Shock wave curvature measuring method and device

Abstract

The invention provides a shock wave curvature measurement method and device. The method comprises the following steps: acquiring an original image obtained through a planar laser scattering technology; performing contrast enhancement and noise reduction on the original image to obtain a target image; a window area is selected from the shock wave starting point in the target image; carrying out adaptive image segmentation on the newest window area to obtain a local shock wave curve, and judging whether a shock wave end point exists or not; if not, moving the window area in the target image based on the geometric direction of the latest local shock wave curve, and returning to the step of performing adaptive image segmentation on the latest window area to obtain the local shock wave curve; if yes, the local shock wave curves are spliced, the curvature is calculated, and the shock wave curvature is obtained. According to the scheme, the strategy of combining the moving window and the local adaptive image segmentation is adopted, it is ensured that the optimal segmentation effect can be achieved in each local area, the accuracy of shock wave curve extraction is improved, and therefore the purpose of improving the accuracy of shock wave curvature measurement is achieved.

Description

Technical Field

[0001] This invention relates to the field of hypersonic flow field measurement and display technology, specifically to a shock wave curvature measurement method and device. Background Technology

[0002] Shock wave curvature is a core parameter characterizing the three-dimensional geometric features of a shock wave surface. It is defined as the curvature of the two-dimensional curve formed by the intersection of the shock wave surface and a given observation plane. This parameter directly quantifies the local bending degree of the shock wave, fundamentally determining key physical properties such as the streamline curvature and pressure gradient of the airflow behind the wave. Therefore, accurate measurement of shock wave curvature is of irreplaceable value for deepening our understanding of hypersonic flows, verifying flow field theory solutions, and optimizing aircraft aerodynamic design. However, achieving accurate and quantitative measurement of shock wave curvature in wind tunnel tests has remained a pressing technical challenge. Planar laser scattering (PLS), as a non-contact flow field visualization and measurement method, has been widely applied in hypersonic wind tunnel tests. This technology captures the flow field structure within a two-dimensional cross-section by seeding nanoscale tracer particles into the flow field and utilizing their Rayleigh scattering effect on specific sheet laser light. Currently, PLS technology has been successfully applied to the study of physical phenomena such as boundary layer transition and shock wave interaction.

[0003] Although PLS technology can clearly display the flow field structure, including shock waves, existing technologies mainly focus on qualitative display and morphological analysis of the flow field. Furthermore, the low signal-to-noise ratio and uneven background of raw PLS images make accurate identification and continuous tracking of shock wave curves extremely difficult (for example, traditional global image processing methods are prone to fragmentation or distortion of the extracted shock wave curves when faced with background intensity variations caused by uneven tracer particle distribution). This further hinders the accurate extraction of shock wave curvature. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a shock wave curvature measurement method and apparatus to improve the accuracy of shock wave curvature measurement.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] The first aspect of this invention discloses a shock wave curvature measurement method, the method comprising:

[0007] Acquire the original image obtained through planar laser scattering technology;

[0008] The original image is subjected to contrast enhancement and noise reduction to obtain the target image;

[0009] A window area is selected by defining a preset shock wave initiation point in the target image;

[0010] Adaptive image segmentation is performed on the latest window region to obtain the local shock curve;

[0011] Determine whether the latest window area contains a preset shock wave termination point;

[0012] If not, then based on the latest geometric direction of the local shock curve, the window region is moved in the target image, and the process returns to perform adaptive image segmentation on the latest window region to obtain the local shock curve.

[0013] If so, the individual local shock curves are spliced ​​together to obtain a complete shock curve;

[0014] The curvature of the complete shock wave curve is calculated to obtain the shock wave curvature.

[0015] Optionally, the step of performing contrast enhancement and noise reduction on the original image to obtain the target image includes:

[0016] The original image is contrast-enhanced using Gamma correction technology;

[0017] The original image after contrast enhancement is subjected to Gaussian blurring to obtain the target image.

[0018] Optionally, the adaptive image segmentation of the latest window region to obtain the local shock curve includes:

[0019] Based on the latest grayscale features of the window region, k-means clustering is performed to obtain the adaptively segmented image.

[0020] The local shock curve is extracted from the image after adaptive image segmentation using the Sobel operator.

