Intelligent deviation correction method and system for strip cutting

Abstract

The present application relates to the field of data processing, and particularly relates to a kind of intelligent deviation rectification method and system for strip cutting, first by real-time acquisition and smooth processing deviation profile data of strip;Then, by calculating the local variation energy of the second derivative of deviation profile, the region where the morphology changes significantly is identified, and its characteristic scale is further calculated;Then, according to the characteristic scale of macro deviation, an adaptive analysis window is dynamically generated;Finally, in the window, using low-order polynomial regression, the complex deviation form is accurately decoupled into basic parameters such as translation, tilt, C-shaped bending and S-shaped bending, and is transmitted to the controller to realize fast and accurate differentiated deviation rectification.The present application identifies macro deviation by calculating local variation energy and characteristic scale, constructs adaptive analysis window and decouples deviation form using local polynomial regression, to realize accurate control.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and in particular to an intelligent deviation correction method and system for strip cutting. BACKGROUND

[0002] In the fields of steel and automobile manufacturing, continuous production and processing of strip steel is a core link. During the process of transporting the strip steel to the cutting station, due to the influence of various factors such as upstream process, mechanical vibration or material internal stress, the strip steel often deviates in the transverse position, i.e. the so-called "run-off" phenomenon. This run-off pattern is complex and may include overall translation, inclination, C-shaped bending or S-shaped bending and other components. If these deviations cannot be accurately identified and corrected in time, it will seriously affect the cutting accuracy, leading to a decline in product quality and material waste. Therefore, it is crucial to monitor and intelligently correct the run-off pattern of the strip steel in real time and accurately.

[0003] Currently, the industry often uses frequency domain analysis methods such as Fourier transform (FFT) to process data collected by sensors. This method can identify the periodic components in the deviation data by performing spectral analysis on the entire observation window. However, in the specific context of intelligent deviation correction of strip steel, the traditional FFT method has obvious defects. First, macroscopic deviations of the strip steel, such as large C-shaped or S-shaped bending, are usually non-periodic and localized events. FFT, as a global analysis tool, is difficult to accurately locate the specific position of these deviations and lacks spatial resolution. Second, in order to accurately analyze long waveform deviations, FFT requires a long sampling window, which will introduce a large calculation delay, which contradicts the real-time requirements of the cutting station. Finally, the FFT analysis results are frequency domain components, which are not intuitive in physical meaning and are difficult to directly decouple into specific pattern parameters such as translation, inclination and bending required by the controller, making it difficult for subsequent accurate deviation correction control. SUMMARY

[0004] To address the contradiction between the significant computational delay and the real-time control requirements of the cutting station, this invention proposes an intelligent deviation correction method for strip cutting in a first aspect. The method includes: acquiring the real-time deviation profile of the strip along its length upstream of the cutting station; calculating the local variation energy of the deviation profile, wherein the local variation energy is positively correlated with the variance of the second derivative of the deviation profile within a local window of a set length; locating the energy peaks in the local variation energy curve along the length direction and calculating the characteristic scale of each energy peak; wherein the characteristic scale is positively correlated with the width of the corresponding energy peak at a set threshold height; filtering out macroscopic deviations whose characteristic scales are greater than a preset scale threshold; constructing an adaptive analysis window centered on the peak position and with a width proportional to the characteristic scale for each macroscopic deviation; performing polynomial fitting on the deviation profile within the adaptive analysis window to obtain fitting coefficients; and transmitting the fitting coefficients to a deviation correction controller to execute a deviation correction action.

[0005] Compared to existing technologies that typically employ fixed windows or simple feedback control based solely on deviation values, this invention, by calculating the local variation energy related to the second derivative of the deviation profile, can more sensitively identify complex shape defects such as S-shaped bends that are difficult to detect using traditional methods. Furthermore, by screening macroscopic deviations with large feature scales and constructing an adaptive analysis window with matching dimensions, the focus of analysis and correction can be precisely concentrated on the truly large and complex deviations that need to be addressed, avoiding ineffective or excessive responses to minute, high-frequency noise. This adaptive analysis method ensures the accuracy of subsequent polynomial fitting, thereby generating more precise correction control parameters and significantly improving the ability to identify and correct complex strip shape defects.

