A measurement method and device for segregation frost heaving deformation field

Abstract

This invention discloses a method and apparatus for measuring the frost heave deformation field, belonging to the field of frozen soil measurement technology. A specific implementation of the method includes: first, acquiring all grayscale images of the target frozen soil area before and during frost heave, forming a first grayscale image sequence; wherein the grayscale images have acquisition time labels; second, for any grayscale image in the first grayscale image sequence: performing image processing to obtain a quasi-analysis image; then, determining a reference time sequence based on a second grayscale image sequence formed from several quasi-analysis images; finally, based on the triggering of the reference time in the reference time sequence, performing an update operation on the reference image in the DIC analysis process of the second grayscale image sequence to obtain the frost heave deformation field. Therefore, without using tracer particles, non-contact measurement of the frost heave deformation field of fine-grained soil during the frost heave process can be performed, improving measurement accuracy.

Description

Technical Field

[0001] This invention belongs to the field of frozen soil measurement technology, and particularly relates to a method and device for measuring condensation and frost heave deformation fields. Background Technology

[0002] my country is a country with a large permafrost area, with perennial permafrost and seasonal permafrost accounting for 22.4% and 53.5% of the total land area, respectively. In recent years, major economic initiatives in my country, such as the West-to-East Gas Pipeline, the West-to-East Electricity Transmission Project, the China-Europe Railway Express, and the China-Russia Oil Pipeline, have all involved the construction of foundation engineering projects that traverse large areas of permafrost and seasonal permafrost. The frost heave and thaw settlement of permafrost in cold regions have led to the constant damage and repair of foundation engineering projects in cold regions of my country, which has had a significant impact on my country's economic construction and national defense infrastructure construction. In order to quantify the deformation characteristics of permafrost in cold regions and the impact of permafrost deformation on foundation structures, there are many existing methods. The first method is to use indoor model tests to simulate the deformation process of permafrost in cold regions and set up several displacement measurement points to reflect the deformation characteristics of permafrost in each region through displacement gauges. This method has the following obvious drawbacks: (1) The number of displacement gauges is limited, and it can only reflect the deformation of permafrost surface or permafrost at a specific depth, which belongs to the scope of local measurement; moreover, the measurement data is small and the universality is limited; (2) It is impossible to conduct continuous and non-destructive measurement of the growth process of permafrost condensation. The second method uses digital image analysis to calculate the thickness of the frozen layer. However, this method relies on manual thresholding of digital images to calculate the thickness, making it labor-intensive and susceptible to automation and human error. The third method is digital image correlation (DIC), a non-contact, full-field deformation observation method widely used in mechanical structures and fluid mechanics experiments. The core algorithm of DIC is the digital image normalized cross-correlation algorithm. During the experiment, the deformation field is calculated by analyzing the changes in the cross-correlation peak positions of grayscale images before and after deformation. However, this method requires the surface of the object under test to have obvious inherent features to obtain clear correlation peaks. Therefore, directly applying DIC to fine-grained permafrost presents the following challenges: fine-grained soil surfaces lack obvious surface texture, making it impossible to obtain stable correlation peaks; the deformation process of fine-grained permafrost involves a water-ice phase transition, and the resulting grayscale changes affect the accuracy of the correlation peak search.

[0003] To address the limitations of the DIC method, existing solutions include the artificial soil substitution method, the monochromatic tracer particle method, and the two-color tracer particle method. The artificial soil substitution method primarily uses finely ground monochromatic quartz sand to create artificial transparent soil for frost heave analysis. This type of soil has a clear surface texture and can be directly used for non-contact measurements. The monochromatic tracer particle method mainly uses large-particle quartz sand of a single color as tracer particles to enhance the surface texture of the soil, allowing the displacement field on the soil surface to be inverted. The two-color tracer particle method involves arranging a layer of black and white graded quartz sand as tracer particles on the soil image acquisition surface to reflect the frost heave deformation field of the frozen soil.

[0004] However, the artificial soil substitution method has the following drawbacks: artificial soil has limited properties, and its particle shape and gradation differ significantly from natural frozen soil. Therefore, the frost heave test results of artificial soil cannot objectively characterize the segregation deformation characteristics of naturally occurring frozen soil. The monochromatic tracer particle method has the following drawbacks: monochromatic tracer particles cannot effectively avoid the grayscale changes caused by pore water freezing, leading the DIC algorithm to identify the downward movement of the freezing front as a displacement change, thus causing measurement errors. The two-color tracer particle method has the following drawbacks: two-color tracer particles can effectively resist image grayscale changes caused by pore water phase transitions to some extent, but the thickness of the tracer particles requires strict control, and the operation process is complex. Furthermore, the large-scale incorporation of tracer particles can cause changes in the properties of the soil being tested, failing to effectively reflect the original frost heave characteristics. Therefore, given the shortcomings of existing non-contact measurement methods for the segregation deformation of fine-grained frozen soil, there is an urgent need to provide an effective non-contact measurement method to improve the measurement accuracy of the deformation field of fine-grained soil. Summary of the Invention

[0005] This invention provides a method and apparatus for measuring the condensation and frost heave deformation field of frozen soil. This method can effectively measure the condensation and frost heave deformation field of frozen soil without adding any tracer particles, thus improving the accuracy of non-contact measurement.

