on the average distribution of vibration data in each vibration segment;

[0092] Step S102: Determine the reference vibration segment in the vibration segmentation, and determine the vibration deviation between each vibration segment and the reference vibration segment based on the difference in the mean vibration value between each vibration segment and the reference vibration segment;

[0093] Step S103: Determine the extreme value sequence of each vibration segment based on the extreme value distribution of vibration data in each vibration segment;

[0094] Step S104: Match the extreme value sequence of each vibration segment with the extreme value sequence of the benchmark vibration segment. Based on the matching result and the difference between each extreme value in the extreme value sequence of each vibration segment and the vibration mean of the benchmark vibration segment, determine the matching similarity index between each vibration segment and the benchmark vibration segment.

[0095] Step S105: Integrate the vibration deviation and matching similarity index to determine the vibration anomaly assessment value for each vibration segment.

[0096] For the above steps, the mean value of vibration is calculated based on all vibration data in each vibration segment. The first vibration segment in the entire vibration segment is taken as the reference vibration segment, and the mean value of the reference vibration segment is taken as the reference vibration mean. At this time, the heat generation and wear of the saw blade are relatively weakest.

[0097] For any vibration segment after the first vibration segment, taking the nth vibration segment as an example, when n=1, it refers to the second vibration segment. The difference between the mean vibration value of the nth vibration segment and the mean vibration value of the reference vibration segment (i.e., the reference vibration mean) is calculated, and this difference is taken as the vibration deviation between the nth vibration segment and the reference vibration segment. Of course, for the first vibration segment, its corresponding vibration deviation is 0.

[0098] During the operation of the saw blade, each vibration segment represents the vibration of a complete saw blade after multiple rotations. Therefore, if wear, chipping, or even deformation occurs at a certain local position of the saw blade, the corresponding fixed position will be reflected in the vibration data of multiple vibration segments at the same time under uniform rotation. Based on this analysis, it is necessary to match the vibration data of each vibration segment with the reference vibration segment to determine whether the abnormal vibration corresponding to each extreme point is actual wear or defect.

[0099] Therefore, by determining the maximum and minimum points corresponding to each vibration segment, and constructing a maximum and minimum value sequence in chronological order, both the maximum and minimum value sequences can be identified as extreme value sequences. Thus, two extreme value sequences can be determined for all vibration segments (including the first vibration segment).

[0100] For any vibration segment after the first vibration segment, taking the nth vibration segment as an example and the maximum value sequence as a target extreme value sequence, calculate the absolute value of the difference between each extreme value (here referring to the maximum value) in the target extreme value sequence of the nth vibration segment and the vibration mean of the benchmark vibration segment, i.e. the benchmark vibration mean. Then, use the norm function to linearly normalize the absolute value of the difference and use the normalized value as the matching weight of the extreme value point.

[0101] Then, DTW matching is performed between the target extreme value sequence of the nth vibration segment and the target extreme value sequence of the reference vibration segment. After DTW matching, each extreme value matching pair is obtained. Each extreme value matching pair consists of two extreme points located in the target extreme value sequences of the nth vibration segment and the reference vibration segment, respectively. The matching distance corresponding to each extreme value matching pair is determined. This matching distance refers to the absolute value of the difference between the two extreme points in each extreme value matching pair. Since each extreme point in the target extreme value sequence of the nth vibration segment has a corresponding matching weight, each extreme value matching pair also has a corresponding matching weight. The matching weight is used as the weight of the matching distance of each extreme value matching pair. The matching distances of all extreme value matching pairs are weighted and accumulated, and the weighted accumulated value is negatively correlated and mapped to obtain the sub-matching similarity index corresponding to the maximum value sequence between the nth vibration segment and the reference vibration segment. In this embodiment of the invention, the reciprocal of the sum of the weighted cumulative value and the set adjustment parameter is taken to perform negative correlation mapping on the weighted cumulative value, and the resulting reciprocal is the sub-matching similarity index. The set adjustment parameter is set to prevent the denominator from being 0; in this embodiment, the value of the set adjustment parameter can be set to 0.01.