[0006] Furthermore, obtaining the real-time deviation profile includes: deploying multiple displacement sensors upstream of the cutting station to collect lateral deviation data points of the strip edge; and performing digital filtering on the lateral deviation data points to obtain a smooth and continuous deviation profile function. .

[0007] Furthermore, the local variation energy The specific calculation method is as follows:

[0008] ;

[0009] in This represents the deviation profile function; Indicates the location The second derivative at that point; This indicates the length of the local window; Display window The arithmetic mean of the inner second derivatives.

[0010] The application provides an effective mathematical method for quantifying the local bending severity of the strip steel by defining the local variation energy as the variance of the second derivative of the deviation profile within a local window. Compared with the traditional method using only the deviation value or the first derivative, the variance of the second derivative is more sensitive to the curvature change of C-bend and S-bend.

[0011] Further, the characteristic scale is calculated as follows:

[0012] ;

[0013] wherein represents the position of the energy peak; represents the local variation energy at the position ; represents the Heaviside step function; represents the threshold coefficient with a value range of 0.2 to 0.8.

[0014] By integrating the energy curve exceeding a certain energy threshold, the method can quantitatively evaluate the influence range of each deviation region along the length direction of the strip steel. Unlike the existing technology which simply treats all deviations equally, the calculation of the characteristic scale enables the system to distinguish between long and mild bending and short and sharp bending, providing key information for screening macroscopic deviations that need to be focused on.

[0015] Further, the adaptive analysis window has a range of , wherein is the peak position of the macroscopic deviation, and the window half-width is calculated as follows: ; wherein represents a proportional coefficient greater than 1; represents the characteristic scale associated with the peak position.

[0016] By making the half-width of the analysis window proportional to the characteristic scale of the identified deviation, the intelligent and adaptive adjustment of the analysis region is realized. Compared with the conventional practice of using a fixed-size window for analysis in the existing technology, the adaptive window of the application can ensure that the analysis range completely covers the entire macroscopic deviation region, greatly improving the accuracy and relevance of subsequent polynomial fitting.

[0017] Further, the proportional coefficient has a value range of 1.2 to 2.0.

[0018] Further, the polynomial fitting is a third-order polynomial regression using the least squares method, specifically as follows:

[0019] ;

[0020] wherein represents the fitted deviation profile; is the peak position of the macro deviation; is the fitting coefficient.

[0021] Compared with the first-order model which can only describe the translation and tilt, or the second-order model which can only describe the C-shaped bending, the third-order polynomial can accurately describe the translation, tilt, C-shaped bending and S-shaped bending and other typical shape defects of the strip steel at the same time. This makes the mathematical model of the present application highly matched with the physical deformation characteristics of the strip steel, and can more comprehensively and accurately capture the complete form of the macro deviation, thereby laying a foundation for generating comprehensive correction instructions.

[0022] Further, the zero-order, first-order, second-order and third-order coefficients in the equation obtained by the third-order polynomial regression correspond to the translation amount, tilt degree, C-shaped bending degree and S-shaped bending degree of the strip steel respectively.

[0023] Further, after receiving the fitting coefficient, the correction controller drives the correction roller actuator to perform compensation action to correct the macro deviation.

[0024] In a second aspect, the present application provides an intelligent correction system for strip steel cutting, comprising a processor and a memory, wherein the memory stores computer program instructions which, when executed by the processor, implement an intelligent correction method for strip steel cutting according to the present application.