[0006] To achieve the above objectives, according to a first aspect of the present application, a method for measuring the condensation and frost heave deformation field is provided. The method includes: acquiring all grayscale images of a target frozen soil region before and during condensation and frost heave, forming a first grayscale image sequence; wherein the grayscale images have acquisition time labels; for any grayscale image in the first grayscale image sequence: performing image processing on the grayscale image to obtain a quasi-analysis image; determining a reference time sequence based on a second grayscale image sequence formed by several quasi-analysis images; and performing an update operation on the reference image in the DIC analysis process of the second grayscale image sequence based on the triggering of a reference time in the reference time sequence to obtain the condensation and frost heave deformation field.

[0007] Optionally, determining the reference time series based on the second grayscale image sequence formed by several quasi-analysis images includes: for any quasi-analysis image: calculating the crack area of ​​each crack region in the quasi-analysis image; determining the total crack area corresponding to the quasi-analysis image based on the several crack areas; constructing a crack area-time curve based on the total crack area corresponding to each quasi-analysis image in the second grayscale image sequence and the acquisition time; and determining the time corresponding to the inflection point of the crack area-time curve as the reference time to obtain the reference time series.

[0008] Optionally, the step of performing an update operation on the reference image in the DIC analysis process of the second grayscale image sequence based on the triggering of the reference time in the reference time series to obtain the segregation and frost heave deformation field includes: selecting a grayscale image corresponding to the reference time from the second grayscale image sequence based on the triggering of the reference time in the reference time series; or, selecting a grayscale image corresponding to a time before and adjacent to the reference time from the second grayscale image sequence based on the triggering of the reference time in the reference time series; using the selected grayscale image as the current reference image in the DIC analysis process of the second grayscale image sequence to obtain the segregation and frost heave deformation field.

[0009] Optionally, the step of performing image processing on the grayscale image to obtain a quasi-analysis image includes: performing image processing on the grayscale image to obtain a pre-analysis image; determining the convolution kernel size information for image convolution processing; and performing opening and closing operations on the pre-analysis image based on the convolution kernel to obtain the quasi-analysis image.

[0010] Optionally, image processing is performed on the grayscale image to obtain a pre-analysis image; including: performing histogram equalization on the grayscale image to obtain a processed image; dividing the processed image into several target regions; for any one of the target regions: performing threshold filtering on the target region to obtain a filtered image; and stitching together several filtered images to generate a pre-analysis image.

[0011] Optionally, the step of threshold filtering the target region to obtain a filtered image includes: acquiring all grayscale values, the average grayscale value, and the maximum grayscale value of the target region; for any grayscale value: determining whether the grayscale value is greater than the average grayscale value; if the determination result indicates that the grayscale value is greater than the average grayscale value, then updating the grayscale value to the maximum grayscale value of the target region; if the determination result indicates that the grayscale value is not greater than the average grayscale value, then updating the grayscale value to zero; and obtaining a filtered image based on the update results of all grayscale values ​​of the target region.

[0012] Optionally, the step of acquiring all grayscale images of the frozen soil target area before and during frost heave to form a first grayscale image sequence includes: acquiring a first original grayscale image of the frozen soil before frost heave, and all second original grayscale images of the frozen soil during frost heave; extracting images of the same target area from the first original grayscale image and each of the second original grayscale images to obtain several grayscale images; for any grayscale image: acquiring the acquisition time of the grayscale image; labeling the grayscale image based on the acquisition time to generate a grayscale image with acquisition time labels; arranging the several labeled grayscale images in chronological order of the acquisition time to generate a first grayscale image sequence.

[0013] To achieve the above objectives, according to a second aspect of the present application, a measuring device for condensation and frost heave deformation fields is provided. The device includes: an acquisition module, configured to acquire all grayscale images of a target frozen soil region before and during condensation and frost heave, forming a first grayscale image sequence; wherein the grayscale images have acquisition time labels; an image processing module, configured to perform image processing on any grayscale image in the first grayscale image sequence to obtain a quasi-analysis image; a determination module, configured to determine a reference time sequence based on a second grayscale image sequence formed by several quasi-analysis images; and an update module, configured to perform an update operation on the reference image in the DIC analysis process of the second grayscale image sequence based on the triggering of a reference time in the reference time sequence, to obtain the condensation and frost heave deformation field.

[0014] Optionally, the determining module includes: a calculation unit, configured to, for any quasi-analysis image: calculate the crack area of ​​each crack region in the quasi-analysis image; determine the total crack area corresponding to the quasi-analysis image based on several crack areas; a curve construction unit, configured to construct a crack area-time curve based on the total crack area and acquisition time corresponding to each quasi-analysis image in the second grayscale image sequence; and a determining unit, configured to determine the time corresponding to the inflection point of the crack area-time curve as a reference time, thereby obtaining a reference time series.

[0015] To achieve the above objectives, a computer-readable medium is provided according to a third aspect of the present application, having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in the first aspect.