[0102] In the process of determining the sub-matching similarity index corresponding to the maximum value sequence between the nth vibration segment and the reference vibration segment, the matching weight of each extreme value (here referring to the maximum value) in the target extreme value sequence of the nth vibration segment and the vibration mean of the reference vibration segment (i.e., the reference vibration mean) is obtained by linearly normalizing the absolute value of the difference. The larger the matching weight, the greater the difference between the extreme value and the reference vibration mean, and the more likely the extreme value is to exhibit abnormal vibration. Therefore, the matching distance corresponding to it is amplified, i.e., the reference weight is larger, thus obtaining a more accurate sub-matching similarity index. At the same time, since the saw blade rotation behavior is exactly the same in each vibration segment under normal circumstances, after DTW matching, it can be determined that the local position of the saw blade corresponding to the cutting part causing the current vibration is the same. Therefore, the smaller the sub-matching similarity index between the nth vibration segment and the reference vibration segment, the greater the probability of new wear or deformation in the nth vibration segment compared to the reference vibration segment.

[0103] Following the same method used above to determine the sub-matching similarity index corresponding to the maximum value sequence between the nth vibration segment and the reference vibration segment, taking the minimum value sequence as a target extreme value sequence as an example, the sub-matching similarity index corresponding to the minimum value sequence between the nth vibration segment and the reference vibration segment can be determined. The average value of the sub-matching similarity index corresponding to the maximum value sequence between the nth vibration segment and the reference vibration segment, and the average value of the sub-matching similarity index corresponding to the minimum value sequence between the nth vibration segment and the reference vibration segment, is then used as the matching similarity index between the nth vibration segment and the reference vibration segment.

[0104] The similarity index between the nth vibration segment and the benchmark vibration segment is used as a weight, and the vibration deviation between the nth vibration segment and the benchmark vibration segment is used as a base value and multiplied by this weight to obtain the vibration anomaly assessment value of the nth vibration segment. The larger this vibration anomaly assessment value, the more unstable the vibration of the saw blade in the nth vibration segment is considered to be compared with the first vibration segment, and the same local position of the saw blade will cause different vibration behaviors when cutting.

[0105] It should be understood that for the first vibration segment, since it is itself the reference vibration segment, its corresponding vibration deviation is 0, therefore the vibration anomaly assessment value of the first vibration segment is 0.

[0106] Step S200: Perform temperature identification on each infrared image in the infrared image sequence to determine the degree of abnormal high temperature performance of the saw blade in each infrared image.

[0107] During normal cutting, the temperature of a saw blade rises significantly at its edges, gradually decreasing towards the center, and the temperature is relatively uniform at the edges. Therefore, the pixel values ​​in the acquired infrared images are usually quite regular when there is no wear or deformation. However, when the saw blade edge has notches, wear, or deformation, the change in the contact area leads to a relatively higher local temperature compared to the normal area, which is reflected in the converted grayscale image as locally bright areas. Therefore, by identifying the temperature of the saw blade area in each infrared image in the infrared image sequence, the degree of abnormally high temperature performance of the saw blade in each infrared image can be determined.

[0108] Furthermore, in embodiments of the present invention, such as Figure 10 As shown, the abnormal high temperature performance of the saw blade in each infrared image is determined by the following steps:

[0109] Step S201: Segment the saw blade region in each infrared image in the infrared image sequence to obtain the saw blade region;

[0110] Step S202: Divide the saw blade area to identify each abnormally high temperature area;

[0111] Step S203: Determine the reference infrared image among all infrared images in the infrared image sequence, and determine the degree of abnormal high temperature performance of the saw blade in each infrared image by combining the uniformity of grayscale distribution of the saw blade area in each infrared image in the infrared image sequence, the average distribution of grayscale values ​​in each abnormal high temperature area of ​​the saw blade area, the area size of each abnormal high temperature area, and the difference in the average grayscale values ​​between the saw blade area of ​​each infrared image in the infrared image sequence and the saw blade area of ​​the reference infrared image.

