A recycled aggregate shaping effect identification method based on multiple fractal spectrum characteristic values

Abstract

The application discloses a recycled aggregate shaping effect identification method based on multiple fractal spectrum characteristic values, and comprises the following steps: placing recycled aggregate in a ball mill; discharging part of the aggregate to an image acquisition platform; acquiring an image of the aggregate; calculating the initial multiple fractal spectrum characteristic values of the aggregate image after pre-processing the image; after the calculation, the image acquisition platform is lifted, and the recycled aggregate is returned to the ball mill; ball milling the recycled aggregate; after the ball milling, the aggregate is discharged to the image acquisition platform; calculating the multiple fractal spectrum characteristic values of the aggregate image after the ball milling after pre-processing the image; counting the pixel number of the aggregate; dividing the multiple fractal spectrum characteristic values by the pixel number of the aggregate to obtain the multiple fractal spectrum characteristic values of the unit pixel aggregate image; the change range of the multiple fractal spectrum characteristic values of the unit pixel aggregate image is not more than 10% for two times in succession, and the ball milling is ended. The application can solve the problems that the aggregate particle shaping effect depends on manual operation, cannot be processed in batches, and is low in efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction engineering, and in particular to a recycled aggregate shaping effect identification method based on multi-fractal spectrum characteristic values. BACKGROUND

[0002] The recycled aggregate utilization technology refers to that after waste concrete blocks are crushed, washed and graded, the waste concrete blocks are mixed with natural aggregates such as sand and gravel in a certain proportion and grading, and partially or wholly replace the natural aggregates, and then the waste concrete blocks are put into the production of construction engineering again. The recycled aggregate is angular after crushing, and the surface of the recycled aggregate is covered with old mortar, the former leads to uneven internal stress distribution of the concrete prepared by using the recycled aggregate under the action of load, and the latter leads to insufficient bonding between the recycled aggregate and the fresh mortar, both of which affect the strength of the recycled aggregate, so that the recycled aggregate can only be used in non-load-bearing components and backfilling.

[0003] The strength of the recycled aggregate concrete can be significantly improved by polishing and shaping the recycled aggregate to remove the angles and old mortar of the recycled aggregate. At present, most aggregate particle shaping technologies only control the polishing time and polishing method, and the quantitative analysis of the aggregate particle shaping effect is less. Although the planeness and circularity of a single aggregate particle can be calculated by using the Image-Pro Plus 6.0 image software, the aggregate needs to be manually framed, the method is low in efficiency, and it is difficult to meet the needs of the construction site. SUMMARY

[0004] In view of the deficiencies in the prior art, the technical problem to be solved by the present application is to provide a recycled aggregate shaping effect identification method based on multi-fractal spectrum characteristic values, which can solve the problems of dependence on manual operation, inability to batch processing and low efficiency of the aggregate particle shaping effect.

[0005] In order to achieve the above purpose, the following technical measures are adopted in the present application:

[0006] A recycled aggregate shaping effect identification method based on multi-fractal spectrum characteristic values comprises the following steps:

[0007] S1, placing the crushed recycled aggregate in a ball mill;

[0008] S2, reversing the ball mill for 1-2 seconds, and unloading part of the aggregate on an image acquisition platform;

[0009] S3, an image of the aggregate is acquired by a video system and transmitted to a computer, and after the image is preprocessed, the initial multi-fractal spectrum characteristic value of the aggregate image is calculated;

[0010] S4, after the calculation is completed, the image acquisition platform is lifted, and the recycled aggregate is returned to the ball mill;

[0011] S5, rotate the ball mill in the forward direction to ball mill the recycled aggregate, after ball milling for 20 minutes, reverse the ball mill, unload part of the aggregate onto the image acquisition platform, after pre-processing the image, calculate the multifractal spectrum characteristic value of the aggregate image after ball milling, count the number of pixels of the aggregate, divide the multifractal spectrum characteristic value by the number of pixels of the aggregate, and obtain the multifractal spectrum characteristic value of the unit pixel aggregate image;

[0012] S6, repeat steps S4 and S5 until the multifractal spectrum characteristic value of the unit pixel aggregate image changes by no more than 10% for two consecutive times, stop ball milling, and reverse the ball mill until the recycled aggregate is unloaded.