[0016] Compared with existing technologies, this invention provides a method and apparatus for measuring the frost heave deformation field. The method includes: first, acquiring all grayscale images of a target frozen soil area before and during frost heave, forming a first grayscale image sequence; wherein the grayscale images have acquisition time tags; second, performing image processing on any grayscale image in the first grayscale image sequence to obtain a quasi-analysis image; then, determining a reference time sequence based on a second grayscale image sequence formed from several quasi-analysis images; finally, based on the triggering of a reference time in the reference time sequence, performing an update operation on the reference image in the DIC analysis process of the second grayscale image sequence to obtain the frost heave deformation field. Therefore, without using tracer particles, non-contact measurement of the frost heave deformation field of fine-grained soil during frost heave can be performed, improving measurement accuracy. Attached Figure Description

[0017] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0018] Figure 1 This is a flowchart illustrating a method for measuring the segregation frost heave deformation field according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the process for determining a reference time series in one embodiment of the present invention;

[0020] Figure 3 The figures above are grayscale images and corresponding grayscale histograms before and after the segregation and frost heave deformation of this invention, without histogram equalization processing. Figure a shows the grayscale image before segregation and frost heave; Figure b shows the grayscale image after segregation and frost heave; and Figure c shows the grayscale histogram.

[0021] Figure 4 The images and corresponding grayscale histograms are shown below, illustrating the process before and after the segregation and freeze-thaw deformation of the present invention. Figure a represents the grayscale image before segregation and freeze-thaw deformation; Figure b represents the grayscale image after segregation and freeze-thaw deformation; and Figure c represents the grayscale histogram.

[0022] Figure 5 The images show the filter diagrams and corresponding grayscale histograms before and after the segregation and freeze-thaw deformation of the present invention; wherein, Figure a shows the filter diagram before segregation and freeze-thaw; Figure b shows the filter diagram after segregation and freeze-thaw; and Figure c shows the grayscale histogram.

[0023] Figure 6 These are quasi-analytical images of the present invention before and after condensation and frost heave deformation;

[0024] Figure 7This is the crack area-time curve corresponding to the second grayscale image sequence of the present invention;

[0025] Figure 8 This is a schematic diagram illustrating the correspondence between the reference image and the current image being analyzed in the traditional DIC analysis method.

[0026] Figure 9 This is a schematic diagram illustrating the correspondence between a reference image and the currently analyzed image in the method of this embodiment of the invention;

[0027] Figure 10 These are schematic diagrams of the condensation frost heave deformation fields measured using the traditional DIC analysis method and the method described in this embodiment of the invention, respectively.

[0028] Figure 11 This is a schematic diagram of the structure of a measuring device for condensation and frost heave deformation field provided in an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0030] like Figure 1 The diagram shown is a flowchart illustrating a method for measuring the condensation and frost heave deformation field according to an embodiment of the present invention.

[0031] A method for measuring the frost heave deformation field during segregation, the method comprising at least the following steps:

[0032] S101, acquire all grayscale images of the target frozen soil area before condensation and frost heave and during condensation and frost heave, respectively, to form the first grayscale image sequence; wherein, the grayscale images have acquisition time labels;

[0033] S102, For any grayscale image in the first grayscale image sequence: Perform image processing on the grayscale image to obtain a quasi-analysis image;

[0034] S103, Determine the reference time series based on the second grayscale image sequence formed by several quasi-analysis images;

[0035] S104, based on the triggering of the reference time in the reference time series, performs an update operation on the reference image in the DIC analysis process of the second grayscale image sequence to obtain the segregation and frost heave deformation field.

[0036] In S101, the grayscale images of the frozen soil target area before condensation and frost heave and all grayscale images of the frozen soil target area during condensation and frost heave are arranged in chronological order of acquisition time to generate the first grayscale image sequence.

[0037] Here, each grayscale image in the first grayscale image sequence can also be numbered according to the order of acquisition time. For example, the first grayscale image in the first grayscale image sequence can be numbered as img0001, the second grayscale image can be numbered as img0001, where img is the prefix of the number.

[0038] It should be noted that when taking grayscale images of the target area of ​​permafrost, the resolution of the target area should be higher than 600×2500 pixels, and care should be taken to avoid obvious halos in the target area when taking pictures.

[0039] In S102, for any grayscale image in the first grayscale image sequence: the grayscale image is subjected to histogram equalization to obtain a quasi-analysis image; or, the grayscale image is subjected to histogram equalization and then threshold filtering to obtain a quasi-analysis image; or, the grayscale image is subjected to histogram equalization and then threshold filtering to obtain a pre-analysis image; then, the pre-analysis image is subjected to image convolution to obtain a quasi-analysis image.

[0040] Therefore, image processing of the grayscale images in the first grayscale image sequence can effectively filter and highlight the crack areas caused by condensation and frost heave, thus facilitating DIC analysis.

[0041] In step S103, the second grayscale image sequence is input into the trained recognition model. The recognition model analyzes the image features of the quasi-analyzable image in the second grayscale image sequence and outputs the previous acquisition time where the image features change significantly as the reference time. This allows for accurate determination of the reference time for updating the reference image during the DIC analysis process of the second grayscale image sequence, improving the accuracy of calculating the condensation and frost heave deformation field.

