Oxide skin detection method and device, electronic equipment, storage medium and product

Abstract

The invention discloses an oxide skin detection method and device, electronic equipment, a storage medium and a product, and relates to the technical field of nondestructive detection.The oxide skin detection method comprises the steps that a terahertz echo signal sparse decomposition model for oxide skin on the surface of hot-rolled strip steel is constructed; based on the terahertz echo signal sparse decomposition model, solving the pulse response sequence optimal solution for the hot-rolled strip steel surface oxide skin to obtain a sparse reconstructed pulse response function; and generating a binary scanning image of the hot-rolled strip steel surface oxide skin based on the pulse response function, wherein the binary scanning image is used for representing the distribution condition of the hot-rolled strip steel surface oxide skin. The method can guarantee the nondestructive and comprehensive measurement of the oxide skin on the surface of the hot-rolled strip steel.

Description

Technical Field

[0001] The present application relates to the field of nondestructive testing technology, and in particular to oxide scale detection methods, devices, electronic equipment, storage media and products. Background Art

[0002] As a common metal material, hot-rolled strip is widely used in construction, automobile manufacturing, chemical industry and other fields due to its advantages such as low price, strong corrosion resistance, excellent mechanical and processing properties. However, during the hot rolling process, micron-level oxide scale will inevitably form on the surface of the steel. If the oxide scale is damaged, it will seriously affect the service life and performance stability of the hot-rolled strip.

[0003] However, existing oxide scale detection technologies such as optical microscope detection, ultrasonic detection, infrared detection, eddy current detection, etc., have shortcomings such as contact detection, destructive detection, and poor spatial resolution. They cannot be fully applied to the industrial production environment of hot-rolled strip, and it is difficult to ensure non-destructive and comprehensive measurement of the oxide scale on the surface of hot-rolled strip. Summary of the invention

[0004] The main purpose of this application is to provide an oxide scale detection method, device, electronic equipment, storage medium and product, aiming to ensure non-destructive and comprehensive measurement of oxide scale on the surface of hot-rolled strip.

[0005] To achieve the above purpose, the present application proposes an oxide scale detection method, which comprises: Construct a sparse decomposition model of terahertz echo signal for the oxide scale on the surface of hot-rolled strip; Solving the optimal solution of the pulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal, and obtaining a sparsely reconstructed pulse response function; A binary scanning image of the oxide scale on the surface of the hot-rolled steel strip is generated based on the impulse response function, and the binary scanning image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0006] In one embodiment, before the step of constructing a sparse decomposition model of a terahertz echo signal for oxide scale on the surface of a hot-rolled steel strip, the step further includes: A time-slice dispersion model for the oxide scale on the surface of hot-rolled steel strip is constructed. The time-slice dispersion model is used to characterize the spatiotemporal morphological evolution characteristics of the pulse waveform of the terahertz time-domain reflection signal of the oxide scale on the surface of the hot-rolled steel strip. The kernel function of the time-slice dispersion model is: , in, represents the kernel function, It represents the dispersion degree of the terahertz echo signal of the oxide scale on the surface of the hot-rolled strip, and n represents the sampling point.

[0007] In one embodiment, the step of constructing a sparse decomposition model of a terahertz echo signal for oxide scale on the surface of a hot-rolled steel strip comprises: A terahertz echo signal sparse decomposition model for the oxide scale on the surface of hot-rolled strip is constructed. The terahertz echo signal sparse decomposition model is expressed as: , Where Y represents the sparse decomposition model of terahertz echo signal, The matrix representing the spread of the terahertz pulse over the entire propagation distance, , The column vector is composed of the time delay sequence of the terahertz reference signal, represents the column vector consisting of impulse response functions, represents Gaussian white noise.

[0008] In one embodiment, the step of solving the optimal solution of the impulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal to obtain the sparsely reconstructed impulse response function includes: The non-convex optimization problem of the terahertz echo signal sparse decomposition model is simplified to a convex optimization problem by solving the l1 norm instead of the l0 norm. The convex optimization problem is expressed as: , in, is a regularization parameter used to balance reconstruction accuracy and sparsity, for The l1 norm of ; The convex optimization problem is solved to obtain a sparsely reconstructed impulse response function.