[0013] Further, the image pre-processing process in steps S3 and S5 is:

[0014] P1, acquire the recycled aggregate image through a video system and store it in JPG format;

[0015] P2, cut the image into a square to ensure that the total number of pixels of the image is an exponential power of 2;

[0016] P3, read the red, blue and green channel values of each pixel point of the image after step P2, and perform grayscale processing on the image based on the channel values;

[0017] P4, perform binaryzation processing on the grayscale image obtained in step P3, update the grayscale value of the pixel points with a grayscale less than 122 to 0, and update the grayscale value of the pixel points with a grayscale greater than 122 to 1, at this time, the part with a grayscale value of 1 is the aggregate position;

[0018] P5, perform filter denoising processing on the binaryzation grayscale image obtained in step P4 to obtain a multifractal analysis image.

[0019] Preferably, the calculation of the multifractal spectrum characteristic value in steps S3 and S5 comprises:

[0020] M1, divide the multifractal analysis image obtained in step P5 into N blocks with a side length of a pixels, and the value of a is [1, 8];

[0021] M2, count the total number of pixels AG sum with a grayscale of 1 in the multifractal analysis image, and the number of pixels AG i with a grayscale of 1 in each block (i = 1, 2, 3... N-1, N), calculate the percentage of the number of pixels with a grayscale of 1 in each block to the total number of pixels

[0022] M3, define a statistical matrix function M(a, q) = ∑p iq(a), wherein q is the order of the statistical matrix, when q is greater than 1, the statistical matrix function embodies singular characteristics of the dense distribution area, when q is less than 1, the statistical matrix function embodies characteristics of the sparse distribution area, M(a, q) represents the numerical value of the statistical matrix function when the block length is a pixel and the order of the statistical matrix is q;

[0023] M4, calculating the quality index function The calculation method is to draw the lnM(a, q)-lna curve under different block lengths a of the pixel and the order q of the statistical matrix, and to obtain the slope of the curve when a approaches 0;

[0024] M5, performing Legendre transformation calculation, and the calculation formula is α(q) is the singular index when the order of the statistical matrix is q, the slope of the quality index function τ(q) with q, f(α) is the multifractal spectrum value when the order of the statistical matrix is q, an α(q)-f(α) image of the order q of the statistical matrix is drawn, and the multifractal spectrum corresponding to the recycled aggregate image is obtained.

[0025] M6, subtracting the multifractal spectrum values corresponding to the maximum and minimum singular indexes from each other, and dividing the total number of pixels of the multifractal analysis image to obtain the multifractal spectrum characteristic value.

[0026] Further, the image acquisition platform is supported by an adjustable support for returning the sampled aggregate to the ball mill.

[0027] Further, the calculation of the gray value in the step P3 uses the sum of 0.39 times the red channel value, 0.5 times the green channel value, and 0.11 times the blue channel value.

[0028] Preferably, the order q of the statistical matrix is an integer between -10 and 10.

[0029] Further, the video system in the step S3 uses a high-pixel camera.

[0030] Compared with the existing traditional method, the beneficial effects and advantages of the recycled aggregate shaping effect identification method based on the multifractal spectrum characteristic value of the present application are that:

[0031] 1. The recycled aggregate particle shaping effect is quantitatively analyzed, and after the aggregate ball mill reaches a certain degree, continuing to ball mill has adverse effects on energy consumption, manpower, construction period, etc., and the use of the present application can significantly reduce the invalid work in the ball milling process.

[0032] 2. The use of the present application can ensure the shaping effect of the recycled aggregate particles, avoid empirical ball milling, cause part of the aggregate surface to have corners and old mortar residues, and affect the performance of the cement-based material prepared by using the recycled aggregate.