[0021] Optionally, moving the window region in the target image based on the latest geometric orientation of the local shock curve includes:

[0022] A movement vector is generated based on the direction of the line connecting the beginning and end of the latest local shock wave curve;

[0023] Based on the latest position of the window region, the movement vector, and the preset movement distance, the window region is moved within the target image.

[0024] Optionally, the step of calculating the curvature of the complete shock wave curve to obtain the shock wave curvature includes:

[0025] Based on a preset loss function, a cubic smooth spline fit is performed on the complete shock curve to obtain the fitting function; the loss function is: ; where φ j and r j Let f be the coordinates of the j-th discrete point of the complete shock curve, n be the total number of discrete points, f be the fitting function, f′′ be the second derivative of the fitting function, and p be the weight coefficient.

[0026] Based on the fitting function and the preset curvature expression, the shock wave curvature is calculated.

[0027] A second aspect of this invention discloses a shock wave curvature measurement device, the device comprising:

[0028] The acquisition unit is used to acquire the original image obtained through planar laser scattering technology;

[0029] The processing unit is used to perform contrast enhancement and noise reduction on the original image to obtain the target image;

[0030] The selection unit is used to select a window area in the target image by defining a preset shock wave initiation point;

[0031] A segmentation unit is used to perform adaptive image segmentation on the latest window region to obtain a local shock curve;

[0032] The judgment unit is used to determine whether the latest window area contains a preset shock wave termination point;

[0033] The moving unit is configured to, if not, move the window region in the target image based on the latest geometric direction of the local shock curve, and return to perform the step of adaptive image segmentation on the latest window region to obtain the local shock curve.

[0034] The splicing unit is used to splice the various local shock curves to obtain a complete shock curve if the condition is met.

[0035] The calculation unit is used to calculate the curvature of the complete shock wave curve to obtain the shock wave curvature.

[0036] Optionally, the processing unit is specifically used for:

[0037] The original image is contrast-enhanced using Gamma correction technology;

[0038] The original image after contrast enhancement is subjected to Gaussian blurring to obtain the target image.

[0039] Optionally, the segmentation unit is specifically used for:

[0040] Based on the latest grayscale features of the window region, k-means clustering is performed to obtain the adaptively segmented image.

[0041] The local shock curve is extracted from the image after adaptive image segmentation using the Sobel operator.

[0042] Optionally, the moving unit is specifically used for:

[0043] A movement vector is generated based on the direction of the line connecting the beginning and end of the latest local shock wave curve;

[0044] Based on the latest position of the window region, the movement vector, and the preset movement distance, the window region is moved within the target image.

[0045] Optionally, the computing unit is specifically used for:

[0046] Based on a preset loss function, a cubic smooth spline fit is performed on the complete shock curve to obtain the fitting function; the loss function is: ; where φ j and r j Let f be the coordinates of the j-th discrete point of the complete shock curve, n be the total number of discrete points, f be the fitting function, f′′ be the second derivative of the fitting function, and p be the weight coefficient.

[0047] Based on the fitting function and the preset curvature expression, the shock wave curvature is calculated.

[0048] Based on the above embodiments of the present invention, a shock wave curvature measurement method and apparatus are provided, which involves: acquiring an original image obtained through planar laser scattering technology; performing contrast enhancement and noise reduction on the original image to obtain a target image; selecting a window region in the target image based on the shock wave initiation point; performing adaptive image segmentation on the latest window region to obtain a local shock wave curve; moving the window region in the target image based on the geometric direction of the latest local shock wave curve, and returning to perform adaptive image segmentation on the latest window region to obtain the local shock wave curve, until the latest window region contains the shock wave termination point; stitching the local shock wave curves together and calculating the curvature to obtain the shock wave curvature. In this scheme, a strategy combining moving the window with local adaptive image segmentation is adopted to ensure optimal segmentation results in each local region, improving the accuracy of shock wave curve extraction, thereby achieving the goal of improving the accuracy of shock wave curvature measurement. Attached Figure Description

[0049] To more clearly illustrate the technical solutions 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 This is a flowchart of a shock wave curvature measurement method disclosed in an embodiment of the present invention;

[0051] Figure 2 This is an example image of an image after contrast enhancement and noise reduction, as disclosed in an embodiment of the present invention;

[0052] Figure 3 This is a flowchart for determining a movement vector disclosed in an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of a complete shock wave curve disclosed in an embodiment of the present invention;