[0025] The technical effects of the present application are as follows:

[0026] The present application firstly effectively identifies complex shape deviations such as S-shaped bending by calculating the variance of the second derivative of the deviation profile; secondly, it uses an adaptive analysis window matched with the characteristic scale of the deviation for local data analysis, overcoming the limitations of traditional fixed window analysis; finally, it uses a third-order polynomial to accurately fit the selected macro deviation, and directly corresponds the fitting coefficient to the physical deformations such as translation, tilt, C-shaped bending and S-shaped bending. This method can accurately diagnose and quantify complex deviations, and realize intelligent and high-precision correction of shape defects of the strip steel. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 Fig. 1 is a flow chart of an intelligent correction method for strip steel cutting according to an embodiment of the present application;

[0028] Figure 2 Fig. 2 is an analysis curve diagram of the local variation energy calculation process according to an embodiment of the present application;

[0029] Figure 3is a schematic diagram illustrating energy peak detection and screening in the embodiment of the present application;

[0030] Figure 4 is a curve diagram illustrating local shape decoupling fitting results in the embodiment of the present application;

[0031] Figure 5 is a structure block diagram of an intelligent deviation correction system for strip cutting. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0033] The specific embodiments of the present application will be described in detail below with reference to the drawings.

[0034] An intelligent deviation correction method for strip cutting

[0035] As shown in Figure 1 , the intelligent deviation correction method for strip cutting of the present application comprises:

[0036] S1, real-time acquisition of strip multi-point transverse deviation, and data smoothing and noise suppression preprocessing by using a filtering algorithm.

[0037] In the embodiment, first, a plurality of non-contact displacement measurement sensors are deployed at equal intervals or non-equal intervals along the running direction of the strip upstream of the cutting station of the strip conveying line. Exemplarily, a laser displacement sensor or a line array camera can be used. These sensors are used to collect the transverse deviation amount of the single-sided or double-sided edge of the strip at a plurality of preset monitoring points in real time . Among them , is the total number of sensors. In this way, a series of discrete data point sets describing the current position shape of the strip can be obtained .

[0038] It can be understood that the originally collected data points can contain measurement noise introduced by factors such as electrical noise, mechanical vibration or environmental light change. In order to improve the accuracy and robustness of subsequent analysis, the discrete data point set needs to be pre-processed. Specifically, a digital filtering algorithm can be used to smooth the data. As a preferred scheme, the embodiment adopts a Gaussian filter or a median filter. The Gaussian filter effectively suppresses Gaussian white noise through weighted average, while the median filter can effectively remove impulse noise while retaining edge information. After filtering, a smooth and continuous real-time deviation profile function is obtained within a limited and continuously rolling observation window , which provides a high-quality data basis for subsequent analysis.

[0039] S2, calculate the energy variance of the second derivative of the deviation profile in the sliding window, and construct a local variation energy curve to quantify the degree of change of the strip shape in space.

[0040] In step , a smooth deviation profile function is obtained . After this step, the purpose is to identify the ROI area where the shape in the deviation profile changes significantly, and ignore the part where the shape changes relatively flat or uniformly. For this purpose, the embodiment introduces a local variation energy index to quantify the local shape complexity or the degree of curvature of the deviation profile at each position point.

[0041] The construction is based on the second derivative of the deviation profile function , which can intuitively represent the local curvature. When the shape of the strip changes, for example, from a straight line to a curve, or at the inflection point of a type bend, its curvature will change dramatically. Therefore, by calculating the energy of the second derivative in a local sliding window, the abnormality of the strip shape in this area can be stably measured, which is:

[0042]

[0043] where represents the local variation energy at position ; is the deviation profile function obtained after preprocessing in step ; represents the second derivative of the deviation profile at position , which corresponds to the local curvature of the point in a physical sense; is a preset, small sliding integral window length, which is used to ensure locality of calculation. As a preferred scheme, the window length can be set according to the sensor spacing and the expected minimum disturbance wavelength, for example, it can be set to arrive The average sensor spacing is times that of the average sensor spacing; Indicates in window The arithmetic mean of the inner second derivatives.