[0112] For the above steps, for each infrared image in the infrared image sequence, the infrared image is first converted into a grayscale image. Preliminary image segmentation is then performed on the converted grayscale infrared image to segment the saw blade region. To determine whether there is localized overheating at the saw blade edge due to deformation, wear, or other issues, further analysis of the saw blade region is necessary. During cutting, the saw blade's temperature primarily increases significantly at the edges before being transferred to the interior of the saw blade, which is not in direct contact with the material. Therefore, the temperature in the acquired infrared images typically decreases from the edge to the center. Localized overheating caused by deformation, wear, and other issues can be confused with the temperature of the edge itself. Therefore, based on this situation, it is necessary to analyze the temperature characteristics of the saw blade edge.

[0113] Therefore, for the saw blade region of each infrared image in the infrared image sequence, the Otsu thresholding method is used for segmentation to obtain the high grayscale regions in the current saw blade region. The high grayscale regions are then segmented again using the Otsu thresholding method to obtain individual high grayscale pixels. These high grayscale pixels are designated as abnormal high-temperature pixels, and connected component determination is performed on these abnormal high-temperature pixels to obtain each connected component. These connected components are the individual abnormal high-temperature regions. When determining the connected components of abnormal high-temperature pixels, it is checked whether other abnormal high-temperature pixels exist in their eight neighboring regions. If so, each abnormal high-temperature pixel and the other abnormal high-temperature pixels in their eight neighboring regions are merged into the same connected component, thus obtaining each connected component. In the process of obtaining each abnormal high temperature region, the Otsu thresholding method considers the entire image and obtains the best segmentation threshold in the entire image. However, the difference between the high temperature region and the abnormal high temperature region is usually not large enough compared to the center of the saw blade. That is, the abnormal high temperature region is not so obvious in the overall saw blade region image through statistical means. Therefore, it is necessary to first use the Otsu thresholding method to segment the saw blade region to obtain the high grayscale region. Since the abnormal high temperature region will be relatively obvious in the high temperature region, the Otsu thresholding method is then used to segment the high grayscale region to finally obtain the abnormal high temperature region.

[0114] Following the above method, each abnormally high-temperature region in the saw blade area of ​​all infrared images in the infrared image sequence can be identified. Based on the actual situation corresponding to severe deformation and wear, this region is usually not very small. Therefore, a filtering threshold is set to remove abnormally high-temperature regions with fewer than the set threshold, thus obtaining the final abnormally high-temperature regions. The filtering threshold can be reasonably set according to the actual situation; in this embodiment, the value of the filtering threshold is set to 10.

[0115] The first infrared image in the infrared image sequence is taken as the reference infrared image, corresponding to the image where the saw blade's heating and wear are relatively weakest. For any infrared image after the first one, taking the nth image as an example (where n=1, it refers to the second infrared image), the average distribution of gray values ​​in the saw blade area of ​​the nth infrared image is compared with that of the reference infrared image. Only when the average distribution of gray values ​​in the nth infrared image is at least greater than that in the reference infrared image can a higher probability of an abnormal temperature be considered, indicating that this abnormal high temperature is caused by wear, deformation, or other abnormalities, with higher temperatures increasing the likelihood of this. Furthermore, the better the uniformity of the gray value distribution in the saw blade area of ​​the nth infrared image (i.e., the worse the gray value concentration), the more likely the saw blade temperature exhibits multiple different amplitudes with significant variations, suggesting that its temperature performance compared to a normal saw blade may show localized high temperatures caused by defects, deformation, or other factors. Furthermore, the higher the average distribution of grayscale values ​​in each abnormally high temperature region of the saw blade area in the nth infrared image, and the larger the area of ​​each abnormally high temperature region, the more likely it is that the current high temperature region still exhibits a large local high temperature, and this local high temperature is more likely to be caused by a local anomaly.

[0116] Based on the above analysis, by comprehensively considering the uniformity of grayscale distribution in the saw blade area of ​​each infrared image in the infrared image sequence, the average distribution of grayscale values ​​in each abnormally high temperature region of the saw blade area, the size of each abnormally high temperature region, and the difference in the average grayscale values ​​between the saw blade area of ​​each infrared image in the infrared image sequence and the saw blade area of ​​the reference infrared image, the degree of abnormal high temperature performance of the saw blade in each infrared image can be determined.