[0033] 3. Compared with calculating the flatness and roundness of a single aggregate particle, this invention can perform particle shaping analysis on a large number of aggregates simultaneously, and eliminates the process of selecting aggregate particles, saving manpower and time, thus meeting the needs of high-efficiency production on construction sites. Attached Figure Description

[0034] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of the method for recognizing the reshaping effect of recycled aggregate based on multiple fractal spectrum feature values ​​according to the present invention;

[0036] Figure 2 This is a flowchart of the image preprocessing process of the present invention;

[0037] Figure 3 This is a schematic diagram of the ball mill-image acquisition structure of the present invention;

[0038] Figure 4 Images of a single aggregate particle from Example 1;

[0039] Figure 5 This is a binarized image of a single aggregate particle from Example 1;

[0040] Figure 6 This is a multifractal image of a single aggregate particle from Example 1;

[0041] Figure 7 This is a multifractal analysis image of the aggregate in Example 2;

[0042] Figure 8 This is a schematic diagram of the block-based binarized image in Example 2;

[0043] Figure 9 The graph shows the lnM(a,q)-lna curve for Example 2.

[0044] Figure 10 This is a multifractal spectrum curve of Example 2;

[0045] Figure 11 This is the initial image acquired in Example 3;

[0046] Figure 12 This is the initial preprocessed image for Example 3;

[0047] Figure 13This is the initial multifractal spectrum of Example 3;

[0048] Figure 14 The image acquired after ball milling in Example 3;

[0049] Figure 15 This is a preprocessed image of the ball milling process in Example 3;

[0050] Figure 16 This is a multifractal spectrum of the ball-milled sample from Example 3.

[0051] In the diagram: 11-Ball mill, 12-Ball mill support, 13-Image acquisition platform, 14-High-resolution camera, 15-Camera support. Detailed Implementation

[0052] like Figures 1-16 As shown, the method for identifying the reshaping effect of recycled aggregate based on multifractal spectrum feature values ​​of the present invention includes the following steps:

[0053] S1. Place the crushed recycled aggregate into the ball mill 11.

[0054] S2, reverse the ball mill for 1-2 seconds, and unload some of the aggregate onto the image acquisition platform 13.

[0055] S3. The video system acquires images of the aggregate, transmits them to the computer, preprocesses the images, and calculates the initial multifractal spectrum feature values ​​of the aggregate images.

[0056] S4. After the calculation is completed, the image acquisition platform is lifted and the recycled aggregate is returned to the ball mill.

[0057] S5. The ball mill 11 rotates forward to ball mill the recycled aggregate. After ball milling for 20 minutes, the ball mill is reversed to unload some of the aggregate onto the image acquisition platform 13. After preprocessing the image, the multifractal spectrum feature value of the aggregate image after ball milling is calculated, the number of pixels of the aggregate is counted, and the multifractal spectrum feature value is divided by the number of pixels of the aggregate to obtain the multifractal spectrum feature value of the aggregate image per unit pixel.

[0058] S6. Repeat steps S4 and S5 until the multifractal spectrum feature value of the unit pixel aggregate image changes by no more than 10% in two consecutive cycles. Then, stop the ball mill and reverse the ball mill until the recycled aggregate is completely unloaded.

[0059] The image preprocessing in steps S3 and S5 of this invention is implemented using the following technical solutions:

[0060] P1. Acquire images of recycled aggregates through a video system and save them in JPG format;

[0061] P2. Cut the image into squares, ensuring that the total number of pixels in the image is a power of 2.

[0062] P3. Read the red, blue and green channel values ​​of each pixel in the image after segmentation in step P2, and use them to convert the image to grayscale.

[0063] P4. Perform binarization processing on the grayscale image obtained in step P3, update the grayscale value of pixels with a grayscale value less than 122 to 0, and update the grayscale value of pixels with a grayscale value greater than 122 to 1. At this time, the part of the pixel with a grayscale value of 1 is the aggregate position.

[0064] P5. Perform filtering and denoising processing on the binarized grayscale image obtained in step P4 to obtain a multifractal analysis image.

[0065] The calculation of multifractal spectrum eigenvalues ​​in steps S3 and S5 of this invention is achieved using the following technical solution:

[0066] M1. Divide the multifractal analysis image obtained in step P5 into N blocks with a side length of a pixels, where a takes the value [1, 8].