[0009] In one embodiment, the step of solving the convex optimization problem to obtain a sparsely reconstructed impulse response function comprises: The two-step iterative shrinkage threshold algorithm is used to solve the convex optimization problem, and the iterative formula of h is obtained as follows: , , Among them, h0 is the initial vector, and are two convergence parameters of the two-step iterative shrinkage threshold algorithm. In addition, , in, is about the current vector Soft thresholding or shrinkage operator for each component of ; The impulse response function of sparse reconstruction is calculated according to the iterative formula.

[0010] In one embodiment, the step of generating a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the impulse response function, wherein the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip, comprises: Performing a binary scanning operation on the impulse response function along the long axis direction of the oxide scale on the surface of the hot-rolled steel strip to obtain a binary scanning result, in which an effective peak in the impulse response function is assigned as a first value, and a position in the impulse response function other than the effective peak is assigned as a second value; A binary scanning image of the oxide scale on the surface of the hot-rolled steel strip is generated based on the binary scanning result, and the binary scanning image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0011] In addition, to achieve the above purpose, the present application also proposes an oxide scale detection device, which includes: A model building module is used to build a sparse decomposition model of terahertz echo signals for the oxide scale on the surface of hot-rolled strip; A function reconstruction module, used for solving the optimal solution of the pulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal, and obtaining a sparsely reconstructed pulse response function; An image generation module is used to generate a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the impulse response function, wherein the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0012] In addition, to achieve the above objectives, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the oxide scale detection method as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the oxide scale detection method as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the oxide scale detection method described above are implemented.

[0015] The present application proposes a method for detecting oxide scale. The present application constructs a sparse decomposition model of a terahertz echo signal for the oxide scale on the surface of a hot-rolled steel strip. The model can parse the terahertz echo signal reflected from the surface of the hot-rolled steel strip, effectively filter out noise interference, and extract information related to the characteristics of the oxide scale on the surface of the hot-rolled steel strip. Then, based on the sparse decomposition model of the terahertz echo signal, an optimal solution of a pulse response sequence that can best reflect the characteristics of the oxide scale on the surface of the hot-rolled steel strip is solved. Based on this optimal solution, a sparsely reconstructed pulse response function is obtained, which is used to characterize the response characteristics of the oxide scale to terahertz waves. Finally, a binary scanning image of the oxide scale on the surface of the hot-rolled steel strip is generated using the sparsely reconstructed pulse response function. The image intuitively displays the distribution of the oxide scale on the surface of the hot-rolled steel strip, including its position and thickness changes.

[0016] In summary, the present application uses terahertz technology to detect the surface of the hot-rolled strip, thereby avoiding physical damage or chemical corrosion that may be caused by traditional methods, and realizing non-destructive detection of oxide scale on the surface of the hot-rolled strip; at the same time, by constructing a sparse decomposition model of the terahertz echo signal and solving the optimal solution of the pulse response sequence, it is possible to fully capture and analyze the oxide scale information contained in the terahertz echo signal, and finally, by generating a binary scanning image, the distribution of the oxide scale is made clearer and more intuitive, which helps to timely discover and solve potential quality problems, and ensure the comprehensiveness and accuracy of the oxide scale detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of a process flow provided for Example 1 of the oxide scale detection method of the present application; Figure 2 A schematic diagram of the propagation path provided for Example 2 of the oxide scale detection method of the present application; Figure 3 A schematic diagram of the time domain spectrum of the terahertz reflection signal provided in Example 2 of the oxide scale detection method of the present application; Figure 4 A schematic diagram of an impulse response function provided for Example 2 of the oxide scale detection method of the present application; Figure 5A schematic diagram of a binary scan image provided in Example 2 of the oxide scale detection method of the present application; Figure 6 This is a schematic diagram of the module structure of the oxide scale detection device according to an embodiment of the present application; Figure 7 Schematic diagram of the equipment structure of the hardware operating environment involved in the oxide scale detection method in the embodiment of the present application.