[0117] In this embodiment of the invention, for each infrared image in the infrared image sequence, the average grayscale value of the saw blade region of that infrared image is determined to obtain the grayscale mean. The grayscale mean corresponding to the reference infrared image is used as the reference grayscale mean. Similarly, taking the nth infrared image as an example, the grayscale difference index corresponding to the nth infrared image is determined based on the difference between the grayscale mean corresponding to the nth infrared image and the grayscale mean corresponding to the reference infrared image. The number of pixels with the same grayscale level in the saw blade region of the nth infrared image is determined, and the information entropy is determined, which is called the grayscale information entropy. Simultaneously, the average grayscale value of each abnormally high temperature region in the saw blade region of the nth infrared image is determined to obtain the average grayscale value, and the number of pixels in each abnormally high temperature region is normalized to obtain the normalized region area. In this embodiment of the invention, the normalization function is used to normalize the number of pixels in each abnormally high temperature region, thereby obtaining the normalized region area.

[0118] Finally, based on the grayscale difference index and grayscale information entropy corresponding to the nth infrared image, as well as the normalized region area and average grayscale value corresponding to each abnormally high temperature region in the saw blade region, the abnormal high temperature performance of the saw blade in each infrared image can be determined.

[0119] In this embodiment of the invention, the abnormal high temperature performance of the saw blade in each infrared image is determined by the following calculation formula:

[0120]

[0121] Among them, T n This indicates the degree of abnormal high temperature performance of the saw blade in the nth infrared image; a n w represents the grayscale difference index corresponding to the nth infrared image. n w represents the mean gray level of the nth infrared image; w0 represents the mean gray level of the reference infrared image; b n t represents the grayscale information entropy corresponding to the nth infrared image; nm This represents the average grayscale value corresponding to the m-th abnormally high temperature region in the saw blade region of the n-th infrared image; s ′ nm Let M represent the normalized region area corresponding to the m-th abnormally high temperature region in the saw blade region of the n-th infrared image; M represents the total number of abnormally high temperature regions in the saw blade region of the n-th infrared image.

[0122] In the above calculation formula, the grayscale difference index a n This reflects the basic temperature characteristics of the nth infrared image. When the basic temperature characteristics are large, if the grayscale information entropy b... n The larger the value of , the more likely the saw blade has multiple different temperature amplitudes, and therefore, compared to a normal saw blade, its temperature performance may exhibit localized high temperatures caused by defects, deformation, etc. The larger the average gray value and normalized area corresponding to each abnormally high temperature region in the saw blade region of the nth infrared image, the more likely the saw blade region, despite having a relatively large base temperature, still exhibits significant localized high temperatures, which is more likely caused by local anomalies. Therefore, the products of the average gray value and normalized area corresponding to each abnormally high temperature region are summed to obtain the abnormal temperature performance value. The gray-scale difference index and gray-scale information entropy are considered as the sensitivity of the saw blade region in the nth infrared image to potential anomalies based on this abnormal temperature performance value. This ultimately yields the abnormal high temperature performance degree of the saw blade in the nth infrared image. The larger the value of this abnormal high temperature performance degree, the greater the likelihood of abnormal high temperature performance of the current saw blade based on the infrared sensor 21.

[0123] Step S300: Based on the growth and change of the vibration anomaly assessment value and the growth and change of the abnormal high temperature performance, and combined with the magnitude of the vibration anomaly assessment value and the abnormal high temperature performance close to the current moment, determine the saw blade anomaly index.

[0124] Considering that inappropriate pressure in the hydraulic module can also cause vibration of the cutting material during the cutting process, leading to additional vibration anomalies, and that when the saw blade has a real anomaly, its abnormal performance will become stronger with continuous cutting, in order to eliminate the interference of vibration anomalies caused by inappropriate pressure in the hydraulic module, it is necessary to further analyze the vibration anomaly assessment value and the abnormal high temperature performance based on the growth and changes in the vibration anomaly assessment value and the abnormal high temperature performance, so as to obtain a more reliable optimized saw blade anomaly index.