[0054] Figure 5 This is an image showing the effect of shock wave curvature extraction according to an embodiment of the present invention;

[0055] Figure 6 This is a structural diagram of a shock wave curvature measurement device disclosed in an embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0058] As the background technology indicates, although PLS technology can clearly display the flow field structure including shock waves, existing technologies mainly focus on qualitative display and morphological analysis of the flow field. Furthermore, the low signal-to-noise ratio and uneven background of raw PLS images make accurate identification and continuous tracking of shock wave curves extremely difficult (for example, traditional global image processing methods are prone to fragmentation or distortion of the extracted shock wave curves when faced with background intensity variations caused by uneven tracer particle distribution). This further hinders the accurate extraction of shock wave curvature.

[0059] Therefore, this invention discloses a shock wave curvature measurement method and apparatus. In this scheme, a strategy combining moving window and local adaptive image segmentation is adopted to ensure that the best segmentation effect can be achieved in each local region, thereby improving the accuracy of shock wave curve extraction and thus achieving the goal of improving the accuracy of shock wave curvature measurement.

[0060] like Figure 1 The diagram shown is a flowchart of a shock wave curvature measurement method disclosed in an embodiment of the present invention, which mainly includes the following steps:

[0061] Step S101: Obtain the original image obtained through planar laser scattering technology.

[0062] In step S101, a planar laser particle scattering image containing the target shock wave is obtained through wind tunnel testing and planar laser scattering technology, and this image is used as the original image.

[0063] Step S102: Perform contrast enhancement and noise reduction on the original image to obtain the target image.

[0064] In the specific implementation of step S102, the contrast of the original image is enhanced using Gamma correction technology; the original image after contrast enhancement is then subjected to Gaussian blurring to obtain the target image.

[0065] Preferably, the parameter for Gamma correction is between 1 / 4 and 1 / 3.

[0066] It should be noted that gamma correction is a non-linear operation used to correct image display. Specifically, it transforms the grayscale value of each pixel in the original image (the normalized grayscale value, i.e., the grayscale value ranges from 0 to 1) into... The power is shown in the following formula:

[0067]

[0068] Where I0 is the normalized gray value of the original image, and I1 is the gray value after Gamma correction. When γ is less than 1, the corrected image will be brighter overall (i.e., the corrected gray value will be larger), better highlighting the details in the dark areas of the original image. The smaller γ is, the brighter the corrected image will be overall.

[0069] It should be noted that, because the original image was too dark, Gamma correction was used to improve the contrast. Furthermore, the contrast-enhanced image was subjected to Gaussian blurring, preferably using a 5×5 Gaussian convolution kernel, to remove shot noise and provide a cleaner data foundation for subsequent edge extraction.

[0070] like Figure 2The image shown is an example of an image after contrast enhancement and noise reduction according to an embodiment of the present invention.

[0071] In this embodiment of the invention, the shock wave morphology is approximately circular. Therefore, a polar coordinate system with its origin at the lower left corner of the image is established for subsequent quantitative description of the shock wave curve. The azimuth angle φ of the polar coordinates is defined as the angle with the horizontal direction; the polar radius r is defined as the distance from the origin O.

[0072] Step S103: Select a window area in the target image by defining the preset shock wave initiation point.

[0073] It should be noted that, in order to overcome the background grayscale differences caused by uneven particle distribution in the target image, a moving window is used for regional processing. Regional processing means dividing the target image into multiple local window regions and processing each window independently to eliminate the impact of global background grayscale changes caused by uneven tracer particle distribution on shock wave extraction.

[0074] The first step in region processing is to process the target image (such as...) Figure 2 On the device, the preset shock wave start point and shock wave end point can be roughly specified by the operator, usually the intersection of the shock wave with the boundary of the target image or the test model.

[0075] Preferably, the size of the window area is 5% to 15% of the target image size.

[0076] For example, the shock wave initiation point can be used as the center point of the window area's edge to select the area, ensuring that the window area contains as much shock wave as possible. Figure 2 As shown in the upper left corner, the left boundary of the shock wave is taken as the midpoint of the left side line of the window area.

[0077] Step S104: Perform adaptive image segmentation on the latest window region to obtain the local shock curve.