[0020] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] Existing oxide scale detection technologies such as optical microscope detection, ultrasonic detection, infrared detection, eddy current detection, etc., have shortcomings such as contact detection, destructive detection, and poor spatial resolution. They cannot be fully applied to the industrial production environment of hot-rolled strip, and it is difficult to ensure non-destructive and comprehensive measurement of the oxide scale on the surface of hot-rolled strip.

[0024] The embodiment of the present application provides a solution, by constructing a terahertz echo signal sparse decomposition model for the oxide scale on the surface of the hot-rolled strip, the model can analyze the terahertz echo signal reflected from the surface of the hot-rolled strip, effectively filter out noise interference, and extract information related to the characteristics of the oxide scale on the surface of the hot-rolled strip; then, based on the terahertz echo signal sparse decomposition model, the optimal solution of the pulse response sequence that can best reflect the characteristics of the oxide scale on the surface of the hot-rolled strip is solved, and based on this optimal solution, a sparsely reconstructed pulse response function is obtained, which is used to characterize the response characteristics of the oxide scale to terahertz waves; finally, a binary scan image of the oxide scale on the surface of the hot-rolled strip is generated using the sparsely reconstructed pulse response function, which displays the distribution of the oxide scale on the surface of the hot-rolled strip in an intuitive way, including its position and thickness changes.

[0025] In summary, the embodiment of the present application uses terahertz technology to detect the surface of the hot-rolled strip, thereby avoiding physical damage or chemical corrosion that may be caused by traditional methods, and realizing non-destructive detection of oxide scale on the surface of the hot-rolled strip; at the same time, by constructing a sparse decomposition model of the terahertz echo signal and solving the optimal solution of the pulse response sequence, it is possible to fully capture and analyze the oxide scale information contained in the terahertz echo signal, and finally, by generating a binary scanning image, the distribution of the oxide scale is made clearer and more intuitive, which helps to timely discover and solve potential quality problems, and ensure the comprehensiveness and accuracy of the oxide scale detection.

[0026] It should be noted that the execution subject of this embodiment can be an electronic device with data processing, network communication and program running functions, such as a computer, a server, etc., or an electronic device capable of realizing the above functions, etc. The following takes the oxide skin detection terminal as an example to illustrate this embodiment and the following embodiments.

[0027] Based on this, the present application embodiment provides a method for detecting oxide scale, referring to Figure 1 , Figure 1 This is a schematic flow chart of the first embodiment of the oxide scale detection method of the present application.

[0028] In this embodiment, the oxide scale detection method includes steps S10 to S30: Step S10, constructing a sparse decomposition model of the terahertz echo signal for the oxide scale on the surface of the hot-rolled strip; A terahertz echo signal sparse decomposition model specifically for the detection of oxide scale on the surface of hot-rolled strip is constructed. The model is used to effectively process the complex echo signal generated by the interaction between the terahertz wave and the oxide scale on the surface of the hot-rolled strip by using the sparse decomposition theory, ensuring that the model can accurately capture the characteristic information related to the oxide scale characteristics of the hot-rolled strip in the terahertz echo signal.

[0029] Step S20, solving the optimal solution of the pulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal to obtain a sparsely reconstructed pulse response function; Based on the constructed sparse decomposition model of terahertz echo signals, the optimal solution of the pulse response sequence of the oxide scale on the surface of the hot-rolled strip is solved, and the optimal solution of the pulse response sequence that can best describe the response characteristics of the oxide scale to the terahertz wave is obtained. Subsequently, the sparse reconstruction technology is used to generate a sparsely reconstructed pulse response function. This function not only retains the important information in the original terahertz echo signal, but also improves the clarity and accuracy of the signal by removing redundancy and noise, thereby obtaining higher quality characteristic information related to the characteristics of the oxide scale of the hot-rolled strip.

[0030] Step S30: generating a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the impulse response function, wherein the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0031] Based on the obtained pulse response function, a binary scan image of the oxide scale on the surface of the hot-rolled strip is generated through a specific image processing algorithm (such as binary scanning). The image is represented in binary form, that is, each pixel in the image contains only two states (such as black or white), so as to intuitively and clearly reflect the distribution of the oxide scale on the surface of the hot-rolled strip. Through this image, the location, thickness, distribution law and other characteristics of the oxide scale can be quickly understood.