[0042] In S104, during the DIC analysis of the second grayscale image sequence, based on the triggering of the reference time in the reference time series, a quasi-analysis image corresponding to the reference time is selected from the second grayscale image sequence; the selected quasi-analysis image is used as the current reference image for the DIC analysis; thus, based on the triggering of each reference time in the reference time series, the current reference image in the DIC analysis process is continuously updated; after performing DIC analysis on all quasi-analysis images in the second grayscale image sequence, the segregation and frost heave deformation field is finally generated.

[0043] It should be noted that since there are several reference times in the reference time series, the update operation of the current reference image triggered based on the reference time also needs to be performed several times.

[0044] This embodiment performs image processing on the grayscale images in the first grayscale image sequence to enhance the crack characteristics of condensation and frost heave. Then, based on the changes in crack characteristics of the quasi-analytical images in the second grayscale image sequence, a reference time series is determined for updating the reference image during the DIC analysis process. Finally, based on the triggering of the reference time in the reference time series, a quasi-analytical image corresponding to the reference time is selected from the second grayscale image sequence as the current reference image during the DIC analysis process. Therefore, this embodiment updates the reference image promptly when image features change significantly, helping to reduce the feature differences between the reference image and the current analysis image, resulting in a clear correlation peak in the cross-correlation matrix. This facilitates the search for the condensation and frost heave deformation field and improves the accuracy of condensation and frost heave field measurement.

[0045] like Figure 2 The diagram shown is a flowchart illustrating the process of determining a reference time series in one embodiment of the present invention.

[0046] In a preferred embodiment of this first example, determining the reference time series includes at least the following steps:

[0047] S201, For any quasi-analysis image: Calculate the crack area of ​​each crack region in the quasi-analysis image; Determine the total crack area corresponding to the quasi-analysis image based on several crack areas;

[0048] S202, construct the crack area-time curve based on the total crack area and acquisition time corresponding to each quasi-analysis image in the second grayscale image sequence;

[0049] S203, the time corresponding to the inflection point of the crack area-time curve is determined as the reference time, and the reference time series is obtained.

[0050] Specifically, for any quasi-analysis image: the crack area of ​​each closed crack region in the quasi-analysis image is statistically calculated using an image convolution algorithm; and the crack areas corresponding to all closed crack regions are summed to obtain the total crack area Sc (unit: pixel) of the quasi-analysis image. 2 Based on the total crack area and corresponding acquisition time of each quasi-analysis image in the second grayscale image sequence, a crack area-time curve (Sc-t curve) is plotted. The second derivative of the Sc-t curve is calculated, and the time corresponding to the second derivative being equal to 0 is determined as the reference time.

[0051] The inflection point of the Sc-t curve represents the moment after which the image crack characteristics begin to change. For example, during freezing, new segregation cracks will appear; during thawing, some segregation cracks will completely close and disappear. During the segregation and frost heave process of frozen soil, because these image feature changes are quite significant, when performing DIC analysis on the second grayscale image sequence, it is necessary to update the reference image promptly at the moment when the image features change significantly. This helps to reduce the feature differences between the reference image and the current analysis image, resulting in a clear correlation peak in the cross-correlation matrix. This facilitates the search for the segregation and frost heave deformation field and improves the accuracy of the segregation and frost heave field measurement.

[0052] Traditional methods do not update the reference image. Since the grayscale image before condensation and frost heave is used as the reference image throughout the DIC analysis process, the feature difference between the reference image and the current analysis image is too large, which causes the peak of the cross-correlation matrix to disappear, and thus leads to the failure of condensation and frost heave deformation field measurement.

[0053] Since the time point at which the concavity / convexity of the Sc-t curve changes significantly is the same time at which the image features of the frozen soil target area change significantly, this embodiment determines the time point at which the concavity / convexity of the Sc-t curve corresponding to the second grayscale image sequence changes significantly as the reference time, thereby improving the accuracy of the reference time determination.

[0054] In another preferred embodiment of this invention, the process of performing an update operation on the reference image during the DIC analysis of the second grayscale image sequence to obtain the segregation and frost heave deformation field includes at least the following steps:

[0055] S301, based on the triggering of the reference time in the reference time series, select the grayscale image corresponding to the reference time from the second grayscale image series;

[0056] S302, the selected grayscale image is used as the current reference image in the DIC analysis process of the second grayscale image sequence.

[0057] or,

[0058] S301, Based on the triggering of the reference time in the reference time series, select the grayscale image corresponding to the time that is before the reference time and adjacent to the reference time from the second grayscale image series;

[0059] S302, the selected grayscale image is used as the current reference image in the DIC analysis process of the second grayscale image sequence.

[0060] Specifically, based on the triggering of the reference time in the reference time series, the reference time is used as a time index to query whether there is a grayscale image corresponding to the reference time in the second grayscale image series; if it exists, the grayscale image corresponding to the reference time is selected from the second grayscale image series; if it does not exist, the grayscale image corresponding to the acquisition time that is before and adjacent to the reference time is selected from the second grayscale image series; the selected grayscale image is used as the current reference image in the DIC analysis process of the second grayscale image series.