[0078] In step S104, adaptive image segmentation is achieved by clustering the pixels within the current window region into two clusters based on their grayscale features.

[0079] In the specific implementation of step S104, k-means clustering (k=2) is performed based on the grayscale features of the latest window region to obtain the image after adaptive image segmentation; the local shock curve is extracted from the image after adaptive image segmentation using the Sobel operator.

[0080] The Sobel operator is a discrete differential operator used in image processing specifically for edge detection. It performs convolution operations with the image using two 3x3 convolution kernels (corresponding to the horizontal and vertical directions respectively) to approximate the gradient of image brightness, thereby quickly identifying regions in the image with drastic gray-level changes (i.e., edges). This edge is the shock curve.

[0081] It should be noted that the window area will move along the shock wave curve, and the latest window area is the window area in the latest position. It may be the first window area selected in step S103, or it may be the window area moved in step S106.

[0082] Step S105: Determine whether the latest window area contains the preset shock wave end point; if yes, proceed to step S107; otherwise, proceed to step S106.

[0083] It is easy to understand that when the latest window area contains the preset shock wave end point, it can be determined that the window area has moved along the entire shock wave curve.

[0084] Step S106: Based on the latest geometric direction of the local shock curve, move the window region in the target image and return to execute step S104.

[0085] In step S106, a movement vector is generated based on the direction of the line connecting the beginning and end points of the latest local shock curve, and the window area is moved a preset distance along the direction of the movement vector.

[0086] like Figure 3 The diagram shown is a flowchart of a method for determining a movement vector according to an embodiment of the present invention.

[0087] In the specific implementation of step S106, a movement vector is generated based on the direction of the first and last line of the latest local shock curve; the window region is moved in the target image based on the latest window region position, movement vector and preset movement distance.

[0088] Preferably, the preset moving distance is 40% to 60% of the window area size.

[0089] Step S107: Segment the individual local shock curves to obtain the complete shock curve.

[0090] like Figure 4 The diagram shown is a schematic representation of a complete shock wave curve disclosed in an embodiment of the present invention.

[0091] In step S107, each local shock curve is a local shock curve extracted from a window region at a different location.

[0092] Step S108: Calculate the curvature of the complete shock curve to obtain the shock curvature.

[0093] In the specific implementation of step S108, the complete shock curve is fitted with a cubic smooth spline based on a preset loss function to obtain the fitting function; the shock curvature is calculated based on the fitting function and the preset curvature expression.

[0094] like Figure 5 The image shown is an illustration of the shock wave curvature extraction effect disclosed in an embodiment of the present invention.

[0095] In other words, a cubic smooth spline is used to globally fit the extracted complete shock curve, and then a preset curvature expression is used to calculate the shock curvature of the fitted shock curve (i.e., the fitting function or the fitted curve).

[0096] In this embodiment of the invention, during the global fitting of the extracted shock curve using cubic smooth splines, a second derivative regularization term is introduced to minimize the change in the second derivative of the fitted curve, thereby ensuring the smoothness of the curvature distribution.

[0097] Specifically, optimizing the parameters of the cubic smoothed spline minimizes the value of the loss function L. In Cartesian coordinates, L is expressed as:

[0098]

[0099] Where x j and y j Here, x represents the coordinates of the j-th discrete point of the extracted complete shock curve, n is the total number of discrete points, f is the fitted function, f′′ is the second derivative of the fitted function, and x is the coordinates of the j-th discrete point of the extracted complete shock curve. min The minimum value of the x-coordinate of a discrete point, x max Let p be the maximum value of the x-coordinate of the discrete point, and p be the weighting coefficient. When p = 1, the fitted curve passes through every discrete point of the shock wave, and the weight of the second derivative regularization term is 0; when p = 0, only the second derivative regularization term is considered, and the fitted curve is the smoothest.

[0100] In polar coordinates, the expression for the loss function L is:

[0101]

[0102] Where, φ j and r j Let f be the coordinates of the j-th discrete point of the complete shock curve, n be the total number of discrete points, f be the fitting function, f′′ be the second derivative of the fitting function, and p be the weight coefficient.

[0103] Specifically, the curvature expression in Cartesian coordinates is:

[0104]

[0105] Where κ is curvature, f is the fitting function, f′ is the first derivative of the fitting function, and f′′ is the second derivative of the fitting function.