[0032] In summary, the embodiment of the present application uses terahertz technology to detect the surface of the hot-rolled strip, thereby avoiding physical damage or chemical corrosion that may be caused by traditional methods, and realizing non-destructive detection of oxide scale on the surface of the hot-rolled strip; at the same time, by constructing a sparse decomposition model of the terahertz echo signal and solving the optimal solution of the pulse response sequence, it is possible to fully capture and analyze the oxide scale information contained in the terahertz echo signal, and finally, by generating a binary scanning image, the distribution of the oxide scale is made clearer and more intuitive, which helps to timely discover and solve potential quality problems, and ensure the comprehensiveness and accuracy of the oxide scale detection.

[0033] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. On this basis, step S40 can also be included before step S10: Step S40, constructing a time-slice dispersion model for the oxide scale on the surface of the hot-rolled steel strip, wherein the time-slice dispersion model is used to characterize the spatiotemporal morphological evolution characteristics of the pulse waveform of the terahertz time-domain reflection signal of the oxide scale on the surface of the hot-rolled steel strip, and the kernel function of the time-slice dispersion model is: , in, represents the kernel function, It represents the dispersion degree of the terahertz echo signal of the oxide scale on the surface of the hot-rolled strip, and n represents the sampling point.

[0034] In this embodiment, before formally constructing a sparse decomposition model of the terahertz echo signal for the oxide scale on the surface of the hot-rolled strip, a time slice dispersion model for the oxide scale on the surface of the hot-rolled strip is constructed. The model is used to characterize the spatiotemporal morphological evolution characteristics of the pulse waveform of the oxide scale on the surface of the hot-rolled strip in the terahertz time domain reflection signal.

[0035] The time-slice dispersion model captures the effect of oxide skin on the propagation characteristics of terahertz waves through its kernel function. is defined as a piecewise function, the specific form is: , in, As the output of the kernel function, it is directly related to the dispersion degree at each sampling point n (representing different positions on the surface of the hot-rolled strip), a represents the dispersion degree of the terahertz echo signal for the oxide scale on the surface of the hot-rolled strip, and is used to quantify the influence of the oxide scale on the waveform distortion and energy dispersion during the propagation of the terahertz wave. Specifically, a represents the dispersion degree of the terahertz echo signal, and 0≤a≤0.5. For example, in this embodiment, a=0.03 is taken.

[0036] In a feasible embodiment, step S10 may include step S101: Step S101, constructing a terahertz echo signal sparse decomposition model for the oxide scale on the surface of the hot-rolled strip, wherein the terahertz echo signal sparse decomposition model is expressed as: , Where Y represents the sparse decomposition model of terahertz echo signal, The matrix representing the spread of the terahertz pulse over the entire propagation distance, , The column vector is composed of the time delay sequence of the terahertz reference signal, represents the column vector consisting of impulse response functions, represents Gaussian white noise.

[0037] In this embodiment, the mathematical expression of the sparse decomposition model of the terahertz echo signal for the oxide scale on the surface of the hot-rolled strip is determined as follows: , where Y represents the output of the sparse decomposition model of the terahertz echo signal, which is a signal reconstruction result containing the oxide skin information, and Φ' is a matrix representing the expansion of the terahertz pulse within the entire propagation distance, whose column vector is determined by the time-sliced ​​dispersion model (through The pulse spread function is composed of , the column vector of Φ is composed of the time delay sequence of the terahertz echo reference signal, h is a column vector composed of the impulse response function, which directly reflects the response characteristics of the oxide skin to the terahertz wave, and e represents Gaussian white noise, which is an inevitable interference factor in actual measurement.

[0038] By constructing a sparse decomposition model of the terahertz echo signal for the oxide scale on the surface of hot-rolled strip, the performance of the oxide scale on the surface of hot-rolled strip in the terahertz echo signal can be analyzed more accurately.