[0044] According to the above formula, when the deviation profile is in position The surrounding area has a complex morphology, for example, there are The inflection point of the bend or At the apex of the bend, its curvature Dramatic changes can occur, resulting in a large variance within that window, which in turn affects the calculated local variation energy. It exhibits a relatively high peak value; conversely, if the strip is nearly straight or inclined at a uniform speed in this region, its curvature is constant or nearly constant, and the variance of the second derivative approaches zero. The value will be very low. Therefore, through analysis The distribution along the length of the strip can effectively locate the key areas where the strip shape undergoes significant changes.

[0045] like Figure 2 As shown in the figure, this illustrates the construction process of the local variation energy index. The upper subplot (smoothed deviation profile) displays the smoothed, continuous deviation profile function obtained after the S1 step preprocessing. ; Middle subplot (second derivative of the deviation profile): shows The second derivative of the strip directly corresponds to the local curvature of the strip at each point in a physical sense. It can be seen that in the straight sections of the strip, the second derivative is close to zero; in the curved sections, there are sharp fluctuations. Lower subplot (local variation energy distribution): This is the final output of step S2. The curve is obtained by calculating the second derivative energy within a sliding window. The peaks on the curve clearly mark regions where the strip morphology changes significantly, such as the two main peaks near 45 meters and 95 meters, providing a basis for subsequent identification of macroscopic deviations.

[0046] S3. Perform peak detection on the local variation energy curve to locate the deviation event, and extract the physical characteristic scale of the deviation by calculating the full width at half maximum (FWHM) of the energy peak.

[0047] In the steps Calculate the local variation energy curve Subsequently, the peak on the curve corresponds to the region of morphological change. However, these changes may originate from macroscopic deviations that significantly affect cutting quality, such as a large... Bending can also originate from localized, high-frequency, harmless disturbances. Therefore, this step requires... The key information that can distinguish between the two is extracted from the curve, namely the characteristic scale corresponding to the macroscopic deviation.

[0048] Its construction is based on the macroscopic deviation of a long waveform, such as a large one spanning tens of meters. The curvature change caused by the bending is gradual, and the corresponding variation energy is distributed over a relatively long spatial range. The curve shows a relatively broad and slowly changing energy peak; while a local, transient perturbation will produce a narrow and sharp energy peak. Therefore, the width of the peak is directly related to the physical scale of the deviation event.

[0049] The specific steps are as follows: First, for Peak detection is performed on the curve to locate the center of all energy peaks. For each detected peak, its characteristic scale is calculated. Specifically:

[0050] ;

[0051] in Is with peak point The characteristic scale of the associated deviation event, in physical terms, is the width of the spatial influence range of the deviation event; It is the Herveside step function, and its function value is [value] when its independent variable is greater than zero. Otherwise, it is 0; It is a threshold coefficient used to determine the effective support range of the peak. As a preferred option, The value can be set to At this point, the formula calculates the width of the energy peak at half its height. ), which is a commonly used and robust parameter value for measuring peak width. When When the value is high, such as The calculated scale is more sensitive to the shape of the peak; when When the value is low, such as This makes it more sensitive to the gradual change at the bottom of the peak. The implementer can evaluate and select a setting value that yields better results based on sensitivity and stability.

[0052] The above formula calculates the total length of the region where the energy value exceeds a certain proportion of the peak value through integration. This is applicable when the strip exhibits a large-scale, slowly changing energy... Bending or When bending, its corresponding The summit was very wide, leading to the calculations... A large value indicates that this deviation needs close monitoring. Conversely, a small value indicates a brief, localized jitter. The peak is very narrow, The value will be small, preferably, by setting a reasonable scale threshold, for example, more than several times the width of the strip itself threshold, that is, the effective screening out those The larger, the downstream cutting precision has a substantial impact on the macro deviation, and filter out the local disturbance without processing.

[0053] As Figure 3 shown, it shows the peak detection and macro deviation screening process.