[0061] In this embodiment, grayscale images related to the reference time are selected from the second grayscale image sequence based on the triggering of the reference time, and the selected grayscale images are used as the current reference images in the DIC analysis process of the second grayscale image sequence. This reduces the feature differences between the reference images and the current analysis images in the DIC analysis process, so that the cross-correlation matrix has obvious correlation peaks, which facilitates the search for the condensation and frost heave deformation field.

[0062] In another preferred embodiment of this invention, acquiring a quasi-analysis image includes at least the following steps:

[0063] S401, perform image processing on the grayscale image to obtain a pre-analyzed image;

[0064] S402, Determine the convolution kernel size information for image convolution processing;

[0065] S403 performs opening and closing operations on the pre-analyzed image based on the convolution kernel to obtain the quasi-analyzed image.

[0066] Specifically, the crack length of the longest segregation crack and the crack width of the narrowest segregation crack are obtained in the pre-analysis image. One-quarter of the obtained crack length is used as the major axis of the elliptical convolution kernel, for example, a pixel; and the obtained crack width is used as the minor axis of the elliptical convolution kernel, for example, bpixel. Thus, the size information of the elliptical convolution kernel is determined based on the major and minor axes, which can effectively use the convolution kernel to filter the pre-analysis image and highlight the crack area caused by segregation, thereby facilitating the DIC analysis of the second grayscale image sequence in the later stage.

[0067] In another preferred embodiment of this example, image processing of the grayscale image to obtain a pre-analysis image includes at least the following steps: S1, performing histogram equalization on the grayscale image to obtain a processed image; S2, dividing the processed image into several target regions; S3, for any target region, performing threshold filtering on the target region to obtain a filtered image; S4, stitching together the several filtered images to generate a pre-analysis image.

[0068] Specifically, the histogram equalization algorithm does not change the displacement information of the original grayscale image itself, but it will fill the entire grayscale value space, i.e., 0-255, with the grayscale distribution curve of the grayscale image, thereby enhancing the contrast of the grayscale image and highlighting the details of the grayscale image.

[0069] The formula used in histogram equalization of grayscale images is shown in the figure. Assume f is a matrix of a grayscale image with size m. r *m c The values ​​of each element in the matrix range from 0 to L-1, and L is set to 255 in image analysis.

[0070]

[0071]

[0072] Where p is the normalized histogram of f, n is any integer in the range of 0 to L-1, and p n This represents the frequency of occurrence of pixels with a grayscale value of n. `floor()` is the function for rounding down to the nearest integer. `g` is the histogram-equalized image, and `i` and `j` are the indices of the pixels in the image.

[0073] Histogram equalization can effectively reduce the difference in grayscale feature distribution in the target area of ​​frozen soil before and after condensation and frost heave deformation by using histogram equalization algorithm on grayscale images.

[0074] Furthermore, threshold filtering is applied to the target region to obtain a filtered image, including at least the following steps: obtaining all grayscale values, the average grayscale value, and the maximum grayscale value of the target region; for any grayscale value: determining whether the grayscale value is greater than the average grayscale value; if the determination result indicates that the grayscale value is greater than the average grayscale value, then updating the grayscale value to the maximum grayscale value of the target region; if the determination result indicates that the grayscale value is not greater than the average grayscale value, then updating the grayscale value to zero; and obtaining the filtered image based on the update results of all grayscale values ​​of the target region.

[0075] An adaptive thresholding algorithm is used to filter the target region, and the specific formula is shown in the figure below:

[0076]

[0077] Where m' and m represent the pixel matrices of the original image and the image after threshold filtering, respectively. (x, y) is the position index of each pixel in the matrix. α is a non-zero integer, and 255 is recommended. T is the pixel threshold; when the pixel value is higher than this threshold, the pixel value is adjusted.

[0078] Therefore, filtering the processed image can not only weaken the influence of the ice-water phase transition, but also clearly show the segregation crack characteristics of the frozen soil in the filtered image, which is beneficial for the statistical analysis of the area of ​​the closed crack in the later stage.

[0079] In another preferred embodiment of this example, all grayscale images of the target frozen soil area before and during frost heave are obtained to form a first grayscale image sequence, including at least the following steps:

[0080] S1, obtain the first original grayscale image of the frozen soil before condensation and frost heave, and all the second original grayscale images of the frozen soil during the condensation and frost heave process.

[0081] S2, extract images of the same target region from the first original grayscale image and each of the second original grayscale images respectively to obtain several grayscale images;

[0082] S3, for any grayscale image: obtain the acquisition time of the grayscale image; label the grayscale image based on the acquisition time to generate a grayscale image with acquisition time labels;

[0083] S4. Arrange several labeled grayscale images in chronological order of acquisition time to generate the first grayscale image sequence.

[0084] It should be understood that, in the various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0085] Currently, in the measurement of frost heave deformation fields, grayscale images before frost heave are often used as reference images. Direct cross-correlation analysis is performed on all grayscale images during the frost heave process, and the frost heave deformation field of frozen soil is calculated based on the peak position of the cross-correlation matrix. However, during the frost heave process of fine-grained soil, one or more frost cracks with continuously varying widths often occur. This results in significant differences in features between the reference image and the currently analyzed image in a pair of images for cross-correlation analysis, making it impossible to effectively obtain the frost heave deformation field.