[0106] The curvature expression in polar coordinates is:

[0107]

[0108] Where κ represents curvature, f is the fitting function, f′ is the first derivative of the fitting function, and f′′ is the second derivative of the fitting function. Spatial calibration is performed using an object of known size in the image (such as an experimental model) to convert pixel curvature into physical curvature. Finally, the extracted shock wave curvature distribution is as follows: Figure 5 As shown.

[0109] The beneficial effects of the shock wave curvature measurement method disclosed in the above embodiments of the present invention are as follows: The present invention overcomes the interference of flow field inhomogeneity, and the extraction results are robust. Traditional global image processing methods are prone to breakage or distortion of the extracted shock wave curve when faced with background intensity changes caused by uneven distribution of tracer particles. The present invention adopts a strategy combining moving window and local adaptive image segmentation, and through the k-means clustering algorithm, ensures that the best segmentation effect can be achieved in each local region, thereby greatly improving the accuracy and robustness of shock wave extraction. The present invention realizes the automation and intelligence of the shock wave extraction process, with high efficiency and small human error. The window moving mechanism based on vector advancement proposed in the present invention enables the algorithm to automatically track along the geometric direction of the shock wave without manual intervention until the process ends. This greatly improves processing efficiency, avoids errors introduced by subjective judgment, and ensures the objectivity and repeatability of measurement results.

[0110] Corresponding to the shock wave curvature measurement method disclosed in the above embodiments of the present invention, such as Figure 6 The diagram shown is a structural diagram of a shock wave curvature measurement device disclosed in an embodiment of the present invention. The device includes: an acquisition unit 601, a processing unit 602, a selection unit 603, a segmentation unit 604, a judgment unit 605, a moving unit 606, a splicing unit 607, and a calculation unit 608.

[0111] The acquisition unit 601 is used to acquire the original image obtained by planar laser scattering technology;

[0112] Processing unit 602 is used to perform contrast enhancement and noise reduction on the original image to obtain the target image;

[0113] The selection unit 603 is used to select a window area in the target image by defining a preset shock wave initiation point;

[0114] The segmentation unit 604 is used to perform adaptive image segmentation on the latest window region to obtain the local shock curve;

[0115] The judgment unit 605 is used to determine whether the latest window area contains a preset shock wave end point;

[0116] The moving unit 606 is used to, if not, move a window region in the target image based on the geometric direction of the latest local shock curve, and return to perform adaptive image segmentation on the latest window region to obtain the local shock curve, until the latest window region contains a preset shock end point.

[0117] The splicing unit 607 is used to splice the various local shock curves to obtain a complete shock curve if the condition is met.

[0118] The calculation unit 608 is used to calculate the curvature of the complete shock curve to obtain the shock curvature.

[0119] Optionally, the processing unit 602 is specifically used for:

[0120] Contrast enhancement is achieved using Gamma correction technology on the original image;

[0121] The original image after contrast enhancement is subjected to Gaussian blurring to obtain the target image.

[0122] Optionally, segmentation unit 604 is specifically used for:

[0123] k-means clustering is performed based on the grayscale features of the latest window region to obtain the adaptive image segmentation image.

[0124] The Sobel operator is used to extract local shock curves from images after adaptive image segmentation.

[0125] Optionally, the moving unit 606 is specifically used for:

[0126] A movement vector is generated based on the direction of the line connecting the beginning and end of the latest local shock wave curve;

[0127] Based on the latest window region position, movement vector, and preset movement distance, the window region is moved in the target image.

[0128] Optionally, computing unit 608 is specifically used for:

[0129] Based on a preset loss function, a cubic smooth spline fit is performed on the complete shock wave curve to obtain the fitting function; the loss function is: ; where φ j and r jLet f be the coordinates of the j-th discrete point of the complete shock curve, n be the total number of discrete points, f be the fitting function, f′′ be the second derivative of the fitting function, and p be the weight coefficient.

[0130] The shock wave curvature is calculated based on the fitting function and the preset curvature expression.

[0131] Based on the above embodiments of the present invention, a shock wave curvature measurement device is disclosed. In this solution, a strategy combining moving window and local adaptive image segmentation is adopted to ensure that the best segmentation effect can be achieved in each local area, thereby improving the accuracy of shock wave curve extraction and thus achieving the goal of improving the accuracy of shock wave curvature measurement.