[0054] The upper subgraph: in S2, the energy curve is obtained, and the center position of all energy peaks is located using the peak detection algorithm, that is, the red dot, corresponding to the key area where the deviation form changes; The lower subgraph: the characteristic scale of each peak is calculated, and the screening is carried out according to the preset threshold (for example, "> 10.0 meters" shown in the figure). The final remaining red triangle marked peak is those caused by large-scale, long-wave C-shaped or S-shaped bend, which has a substantial impact on the cutting precision of macro deviation, and those narrow and sharp local disturbances have been filtered out.

[0055] It should be noted that in this example, no local disturbance is screened, so the marked points of the upper and lower subgraphs are the same.

[0056] S4, according to the characteristic scale extracted in step S3, dynamically generate an adaptive analysis window, and use a third-order polynomial regression for local fitting in the window to realize accurate quantitative decoupling of the composite deviation form.

[0057] For each macro deviation screened in step , its characteristic scale is used to dynamically generate an analysis window with appropriate size, and the original deviation profile is locally decoupled in the window to replace the global analysis.

[0058] In the above steps, the occurrence position and physical scale of the macro deviation have been accurately located , and the subsequent analysis can be carried out in the local window centered on and with as the reference length. For this purpose, an adaptive scale analysis window half-width is constructed, which has:

[0059] Where represents the half-width of the analysis window customized for the current macro deviation; represents a value greater than a scale factor. Its role is to ensure that the analysis window can completely cover the entire deviation pattern region and its transition zone. As a preferred solution, the value range of a can be to . If is too small, it may cause the window edge to cut off part of the deviation pattern, affecting the fitting accuracy; if is too large, it will introduce too much irrelevant flat part, increasing the computational burden and possibly reducing the fitting sensitivity to the core pattern. Therefore, in the present embodiment, a can be set to .

[0060] In the above formula, the larger the characteristic scale of the macroscopic deviation, the larger the analysis window will be obtained automatically, and vice versa, thereby achieving a high degree of adaptive matching between the analysis scale and the deviation content.

[0061] Finally, within the local window defined by , the original deviation profile data in this interval is fitted by applying low-order polynomial regression. For example, the present embodiment uses a third-order polynomial model, as it is sufficient to describe the composite pattern of translation, tilt, type bending and type bending, specifically:

[0062] ;

[0063] By applying the least squares method in this local window, a set of unique fitting coefficients can be obtained. Specifically:

[0064] : represents the overall translation at the center of the deviation core region ;

[0065] : represents the local tilt at , corresponding to the angle of deflection of the strip;

[0066] : quantifies the degree and direction of type bending. The sign of its value represents the concave-convex direction of the bending, and the absolute value size represents the severity of the bending;

[0067] : quantifies the degree and direction of type bending. The presence and size of its value represent whether there is a twist in the strip in this region and the degree of the twist. ​

[0068] As shown in Figure 4 , which shows the decoupling process of the detected deviation event at 29.6 meters;

[0069] Analysis window: the X-axis range of the entire graph represents the characteristic scale-based S4 Dynamic adaptive analysis window; Local fitting: the blue curve in the graph is the original deviation data within this window, and the red curve is the fitting model obtained by third-order polynomial regression ; Parameter decoupling: the text box in the upper left corner lists the four core morphological parameters solved by the least square method: .

[0070] The above decoupled morphological parameters are transmitted to the downstream correction controller. The controller drives the corresponding actuator, such as the correction roller, to perform accurate and differentiated compensation actions. Since the entire analysis process is local and event-driven, and the analysis window size is dynamically adaptive, the present application reduces the response delay of the system without sacrificing the identification accuracy of long waveform macro-deviation, and realizes fast response and high precision of the correction control.

[0071] Compared with the prior art, the method proposed by the present application introduces the concepts of local variation energy and characteristic scale, and constructs an adaptive scale analysis framework. This framework can dynamically adjust the analysis window to match the physical scale of the deviation event, thereby solving the fundamental contradiction between real-time and analysis accuracy of the traditional method. At the same time, through local polynomial regression, the present application realizes accurate decoupling and quantification of complex deviation morphologies such as translation, tilt, type bending and type bending, etc., providing diagnostic information with rich spatial attributes for the downstream precise control, and significantly improving the performance and efficiency of the entire intelligent correction system.