[0086] The method of this embodiment will be described in detail below with reference to specific applications. The specific process is as follows.

[0087] S1: Obtain the first original grayscale image of the frozen soil before condensation and frost heave, and all the second original grayscale images of the frozen soil during condensation and frost heave; extract images of the same target area from the first original grayscale image and each second original grayscale image to obtain several grayscale images; for any grayscale image: obtain the acquisition time of the grayscale image; label the grayscale image based on the acquisition time to generate a grayscale image with acquisition time label; arrange the several labeled grayscale images in chronological order of the acquisition time to generate a first grayscale image sequence.

[0088] S2: For any grayscale image in the first grayscale image sequence: perform histogram equalization on the grayscale image to obtain the processed image.

[0089] like Figure 3 The figures shown are grayscale images and grayscale histograms before and after frost heave deformation without histogram equalization processing, as well as the grayscale images of the present invention. Figure a represents the grayscale image before frost heave and segregation; Figure b represents the grayscale image after frost heave and segregation; and Figure c represents the grayscale histogram.

[0090] like Figure 4 The figures shown are the processed images and corresponding grayscale histograms before and after the freeze-thaw deformation of the present invention; wherein, Figure a represents the grayscale image before segregation freeze-thaw; Figure b represents the grayscale image after segregation freeze-thaw; and Figure c represents the grayscale histogram.

[0091] It can be seen that after histogram equalization, the crack characteristics caused by frost heave and condensation of frozen soil are more obvious, and the gray difference between the upper frozen area and the bottom unfrozen area is further widened.

[0092] S3: Divide the processed image into several target regions; for any target region: obtain all gray values, average gray value, and maximum gray value of the target region; for any gray value: determine whether the gray value is greater than the average gray value; if the determination result indicates that the gray value is greater than the average gray value, then update the gray value to the maximum gray value of the target region; if the determination result indicates that the gray value is not greater than the average gray value, then update the gray value to zero; based on the update results of all gray values ​​of the target regions, obtain the filtered image.

[0093] like Figure 5 The figures shown are the filtered images and corresponding grayscale histograms before and after the condensation and freeze-thaw deformation of the present invention; wherein, Figure a represents the filtered image before condensation and freeze-thaw deformation; Figure b represents the filtered image after condensation and freeze-thaw deformation; and Figure c represents the grayscale histogram.

[0094] The filtered images before and after condensation and frost heave deformation show that the entire image exhibits speckle characteristics with no obvious black-and-white transition areas. The corresponding grayscale histogram results show that the grayscale statistical characteristics of the images before and after condensation and frost heave deformation are very similar.

[0095] S4: Stitch together several filtered images to generate a pre-analysis image; determine the convolution kernel size information for image convolution processing; perform opening and closing operations on the pre-analysis image based on the convolution kernel to obtain a quasi-analysis image.

[0096] like Figure 6 The image shown is a quasi-analytical image of the present invention before and after condensation and frost heave deformation;

[0097] As can be seen from the quasi-analysis images before and after frost heave deformation, no obvious cracks appeared in the frozen soil before frost heave. The black horizontal lines in the quasi-analysis images represent small cracks caused by localized drying shrinkage of the soil during sample preparation. After frost heave, the cracks caused by frost heave are clearly visible in the quasi-analysis images, and the image convolution algorithm targets these types of cracks.

[0098] S5: For any quasi-analysis image: calculate the crack area of ​​each crack region in the quasi-analysis image; determine the total crack area corresponding to the quasi-analysis image based on several crack areas; construct a crack area-time curve based on the total crack area corresponding to each quasi-analysis image in the second grayscale image sequence; determine the time corresponding to the inflection point of the crack area-time curve as the reference time to obtain the reference time series.

[0099] like Figure 7 The image shown is the crack area-time curve corresponding to the second grayscale image sequence in an application embodiment of the present invention. During the analysis, the reference image is updated at the inflection points of the curve. The inflection points of the Sc-t curve represent the moment after which the image crack characteristics begin to change. For example, during freezing, new segregation cracks are generated; during melting, some segregation cracks completely close and disappear. These changes in image characteristics can cause excessive differences in features between the reference image and the currently analyzed image in the cross-correlation analysis.

[0100] S6: Based on the triggering of the reference time in the reference time series, select a grayscale image corresponding to the reference time from the second grayscale image sequence; or, based on the triggering of the reference time in the reference time series, select a grayscale image corresponding to a time that is before and adjacent to the reference time from the second grayscale image sequence; use the selected grayscale image as the current reference image in the DIC analysis process of the second grayscale image sequence.

[0101] like Figure 8 The image shown is a schematic diagram illustrating the correspondence between the reference image and the current image being analyzed in a traditional DIC analysis method; Figure 9 The diagram shown is a schematic representation of the correspondence between the reference image and the current analysis image in the method of this embodiment of the invention.