[0125] In this embodiment of the invention, the steps for determining abnormal saw blade indicators include:

[0126] Based on the growth and change of the vibration anomaly assessment value and the growth and change of the abnormal high temperature performance, the abnormal vibration growth value and the abnormal high temperature growth value are determined.

[0127] Based on the abnormal vibration growth value and abnormal high temperature growth value, combined with the vibration anomaly assessment value of the last vibration segment and the abnormal high temperature performance of the last infrared image in the infrared image sequence, the saw blade anomaly index is determined.

[0128] For the above steps, in all vibration segments, the difference between the abnormal vibration growth value of the nth vibration segment and the abnormal vibration growth value of the preceding (n-1)th vibration segment is calculated. The average of all differences is then normalized using the norm function to obtain the abnormal vibration growth value. Similarly, in the infrared image sequence, the difference between the abnormal high temperature performance corresponding to the nth infrared image and the abnormal high temperature performance corresponding to the preceding (n-1)th infrared image is calculated. The average of all differences is then normalized using the norm function to obtain the abnormal high temperature growth value. Then, the norm function is used to linearly normalize the vibration anomaly assessment value of the last vibration segment and the abnormal high temperature performance value of the last infrared image in the infrared image sequence, respectively, thus obtaining the normalized vibration anomaly assessment value and the normalized abnormal high temperature performance value. Furthermore, based on the abnormal vibration growth value and the normalized value of the vibration anomaly assessment value, the first anomaly index is determined, and based on the abnormal high temperature growth value and the normalized value of the abnormal high temperature performance, the second anomaly index is determined; combining the first and second anomaly indices, the saw blade anomaly index is determined.

[0129] In this embodiment of the invention, the abnormal indicators of the saw blade are determined, and the corresponding calculation formula is as follows:

[0130] F=softmax(Z′*kz+T′*kt);

[0131] Where F represents the saw blade abnormality index; softmax represents the normalization function; Z′*kz represents the first abnormality index, Z′ represents the normalized value of the vibration abnormality assessment value; kz represents the abnormal vibration growth value; T′*kt represents the second abnormality index, T′ represents the normalized value of the abnormal high temperature performance, and kt represents the abnormal high temperature growth value.

[0132] In the above calculation formula, the larger the normalized value of the vibration anomaly assessment value and the normalized value of the abnormal high temperature performance corresponding to the last vibration segment and the last infrared image close to the current moment, and the higher the degree of increase of the vibration anomaly assessment value corresponding to all vibration segments and the abnormal high temperature performance corresponding to all infrared images, the higher the abnormal performance of the current saw blade is, and it has a higher probability of having abnormal problems such as severe wear and deformation.

[0133] Step S400: Determine whether there is a saw blade abnormality based on the saw blade abnormality index.

[0134] A pre-set threshold for saw blade abnormality indicators is established. This threshold is set appropriately as needed; in this embodiment, the threshold value is set to 0.7. The calculated saw blade abnormality indicator is compared with this threshold. If the saw blade abnormality indicator is less than or equal to the threshold, it indicates that the current saw blade is not abnormal. If the saw blade abnormality indicator is greater than the threshold, it indicates that the current saw blade is highly likely to have severe wear, deformation, or other abnormal problems. In this case, the saw blade is determined to be abnormal, and the detection and processing module 05 sends a saw blade replacement signal to the backup saw blade replacement module. Upon receiving the saw blade replacement signal, the backup saw blade replacement module controls the replacement of the current saw blade with the backup saw blade. After replacing the current saw blade with the spare saw blade and continuing the cutting operation, perform a saw blade anomaly detection on the spare saw blade in the same way as the current saw blade anomaly detection described above. If the spare saw blade also malfunctions during the operation, stop the equipment and have a professional replace the saw blade; otherwise, perform a complete equipment overhaul and other operations after the current batch of materials has been cut.