[0132] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0133] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0134] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring shock wave curvature, characterized in that, The method includes: Acquire the original image obtained through planar laser scattering technology; The original image is subjected to contrast enhancement and noise reduction to obtain the target image; A window area is selected by defining a preset shock wave initiation point in the target image; Adaptive image segmentation is performed on the latest window region to obtain the local shock curve; Determine whether the latest window area contains a preset shock wave termination point; If not, then based on the latest geometric direction of the local shock curve, the window region is moved in the target image, and the process returns to perform adaptive image segmentation on the latest window region to obtain the local shock curve. If so, the individual local shock curves are spliced ​​together to obtain a complete shock curve; The curvature of the complete shock wave curve is calculated to obtain the shock wave curvature.

2. The method according to claim 1, characterized in that, The process of enhancing contrast and reducing noise in the original image to obtain the target image includes: The original image is contrast-enhanced using Gamma correction technology; The original image after contrast enhancement is subjected to Gaussian blurring to obtain the target image.

3. The method according to claim 1, characterized in that, The step of adaptively segmenting the latest window region to obtain the local shock curve includes: Based on the latest grayscale features of the window region, k-means clustering is performed to obtain the adaptively segmented image. The local shock curve is extracted from the image after adaptive image segmentation using the Sobel operator.

4. The method according to claim 1, characterized in that, The step of moving the window region in the target image based on the latest geometric direction of the local shock curve includes: A movement vector is generated based on the direction of the line connecting the beginning and end of the latest local shock wave curve; Based on the latest position of the window region, the movement vector, and the preset movement distance, the window region is moved within the target image.

5. The method according to any one of claims 1 to 4, characterized in that, The curvature calculation of the complete shock wave curve to obtain the shock wave curvature includes: Based on a preset loss function, a cubic smooth spline fit is performed on the complete shock curve to obtain the fitting function; the loss function is: ; where φ j and r j Let f be the coordinates of the j-th discrete point of the complete shock curve, n be the total number of discrete points, f be the fitting function, f′′ be the second derivative of the fitting function, and p be the weight coefficient. Based on the fitting function and the preset curvature expression, the shock wave curvature is calculated.

6. A shock wave curvature measuring device, characterized in that, The device includes: The acquisition unit is used to acquire the original image obtained through planar laser scattering technology; The processing unit is used to perform contrast enhancement and noise reduction on the original image to obtain the target image; The selection unit is used to select a window area in the target image by defining a preset shock wave initiation point; A segmentation unit is used to perform adaptive image segmentation on the latest window region to obtain a local shock curve; The judgment unit is used to determine whether the latest window area contains a preset shock wave termination point; The moving unit is configured to, if not, move the window region in the target image based on the latest geometric direction of the local shock curve, and return to perform the step of adaptive image segmentation on the latest window region to obtain the local shock curve. The splicing unit is used to splice the various local shock curves to obtain a complete shock curve if the condition is met. The calculation unit is used to calculate the curvature of the complete shock wave curve to obtain the shock wave curvature.

7. The apparatus according to claim 6, characterized in that, The processing unit is specifically used for: The original image is contrast-enhanced using Gamma correction technology; The original image after contrast enhancement is subjected to Gaussian blurring to obtain the target image.

8. The apparatus according to claim 6, characterized in that, The segmentation unit is specifically used for: Based on the latest grayscale features of the window region, k-means clustering is performed to obtain the adaptively segmented image. The local shock curve is extracted from the image after adaptive image segmentation using the Sobel operator.

9. The apparatus according to claim 6, characterized in that, The moving unit is specifically used for: A movement vector is generated based on the direction of the line connecting the beginning and end of the latest local shock wave curve; Based on the latest position of the window region, the movement vector, and the preset movement distance, the window region is moved within the target image.

10. The apparatus according to any one of claims 6 to 9, characterized in that, The computing unit is specifically used for: Based on a preset loss function, a cubic smooth spline fit is performed on the complete shock curve to obtain the fitting function; the loss function is: ; where φ j and r j Let f be the coordinates of the j-th discrete point of the complete shock curve, n be the total number of discrete points, f be the fitting function, f′′ be the second derivative of the fitting function, and p be the weight coefficient. Based on the fitting function and the preset curvature expression, the shock wave curvature is calculated.

Images