[0039] For example, in one possible implementation, Figure 2Schematic diagram of the propagation path of terahertz pulses emitted to hot-rolled steel strips. The hot-rolled steel strips include a steel substrate and an oxide scale wrapped on the surface of the substrate. The reflective terahertz time-domain spectroscopy system emits terahertz pulses to the complete hot-rolled steel strips and obtains the reflected terahertz pulses from the interface between air and oxide scale. Echo and interface between oxide scale and steel substrate The echo is the terahertz reflection signal, and the time domain spectrum of the terahertz reflection signal is as follows: Figure 3 As shown, the horizontal axis Optical delay (ps) is the optical delay (picoseconds), and the vertical axis Amplitude (au) is the amplitude (arbitrary unit).

[0040] In a feasible embodiment, step S20 may include steps S201-S202: Step S201, simplifying the non-convex optimization problem of the terahertz echo signal sparse decomposition model into a convex optimization problem by solving the l1 norm instead of the l0 norm. The convex optimization problem is expressed as: , in, is a regularization parameter used to balance reconstruction accuracy and sparsity, for The l1 norm of ; Since directly solving the sparse decomposition model of the terahertz echo signal based on the l0 norm (i.e. directly finding the solution with the least non-zero elements) is a non-convex optimization problem, the computational complexity is high and it is easy to fall into the local optimum. Therefore, in this embodiment, the l1 norm (i.e. the sum of the absolute values ​​of each element in the vector) is used to replace the l0 norm to transform the problem into a convex optimization problem. The convex optimization problem has the advantages of global optimal solution and easy solution. The converted convex optimization problem is expressed as: , where λ is the regularization parameter used to find the best balance between reconstruction accuracy (i.e., the accuracy of the signal fit) and sparsity (i.e., the number of non-zero elements in the impulse response function), for The l1 norm of , which is used to promote the sparsity of the solution.

[0041] Step S202, solving the convex optimization problem to obtain a sparsely reconstructed impulse response function.

[0042] In a feasible embodiment, step S202 may include steps S2021-S2022: Step S2021, using a two-step iterative shrinkage threshold algorithm to solve the convex optimization problem, and obtaining the iterative formula of h is: , , Among them, h0 is the initial vector, and are two convergence parameters of the two-step iterative shrinkage threshold algorithm. In addition, , in, is about the current vector Soft thresholding or shrinkage operator for each component of ; In this embodiment, in order to solve the convex optimization problem, a two-step iterative shrinkage / thresholding algorithm (Two-Step Iterative Shrinkage / Thresholding Algorithm, TWIST) may be used. The algorithm gradually approaches the optimal solution by iteratively updating the vector h.

[0043] In the TWIST algorithm, each iteration updates the vector h based on the results of the previous two iterations and the current gradient information. Specifically, the iteration formula is: , , Among them, h0 is the initial vector, and are two convergence parameters of the two-step iterative shrinkage threshold algorithm, which are used to control the convergence speed and stability during the iteration process. In addition, , in, is about the current vector A soft threshold or shrinkage operator is applied to each component of , which is used to set elements smaller than a threshold λ to zero to promote sparsity.

[0044] Step S2022: Calculate the impulse response function of sparse reconstruction according to the iterative formula.

[0045] By repeatedly applying the above iterative formula until the convergence condition is met (such as the number of iterations reaches the preset upper limit or the change in the objective function value is less than the preset threshold), the sparsely reconstructed impulse response function h(t) is finally obtained, which can effectively reflect the characteristics of the oxide scale on the surface of the hot-rolled strip, such as thickness, distribution, etc.

[0046] For example, in a feasible implementation manner, the impulse response function after sparse reconstruction is as follows: Figure 4As shown, the horizontal axis Optical delay (ps) is optical delay (picoseconds), and the vertical axis Amplitude (au) is amplitude (arbitrary unit), which describes the change of the amplitude of the optimized terahertz echo over time in the time domain. By analyzing the amplitude and phase of the pulse response function, the thickness and uniformity characteristics of the oxide scale on the surface of the hot-rolled strip can be determined.