[0135] Based on the same inventive concept, this invention also provides a method for detecting saw blade anomalies in a mobile horizontal continuous casting profile cutting device, the method comprising:

[0136] Infrared image sequences and vibration data sequences are acquired. The infrared images in the infrared image sequence are obtained by detecting the surface temperature of the saw blade, and the vibration data in the vibration data sequence are obtained by detecting the vibration of the saw blade.

[0137] The vibration data sequence is segmented to obtain each vibration segment. Based on the fluctuation of each vibration segment, the vibration anomaly assessment value of each vibration segment is determined.

[0138] Temperature identification is performed on each infrared image in the infrared image sequence to determine the degree of abnormal high temperature performance of the saw blade in each infrared image;

[0139] Based on the growth and change of the vibration anomaly assessment value and the growth and change of the abnormal high temperature performance, and combined with the magnitude of the vibration anomaly assessment value and the abnormal high temperature performance close to the current moment, the saw blade anomaly index is determined.

[0140] Based on the aforementioned saw blade abnormality indicators, determine whether a saw blade abnormality exists.

[0141] Based on the same inventive concept, embodiments of the present invention also provide a saw blade anomaly detection system for a portable horizontal continuous casting profile cutting equipment, such as... Figure 11 As shown, the system includes: a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and running on the processor 502, wherein when the processor 502 executes the computer program 503, the system can perform the steps of the saw blade abnormality detection process described above.

[0142] In this embodiment of the invention, the system can be divided into functional modules based on the above-described saw blade anomaly detection process method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0143] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to perform the steps of the saw blade abnormality detection process described above.

[0144] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing computer program code, which, when executed on a computer, causes the computer to perform the steps of the aforementioned saw blade anomaly detection process.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention 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 scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A mobile horizontal continuous casting profile cutting device, characterized in that, The cutting equipment includes a cutting equipment body and a detection device. The detection device includes an infrared sensor for detecting the surface temperature of the saw blade, a vibration sensor for detecting the vibration of the saw blade, and a detection processing module. The detection processing module is communicatively connected to the infrared sensor and the vibration sensor to acquire infrared image sequences and vibration data sequences, and performs saw blade anomaly detection based on the infrared image sequences and vibration data sequences. The saw blade anomaly detection process includes: The vibration data sequence is segmented to obtain each vibration segment. Based on the fluctuation of each vibration segment, the vibration anomaly assessment value of each vibration segment is determined. Temperature identification is performed on each infrared image in the infrared image sequence to determine the degree of abnormal high temperature performance of the saw blade in each infrared image; Based on the growth and changes in the vibration anomaly assessment value and the abnormal high temperature performance, and combined with the magnitude of the vibration anomaly assessment value and abnormal high temperature performance closest to the current moment, the saw blade anomaly index is determined; specifically, this includes: determining the abnormal vibration growth value and the abnormal high temperature growth value based on the growth and changes in the vibration anomaly assessment value and the abnormal high temperature performance; determining the first anomaly index based on the vibration anomaly assessment value and the abnormal vibration growth value of the last vibration segment; determining the second anomaly index based on the abnormal high temperature performance and the abnormal high temperature growth value of the last infrared image in the infrared image sequence; and combining the first and second anomaly indices to determine the saw blade anomaly index. Based on the aforementioned saw blade abnormality indicators, determine whether there is a saw blade abnormality. If a saw blade abnormality is found, replace the current saw blade with a spare saw blade.

2. The movable horizontal continuous casting profile cutting equipment according to claim 1, characterized in that, Determine the vibration anomaly assessment values ​​for each vibration segment, including: The average vibration value of each vibration segment is determined based on the average distribution of vibration data in each vibration segment. A reference vibration segment is determined in the vibration segmentation, and the vibration deviation between each vibration segment and the reference vibration segment is determined based on the difference in the mean vibration value between each vibration segment and the reference vibration segment. Based on the extreme value distribution of vibration data in each vibration segment, determine the extreme value sequence of each vibration segment; The extreme value sequence of each vibration segment is matched with the extreme value sequence of the benchmark vibration segment. Based on the matching results and the difference between each extreme value in the extreme value sequence of each vibration segment and the vibration mean of the benchmark vibration segment, the matching similarity index between each vibration segment and the benchmark vibration segment is determined. By integrating the vibration deviation and matching similarity index, a vibration anomaly assessment value is determined for each vibration segment.