[0072] An embodiment of an intelligent correction system for strip cutting:

[0073] On the other hand, the present application also provides an intelligent correction system for strip cutting. As shown in Figure 5 , an intelligent correction system for strip cutting includes a processor and a memory, and the memory stores computer program instructions, which, when executed by the processor, implement the intelligent correction method for strip cutting according to the first aspect of the present application.

[0074] The intelligent correction system for strip cutting also includes a communication interface and other components familiar to those skilled in the art, the settings and functions of which are known in the art, and therefore will not be described here.

[0075] In this description, the term "computer readable medium" can be replaced by terms such as memory, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), electrically programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM), static random access memory (SRAM), erasable programmable logic and other similar devices. When the system reads certain instructions from the computer-readable medium, the instructions become part of the operating system or application program. In the context of this document, the terms "computer readable medium" and "computer readable storage medium" are used to generally refer to media such as removable storage drive, a hard disk installed in the computer, a magnetic disk that stores data, or any other appropriate medium that stores the desired information.

Claims

1. A smart correction method for strip steel cutting, characterized in that, The method includes: Obtain the real-time deviation profile of the strip steel along its length upstream of the cutting station, including: Multiple displacement sensors are deployed upstream of the cutting station to collect lateral deviation data points of the strip edge; the lateral deviation data points are digitally filtered to obtain a smooth and continuous deviation profile function. ; Calculating the local variation energy of the deviation profile includes: ;in Indicates the location The second derivative at that point; Indicates the length of the local window; For window The arithmetic mean of the inner second derivative, wherein the local variation energy is positively correlated with the variance of the second derivative of the deviation profile within a local window of a set length; Locate the energy peaks in the local variation energy curves along the length direction, and calculate the characteristic scales of each energy peak, including: ;in Indicates the peak position of the macroscopic deviation; For position Local variation energy at the location; For the Herveside step function; The threshold coefficient is between 0.2 and 0.8; the feature scale is positively correlated with the width of the corresponding energy peak at a set threshold height; Macroscopic deviations with feature scales greater than a preset scale threshold are selected; for each macroscopic deviation, an adaptive analysis window is constructed with its peak position as the center and its width proportional to the feature scale; within the adaptive analysis window, the deviation profile is fitted with a polynomial to obtain the fitting coefficients; The fitting coefficients are transmitted to the correction controller to perform correction actions.

2. The intelligent correction method for strip steel cutting according to claim 1, characterized in that, The range of the adaptive analysis window is: ,in The peak position of the macroscopic deviation, half the window width. The specific calculation method is as follows: ;in This indicates a proportionality coefficient greater than 1; This represents the characteristic scale associated with the peak location.

3. The intelligent correction method for strip cutting according to claim 2, characterized in that, The proportionality coefficient The value range is 1.2 to 2.

0.

4. The intelligent correction method for strip steel cutting according to claim 1, characterized in that, The polynomial fitting is a third-order polynomial regression performed using the least squares method, specifically: ； in This represents the deviation profile after fitting. This represents the peak position of the macroscopic deviation; The fitting coefficient is denoted as .

5. The intelligent correction method for strip steel cutting according to claim 4, characterized in that, In the equation obtained by the third-order polynomial regression, the zero-order, first-order, second-order, and third-order coefficients correspond to the translation, inclination, C-shaped bending degree, and S-shaped bending degree of the strip, respectively.

6. The intelligent correction method for strip steel cutting according to claim 1, characterized in that, After receiving the fitting coefficients, the correction controller drives the correction roller actuator to perform a compensation action to correct the macroscopic deviation.

7. An intelligent deviation correction system for strip steel cutting, characterized in that, It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent correction method for strip cutting as described in any one of claims 1 to 6 is implemented.

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