[0047] In a feasible embodiment, step S30 may include steps S301-S302: Step S301, performing a binary scanning operation on the impulse response function along the long axis direction of the oxide scale on the surface of the hot-rolled steel strip to obtain a binary scanning result, in which the effective peak in the impulse response function is assigned as a first value, and the position other than the effective peak in the impulse response function is assigned as a second value; After obtaining the sparsely reconstructed impulse response function, in order to extract the information directly related to the oxide scale on the surface of the hot-rolled strip, a binary scanning operation is performed along the long axis direction of the oxide scale on the surface of the hot-rolled strip to identify and mark the effective peaks in the impulse response function. These effective peaks correspond to the specific positions and characteristics of the oxide scale on the surface of the hot-rolled strip.

[0048] Specifically, by traversing each element of the impulse response function and comparing it with a preset threshold, if the value of an element exceeds the threshold, it is considered to represent a valid peak, and the position is assigned as the first value in the binary scan result, and the first value is usually set to "1". Conversely, if the element value does not exceed the threshold, the position is considered to contain no valid peak, and it is assigned as the second value in the binary scan result, and the second value is usually set to "0". After binary scanning, a binary array (or binary image) of the same size as the impulse response function is obtained, in which the position of "1" directly corresponds to the presence of oxide scale.

[0049] Step S302: generating a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the binary scan result, wherein the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0050] Based on the obtained binary scanning results, a binary scanning image of the oxide scale on the surface of the hot-rolled strip is further generated. The image shows the distribution of the oxide scale on the surface of the hot-rolled strip in an intuitive manner. For example, each "1" and "0" in the binary scanning results is mapped to a specific color or grayscale in the image. Usually, "1" is mapped to white in the image to highlight the location of the oxide scale; and "0" is mapped to black in the image to exist as the background.

[0051] Exemplarily, in a feasible implementation, the surface structure of the hot-rolled strip steel is scanned along a straight line, and the time domain spectra of multiple continuous measurement points are obtained by emitting terahertz pulses using a reflective terahertz time-domain spectroscopy system, and the stratigraphic information of the complete oxide scale on the surface of the hot-rolled strip steel is studied based on the sparse reconstruction results. In this embodiment, the spacing between adjacent measurement points is 1.5 mm. Figure 5 The binary scan image along the long axis of the hot-rolled strip after sparse representation is shown, in which the effective peak is assigned the value "1" and is mapped to white in the figure, and the other positions are assigned the value "0" and are mapped to black in the figure. The two white lines along the long axis of the hot-rolled strip in the figure represent the interface between air and oxide scale and the interface between oxide scale and steel substrate, respectively. Regardless of the height and sign of the peak, the wavy oxide scale and steel substrate interface can also be observed in the binary scan image, and the optical distance between the two non-zero positions (i.e., the thickness of the oxide scale) varies along the long axis of the hot-rolled strip under study, indicating that the physical thickness of the oxide scale on the surface of the hot-rolled strip is not uniform.

[0052] In this way, through binary scanning operations and image generation technology, the sparsely reconstructed impulse response function is converted into a visual binary scanning image, making the distribution of oxide scale on the surface of the hot-rolled strip clearer and more intuitive, which helps to timely discover and solve potential quality problems and ensure the comprehensiveness and accuracy of oxide scale detection.

[0053] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the oxide scale detection method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0054] The present application also provides an oxide scale detection device, please refer to Figure 6 , the oxide skin detection device includes: A model building module 10 is used to build a sparse decomposition model of a terahertz echo signal for oxide scale on the surface of a hot-rolled steel strip; A function reconstruction module 20, for solving the optimal solution of the pulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal, and obtaining a sparsely reconstructed pulse response function; The image generation module 30 is used to generate a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the impulse response function, wherein the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0055] Optionally, the scale detection device further includes a dispersion model building module (not shown), and the dispersion model building module is used to: A time-slice dispersion model for the oxide scale on the surface of hot-rolled steel strip is constructed. The time-slice dispersion model is used to characterize the spatiotemporal morphological evolution characteristics of the pulse waveform of the terahertz time-domain reflection signal of the oxide scale on the surface of the hot-rolled steel strip. The kernel function of the time-slice dispersion model is: , in, represents the kernel function, It represents the dispersion degree of the terahertz echo signal of the oxide scale on the surface of the hot-rolled strip, and n represents the sampling point.