3. The movable horizontal continuous casting profile cutting equipment according to claim 2, characterized in that, The extreme value sequence includes a maximum value sequence and a minimum value sequence. A similarity index is determined for each vibration segment and the benchmark vibration segment, including: Both the maximum and minimum value sequences are treated as a single target extreme value sequence. The target extreme value sequence of each vibration segment is matched with the target extreme value sequence corresponding to the benchmark vibration segment to obtain each extreme value matching pair; The sub-matching similarity index is determined by combining the matching distance between extreme points in each extreme matching pair and the difference between each extreme value in the target extreme value sequence of each vibration segment and the vibration mean of the benchmark vibration segment. By combining the sub-matching similarity index corresponding to the maximum value sequence between each vibration segment and the reference vibration segment, and the sub-matching similarity index corresponding to the minimum value sequence between each vibration segment and the reference vibration segment, a matching similarity index between each vibration segment and the reference vibration segment is determined.

4. The mobile horizontal continuous casting profile cutting equipment according to claim 2, characterized in that, The first vibration segment among all the vibration segments is determined as the reference vibration segment.

5. A mobile horizontal continuous casting profile cutting device according to claim 1, characterized in that, Determine the degree of abnormal high temperature performance of the saw blade in each infrared image, including: The saw blade region is segmented for each infrared image in the infrared image sequence to obtain the saw blade region; The saw blade area is divided to identify each abnormally high temperature zone; A reference infrared image is identified among all infrared images in the infrared image sequence. The uniformity of grayscale distribution in the saw blade area of ​​each infrared image in the infrared image sequence, the average distribution of grayscale values ​​in each abnormally high temperature area of ​​the saw blade area, the size of each abnormally high temperature area, and the difference in the average distribution of grayscale values ​​between the saw blade area of ​​each infrared image in the infrared image sequence and the saw blade area of ​​the reference infrared image are considered to determine the abnormal high temperature performance of the saw blade in each infrared image.

6. The mobile horizontal continuous casting profile cutting equipment according to claim 5, characterized in that, Determine the degree of abnormal high temperature performance of the saw blade in each infrared image, including: The average gray value of the saw blade region in each infrared image in the infrared image sequence is determined based on the average distribution of gray values ​​in the saw blade region of each infrared image in the infrared image sequence. Based on the difference in the mean grayscale value between the saw blade area of ​​each infrared image in the infrared image sequence and the saw blade area of ​​the reference infrared image, the grayscale difference index corresponding to each infrared image in the infrared image sequence is determined. The grayscale information entropy corresponding to each infrared image in the infrared image sequence is determined based on the number of pixels with the same grayscale level in the saw blade region of each infrared image in the infrared image sequence. By combining the average distribution of grayscale values ​​in each abnormally high temperature region within the saw blade region of each infrared image in the infrared image sequence, as well as the size of each abnormally high temperature region, the abnormal temperature performance value corresponding to each infrared image in the infrared image sequence is determined. By combining the grayscale difference index, grayscale information entropy, and abnormal temperature performance value corresponding to each infrared image in the infrared image sequence, the abnormal high temperature performance of the saw blade in each infrared image is determined.

7. The mobile horizontal continuous casting profile cutting equipment according to claim 1, characterized in that, Determine the abnormal vibration growth value and the abnormal high temperature growth value, including: Determine the difference between the vibration anomaly assessment value of each subsequent vibration segment and the preceding vibration segment in all the vibration segments, and use the normalized result of the mean of all differences as the abnormal vibration growth value. The difference in abnormal high temperature performance between each subsequent infrared image and its preceding infrared image in the infrared image sequence is determined, and the normalized result of the mean of all differences is taken as the abnormal high temperature growth value.

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