[0102] pass Figure 8The mapping relationship between the reference image and the current analysis image shows that the reference image is often fixed and unchanging, serving as the initial reference image. Through... Figure 9 From the mapping relationship between the intermediate reference image and the current analysis image, it can be seen that in the method of this embodiment of the invention, the first reference image is the initial reference image, and subsequent reference images are updated reference images that change in real time according to the image crack features. Figure 9 It can also clearly show the changes in the image during the melting and refreezing process. (Comparison) Figure 8 and Figure 9 It is understood that the method of this invention updates the reference image when the frozen soil structure changes significantly, so as to reduce the feature differences of image pairs during DIC analysis and thus effectively obtain the wind condensation and frost heave deformation field.

[0103] like Figure 10 The diagram shows the condensation and frost heave deformation fields measured using the traditional DIC analysis method and the method of this invention, respectively.

[0104] pass Figure 10 It is known that the traditional DIC method directly performs cross-correlation analysis on grayscale images. This method misidentifies the downward movement of the frozen surface as a deformation process of the permafrost, leading to erroneous results as shown in Figure a. Using this invention, measurement results conforming to the frost heave law can be obtained, as shown in Figure b.

[0105] Therefore, in this embodiment, when the feature difference between the reference image and the current analysis image is too large, the reference image is updated to reduce the feature difference between the reference image and the current analysis image, thereby realizing non-contact high-precision measurement of frost heave deformation of fine-grained soil; thus effectively overcoming the error caused by the process of segregation crack generation to displacement measurement.

[0106] like Figure 11 The diagram shows a schematic of a measuring device for condensation and frost heave deformation fields according to an embodiment of the present invention. The measuring device 110 includes: an acquisition module 111, used to acquire all grayscale images of a frozen soil target area before and during condensation and frost heave, forming a first grayscale image sequence; wherein the grayscale images have acquisition time labels; an image processing module 112, used to perform image processing on any grayscale image in the first grayscale image sequence to obtain a quasi-analysis image; a determination module 113, used to determine a reference time sequence based on a second grayscale image sequence formed by several quasi-analysis images; and an update module 114, used to perform an update operation on the reference image during the DIC analysis process of the second grayscale image sequence based on the triggering of a reference time in the reference time sequence, to obtain the condensation and frost heave deformation field.

[0107] In a preferred embodiment, the determining module includes: a calculation unit, configured to, for any quasi-analysis image: calculate the crack area of ​​each crack region in the quasi-analysis image; and determine the total crack area corresponding to the quasi-analysis image based on a plurality of crack areas; a curve construction unit, configured to construct a crack area-time curve based on the total crack area corresponding to each quasi-analysis image in the second grayscale image sequence; and a determining unit, configured to determine the time corresponding to the inflection point of the crack area-time curve as a reference time, thereby obtaining a reference time series.

[0108] In a preferred embodiment, the update module includes: a selection unit, configured to select a grayscale image corresponding to the reference time from the second grayscale image sequence based on the triggering of the reference time in the reference time series; or, configured to select a grayscale image corresponding to a time preceding and adjacent to the reference time from the second grayscale image sequence based on the triggering of the reference time in the reference time series; and a determination unit, configured to use the selected grayscale image as the current reference image in the DIC analysis process of the second grayscale image sequence.

[0109] In a preferred embodiment, the image processing module includes: an image processing unit for processing the grayscale image to obtain a pre-analyzed image; a determination unit for determining the kernel size information of the image convolution processing; and an image convolution processing unit for performing opening and closing operations on the pre-analyzed image based on the convolution kernel to obtain a quasi-analyzed image.

[0110] In a preferred embodiment, the image processing unit includes: a histogram equalization processing subunit, used to perform histogram equalization processing on the grayscale image to obtain a processed image; a division subunit, used to divide the processed image into several target regions; a filtering subunit, used to perform threshold filtering on any of the target regions to obtain a filtered image; and a generation subunit, used to stitch together several filtered images to generate a pre-analyzed image.

[0111] In a preferred embodiment, the filtering subunit includes: an acquisition unit, configured to acquire all grayscale values, the average grayscale value, and the maximum grayscale value of the target region; a grayscale value update unit, configured to, for any grayscale value: determine whether the grayscale value is greater than the average grayscale value; if the determination result indicates that the grayscale value is greater than the average grayscale value, update the grayscale value to the maximum grayscale value of the target region; if the determination result indicates that the grayscale value is not greater than the average grayscale value, update the grayscale value to zero; and an obtaining unit, configured to obtain a filtered image based on the update results of all grayscale values ​​of the target region.

[0112] In a preferred embodiment, the acquisition module includes: an acquisition unit, configured to acquire a first original grayscale image of the frozen soil before condensation and frost heave, and all second original grayscale images of the frozen soil during condensation and frost heave; an extraction unit, configured to extract images of the same target area from the first original grayscale image and each of the second original grayscale images, respectively, to obtain a plurality of grayscale images; a labeling unit, configured to, for any grayscale image: acquire the acquisition time of the grayscale image; label the grayscale image based on the acquisition time, to generate a grayscale image with acquisition time label; and a generation unit, configured to arrange the plurality of labeled grayscale images in chronological order of the acquisition time, to generate a first grayscale image sequence.

[0113] The above-described apparatus can execute the measurement method for condensation and frost heave deformation fields provided in an embodiment of the present invention, and has functional modules and beneficial effects corresponding to the measurement method for condensation and frost heave deformation fields. Technical details not described in detail in this embodiment can be found in the measurement method for condensation and frost heave deformation fields provided in an embodiment of the present invention.