[0056] Optionally, the model building module 10 is further used to: A terahertz echo signal sparse decomposition model for the oxide scale on the surface of hot-rolled strip is constructed. The terahertz echo signal sparse decomposition model is expressed as: , Where Y represents the sparse decomposition model of terahertz echo signal, The matrix representing the spread of the terahertz pulse over the entire propagation distance, , The column vector is composed of the time delay sequence of the terahertz reference signal, represents the column vector consisting of impulse response functions, represents Gaussian white noise.

[0057] Optionally, the function reconstruction module 20 is further used for: The non-convex optimization problem of the terahertz echo signal sparse decomposition model is simplified to a convex optimization problem by solving the l1 norm instead of the l0 norm. The convex optimization problem is expressed as: , in, is a regularization parameter used to balance reconstruction accuracy and sparsity, for The l1 norm of ; The convex optimization problem is solved to obtain a sparsely reconstructed impulse response function.

[0058] Optionally, the function reconstruction module 20 is further used for: The two-step iterative shrinkage threshold algorithm is used to solve the convex optimization problem, and the iterative formula of h is obtained as follows: , , Among them, h0 is the initial vector, and are two convergence parameters of the two-step iterative shrinkage threshold algorithm. In addition, , in, is about the current vector Soft thresholding or shrinkage operator for each component of ; The impulse response function of sparse reconstruction is calculated according to the iterative formula.

[0059] Optionally, the image generation module 30 is further configured to: Performing a binary scanning operation on the impulse response function along the long axis direction of the oxide scale on the surface of the hot-rolled steel strip to obtain a binary scanning result, in which an effective peak in the impulse response function is assigned as a first value, and a position in the impulse response function other than the effective peak is assigned as a second value; A binary scanning image of the oxide scale on the surface of the hot-rolled steel strip is generated based on the binary scanning result, and the binary scanning image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0060] The oxide scale detection device provided in the embodiment of the present application adopts the oxide scale detection method in the above embodiment, which can ensure non-destructive and comprehensive measurement of the oxide scale on the surface of the hot-rolled strip. Compared with the prior art, the beneficial effects of the oxide scale detection device provided in the embodiment of the present application are the same as the beneficial effects of the oxide scale detection method provided in the above embodiment, and other technical features in the oxide scale detection device are the same as the features disclosed in the oxide scale detection method in the above embodiment, which will not be repeated here.

[0061] An embodiment of the present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the oxide scale detection method in the above-mentioned embodiment 1.

[0062] Reference below Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include but are not limited to mobile terminals such as laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0063] like Figure 7 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0064] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0065] The electronic device provided in the embodiment of the present application adopts the oxide scale detection method in the above embodiment, which can ensure non-destructive and comprehensive measurement of the oxide scale on the surface of the hot-rolled strip. Compared with the prior art, the beneficial effects of the electronic device provided in the embodiment of the present application are the same as the beneficial effects of the oxide scale detection method provided in the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the oxide scale detection method in the previous embodiment, which will not be repeated here.

[0066] It should be understood that the various parts disclosed in the embodiments of the present application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0067] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0068] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the oxide scale detection method in the above-mentioned embodiment.

[0069] The computer-readable storage medium provided in the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0070] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0071] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: constructs a terahertz echo signal sparse decomposition model for the oxide scale on the surface of the hot-rolled steel strip; solves the optimal solution of the pulse response sequence for the oxide scale on the surface of the hot-rolled steel strip based on the terahertz echo signal sparse decomposition model to obtain a sparsely reconstructed pulse response function; generates a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the pulse response function, and the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

[0072] The computer program code for performing the operation of the embodiment of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0073] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0074] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0075] The readable storage medium provided in the embodiment of the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned oxide scale detection method, and can ensure non-destructive and comprehensive measurement of oxide scale on the surface of hot-rolled strip. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the embodiment of the present application are the same as the beneficial effects of the oxide scale detection method provided in the above-mentioned embodiment, and will not be described in detail here.

[0076] The present application also provides a computer program product, including a computer program, which implements the steps of the oxide scale detection method as described above when executed by a processor.

[0077] The computer program product provided in this application can solve the technical problem of oxide scale detection. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the oxide scale detection method provided in the above embodiment, which will not be repeated here.