[0114] The present invention also provides an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the measurement method for condensation and frost heave deformation fields described in the present invention.

[0115] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0116] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0117] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to the following embodiments of this application described in the "Exemplary Methods" section above.

[0118] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0119] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0120] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0121] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0122] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0123] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

[0124] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0125] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0126] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for measuring the frost heave deformation field, characterized in that, The method includes: All grayscale images of the target frozen soil area before and during condensation and frost heave are acquired to form a first grayscale image sequence; wherein, the grayscale images have acquisition timestamp labels; For any grayscale image in the first grayscale image sequence: perform image processing on the grayscale image to obtain a quasi-analytical image; A reference time series is determined based on a second grayscale image sequence formed from several of the aforementioned quasi-analyzed images; Based on the triggering of the reference time in the reference time series, an update operation is performed on the reference image in the DIC analysis process of the second grayscale image sequence to obtain the segregation frost heave deformation field. The step of determining the reference time series based on the second grayscale image sequence formed by several quasi-analysis images includes: for any quasi-analysis image: calculating the crack area of ​​each crack region in the quasi-analysis image; determining the total crack area corresponding to the quasi-analysis image based on the several crack areas; constructing a crack area-time curve based on the total crack area corresponding to each quasi-analysis image in the second grayscale image sequence and the acquisition timestamp; and determining the time corresponding to the inflection point of the crack area-time curve as the reference time to obtain the reference time series.

2. The method according to claim 1, characterized in that, The step of triggering an update operation on the reference image during the DIC analysis of the second grayscale image sequence based on the reference time in the reference time series includes: Based on the triggering of the reference time in the reference time series, a grayscale image corresponding to the reference time is selected from the second grayscale image sequence; or, based on the triggering of the reference time in the reference time series, a grayscale image corresponding to a time that is before and adjacent to the reference time is selected from the second grayscale image sequence. The selected grayscale image is used as the current reference image in the DIC analysis process of the second grayscale image sequence.

3. The method according to claim 1, characterized in that, The image processing of the grayscale image to obtain a quasi-analytical image includes: The grayscale image is processed to obtain a pre-analyzed image; Determine the kernel size information for image convolution processing; The pre-analyzed image is subjected to opening and closing operations based on the convolution kernel to obtain a quasi-analyzed image.

4. The method according to claim 3, characterized in that, The grayscale image is processed to obtain a pre-analyzed image; including: The grayscale image is subjected to histogram equalization to obtain an equalized histogram; The balanced histogram is divided into several target regions; For any of the target regions: apply threshold filtering to the target region to obtain a filtered image; Several filtered images are stitched together to generate a pre-analyzed image.

5. The method according to claim 4, wherein threshold filtering of the target region to obtain a filtered image comprises: Obtain all grayscale values, the average grayscale value, and the maximum grayscale value of the target region; For any of the aforementioned grayscale values: determine whether the grayscale value is greater than the average grayscale value; if the determination result indicates that the grayscale value is greater than the average grayscale value, then update the grayscale value to the maximum grayscale value of the target area; if the determination result indicates that the grayscale value is not greater than the average grayscale value, then update the grayscale value to zero. The filtered image is obtained based on the update results of all grayscale values ​​in the target region.

6. The method according to claim 1, characterized in that, The step of acquiring all grayscale images of the target frozen soil area before and during frost heave to form a first grayscale image sequence includes: Obtain the first original grayscale image of the frozen soil before condensation and frost heave, and all the second original grayscale images of the frozen soil during the condensation and frost heave process. Images of the same target region are extracted from the first original grayscale image and each of the second original grayscale images respectively to obtain several grayscale images; For any grayscale image: obtain the acquisition timestamp of the grayscale image; label the grayscale image based on the acquisition timestamp to generate a grayscale image with acquisition timestamp labels; Several labeled grayscale images are arranged in chronological order according to the acquisition timestamps to generate the first grayscale image sequence.

7. A measuring device for the condensation and frost heave deformation field, characterized in that, include: The acquisition module is used to acquire all grayscale images of the target frozen soil area before condensation and frost heave and during condensation and frost heave, respectively, to form a first grayscale image sequence; wherein, the grayscale images have acquisition timestamp labels; The image processing module is configured to perform image processing on any grayscale image in the first grayscale image sequence to obtain a quasi-analytical image; The determination module is used to determine a reference time series based on a second grayscale image sequence formed from several of the quasi-analyzed images; The update module is used to perform an update operation on the reference image in the DIC analysis process of the second grayscale image sequence based on the triggering of the reference time in the reference time series, so as to obtain the segregation and frost heave deformation field. The determining module includes: a calculation unit, used for calculating the crack area of ​​each crack region in any of the quasi-analysis images; and determining the total crack area corresponding to the quasi-analysis image based on several crack areas; a curve construction unit, used for constructing a crack area-time curve based on the total crack area corresponding to each of the quasi-analysis images in the second grayscale image sequence and the acquisition timestamp; and a determining unit, used for determining the time corresponding to the inflection point of the crack area-time curve as the reference time to obtain a reference time series.

8. A computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of claims 1-6.

Images