[0078] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for detecting oxide scale, characterized in that: The method comprises: Construct a sparse decomposition model of terahertz echo signal for the oxide scale on the surface of hot-rolled strip; Solving the optimal solution of the pulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal, and obtaining a sparsely reconstructed pulse response function; A binary scanning image of the oxide scale on the surface of the hot-rolled steel strip is generated based on the impulse response function, and the binary scanning image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

2. The scale detection method according to claim 1, characterized in that: Before the step of constructing a sparse decomposition model of the terahertz echo signal for the oxide scale on the surface of the hot-rolled strip, the method further includes: A time-slice dispersion model for the oxide scale on the surface of hot-rolled steel strip is constructed. The time-slice dispersion model is used to characterize the spatiotemporal morphological evolution characteristics of the pulse waveform of the terahertz time-domain reflection signal of the oxide scale on the surface of the hot-rolled steel strip. The kernel function of the time-slice dispersion model is: ， in, represents the kernel function, It represents the dispersion degree of the terahertz echo signal of the oxide scale on the surface of the hot-rolled strip, and n represents the sampling point.

3. The scale detection method according to claim 2, characterized in that: The step of constructing a sparse decomposition model of a terahertz echo signal for oxide scale on the surface of a hot-rolled steel strip comprises: A terahertz echo signal sparse decomposition model for the oxide scale on the surface of hot-rolled strip is constructed. The terahertz echo signal sparse decomposition model is expressed as: ， Where Y represents the sparse decomposition model of terahertz echo signal, The matrix representing the spread of the terahertz pulse over the entire propagation distance, , The column vector is composed of the time delay sequence of the terahertz reference signal, represents the column vector consisting of impulse response functions, represents Gaussian white noise.

4. The scale detection method according to claim 3, characterized in that: The step of solving the optimal solution of the impulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal to obtain the sparsely reconstructed impulse response function comprises: The non-convex optimization problem of the terahertz echo signal sparse decomposition model is simplified to a convex optimization problem by solving the l1 norm instead of the l0 norm. The convex optimization problem is expressed as: ， in, is a regularization parameter used to balance reconstruction accuracy and sparsity, for h The l1 norm of ; The convex optimization problem is solved to obtain a sparsely reconstructed impulse response function.

5. The scale detection method according to claim 4, characterized in that: The step of solving the convex optimization problem to obtain a sparsely reconstructed impulse response function comprises: A two-step iterative shrinkage threshold algorithm is used to solve the convex optimization problem, and we get h The iteration formula is: ， ， Among them, h0 is the initial vector, and are two convergence parameters of the two-step iterative shrinkage threshold algorithm. In addition, ， in, is about the current vector Soft thresholding or shrinkage operator for each component of ; The impulse response function of sparse reconstruction is calculated according to the iterative formula.

6. The scale detection method according to claim 1, characterized in that: The step of generating a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the impulse response function, wherein the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip, comprises: Performing a binary scanning operation on the impulse response function along the long axis direction of the oxide scale on the surface of the hot-rolled steel strip to obtain a binary scanning result, in which an effective peak in the impulse response function is assigned as a first value, and a position in the impulse response function other than the effective peak is assigned as a second value; A binary scanning image of the oxide scale on the surface of the hot-rolled steel strip is generated based on the binary scanning result, and the binary scanning image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

7. An oxide scale detection device, characterized in that: The device comprises: A model building module is used to build a sparse decomposition model of terahertz echo signals for the oxide scale on the surface of hot-rolled strip; A function reconstruction module, used for solving the optimal solution of the pulse response sequence for the oxide scale on the surface of the hot-rolled strip based on the sparse decomposition model of the terahertz echo signal, and obtaining a sparsely reconstructed pulse response function; An image generation module is used to generate a binary scan image of the oxide scale on the surface of the hot-rolled steel strip based on the impulse response function, wherein the binary scan image is used to characterize the distribution of the oxide scale on the surface of the hot-rolled steel strip.

8. An electronic device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the scale detection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the oxide scale detection method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the scale detection method according to any one of claims 1 to 6 are implemented.

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