[0067] M2. Calculate the total number of pixels with a gray level of 1 (AGsum) within the image using multifractal analysis, and the number of pixels with a gray level of 1 (AG) within each block. i (i = 1, 2, 3...N-1, N), calculate the percentage of pixels with a gray level of 1 in each block out of the total number of pixels.

[0068] M3. Define the statistical matrix function M(a, q) = ∑p i q(a), where q is the order of the statistical matrix. When q is greater than 1, the statistical matrix function exhibits the characteristics of a densely distributed region. When q is less than 1, the statistical matrix function exhibits the characteristics of a sparsely distributed region. M(a,q) represents the value of the statistical matrix function when the block side length is a pixels and the order of the statistical matrix is ​​q.

[0069] M4, Calculate the quality index function The calculation method involves plotting the lnM(a, q)-lna curve under different block side lengths of a pixels and the order of the statistical matrix of q, and then obtaining the slope of the curve when a approaches 0.

[0070] M5. Perform Legendre transformation calculation; the calculation formula is as follows: α(q) is the singularity index when the order of the statistical matrix is ​​q, the slope of the quality index function τ(q) as q changes, and f(α) is the multifractal spectrum value when the order of the statistical matrix is ​​q. Plot the α(q)-f(α) image for the order of the statistical matrix q to obtain the multifractal spectrum corresponding to the recycled aggregate image.

[0071] M6. Subtract the multifractal spectrum values ​​corresponding to the maximum and minimum singularity indices, and divide by the total number of pixels in the multifractal analysis image to obtain the multifractal spectrum feature values.

[0072] The video system in step S3 uses a high-resolution camera 14, preferably outputting video signals with a resolution of 1080P or higher. The high-resolution camera 14 is supported by a camera bracket 15. The image acquisition platform 13 is supported by an adjustable bracket, facilitating the return of sampled aggregate to the ball mill 11. The ball mill 11 is supported and fixed by a ball mill bracket 12.

[0073] In step P3, the grayscale value is calculated using the sum of 0.39 times the red channel value, 0.5 times the green channel value, and 0.11 times the blue channel value. The resolution of the cropped image in step P2 should preferably be no less than 256*256.

[0074] The order q of the statistical matrix should ideally be an integer between -10 and 10 to reduce the amount of computer processing required. If q is too large, the calculation becomes difficult, and if q is too small, it loses its computational value.

[0075] Example 1

[0076] This embodiment aims to illustrate the steps of aggregate image acquisition, grayscale conversion, binarization, and filtering. All of the above steps involve processing and calculating each pixel. Displaying all of them would result in too much data. To better illustrate the data and calculation methods in the process, a single aggregate is used to illustrate the above steps.

[0077] The steps in this embodiment are as follows:

[0078] (1): Place the crushed recycled aggregate into the ball mill 11.

[0079] (2): Reverse the ball mill for 2 seconds and unload some of the aggregate onto the image acquisition platform 13.

[0080] (3): Acquire images of recycled aggregates through a video system and save them in JPG format.

[0081] (4): Randomly select a single aggregate piece, and the image of the single aggregate piece is as follows: Figure 4 As shown, this part has 25*25 pixels.

[0082] (5): Read the red, blue, and green channel values ​​of each pixel in the image of a single aggregate. The red channel value of each pixel is:

[0083]

[0084]

[0085] The green channel values ​​for each pixel are:

[0086] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 114 118 154 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 160 221 220 204 235 192 0 0 0 0 0 0 0 0 0 0 0 0 0 33 232 208 183 190 218 204 232 103 3 0 0 0 0 0 0 0 0 0 0 211 198 194 184 194 213 212 189 114 181 96 0 0 0 0 0 0 0 0 187 225 201 203 192 201 205 197 197 215 184 198 217 0 0 0 0 0 0 55 224 191 221 176 144 230 214 210 227 215 203 141 196 133 0 0 0 0 0 125 199 224 209 178 76 228 215 202 223 226 221 215 229 201 0 0 0 0 0 108 210 213 187 188 192 210 200 200 208 223 226 217 231 237 11 0 0 0 0 229 204 198 176 178 192 193 179 206 213 185 192 208 218 223 25 0 0 0 0 137 190 167 178 175 211 197 188 191 211 194 196 199 199 170 0 0 0 0 0 0 131 182 198 150 123 182 159 190 160 172 146 177 206 91 0 0 0 0 0 0 0 0 9 0 0 29 24 24 19 23 17 16 11 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[0087] The blue channel values ​​for each pixel are:

[0088]

[0089]

[0090] (6): Based on the red, blue, and green channel values ​​of each pixel in the single aggregate image, calculate the grayscale value of each pixel in the single aggregate image with weights of 0.39, 0.5, and 0.11. The grayscale values ​​are shown below:

[0091] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 129 115 119 156 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 161 224 222 205 236 193 0 0 0 0 0 0 0 0 0 0 0 0 0 35 233 210 185 192 220 205 233 104 3 0 0 0 0 0 0 0 0 0 0 212 199 195 186 196 215 213 190 114 181 95 0 0 0 0 0 0 0 0 187 226 203 205 194 204 207 198 198 216 186 199 218 0 0 0 0 0 0 56 225 193 223 178 145 232 215 211 227 216 204 142 196 133 0 0 0 0 0 127 201 225 211 179 76 230 216 203 224 227 222 215 230 202 0 0 0 0 0 109 212 215 189 189 194 212 202 201 210 225 228 217 231 237 11 0 0 0 0 231 206 199 178 179 194 195 180 207 215 187 194 208 218 223 26 0 0 0 0 138 191 169 179 177 213 199 189 192 213 195 197 199 199 170 0 0 0 0 0 0 131 183 199 151 126 184 161 190 161 174 148 177 206 92 0 0 0 0 0 0 0 0 10 0 0 30 24 24 19 23 18 16 11 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[0092] (7): Binarize the gray values ​​obtained in step (6), updating the gray values ​​of pixels with a gray value less than 122 to 0, and updating the gray values ​​of pixels with a gray value greater than 122 to 1. At this time, the part of the pixel with a gray value of 1 is the aggregate position. The binarized image is as follows. Figure 5 As shown, the binarized values ​​of each pixel are as follows:

[0093] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[0094] (8): The binarized image is filtered and denoised to obtain a multifractal image of a single aggregate, such as... Figure 6 As shown.

[0095] Example 2

[0096] This embodiment aims to illustrate the steps for calculating the eigenvalues ​​of the multifractal spectrum. This embodiment uses a 256*256 resolution image for analysis, so the image preprocessing process is not shown. For details of the image preprocessing steps, please refer to Embodiment 1.

[0097] The steps in this embodiment are as follows:

[0098] (1): The aggregate image is preprocessed to obtain a multifractal analysis image of the aggregate before shaping, such as... Figure 7 As shown.

[0099] (2): Divide the multifractal analysis image obtained in step (1) into N blocks with a side length of a pixels. The value of a is an integer power of 2. The maximum value of a is half the number of pixels on the side length of the multifractal analysis image. Taking this embodiment as an example, the multifractal analysis image with a resolution of 256*256 has the values ​​of a as 1, 2, 3, 4, 5, 6, 7, and 8.

[0100] (3): Statistical analysis of the total number of pixels with a gray level of 1 in the multifractal analysis image AG sumAG is the number of pixels with a gray level of 1 in each block. i (i = 1, 2, 3...N-1, N), calculate the percentage of pixels with a gray level of 1 in each block out of the total number of pixels. by Figure 8 Taking the block shown as an example, the block side length is 2 pixels, and there are three pixels with a value of 1 within the block. AG i The total number of pixels with a value of 3 and a grayscale value of 1 (AG) sum The value is 43915, therefore the proportion of pixels with a value of 1 in this block is 3 / 43915.

[0101] (4): Determine the range of the order q of the statistical matrix. In this embodiment, q takes the value of [-5, 5].

[0102] (5): When the side length of different blocks is a pixels and the order of the statistical matrix is ​​q, the statistical matrix function M(a, q) = ∑p i q(a), taking the statistical matrix function M(2, -5) with a block side length of 2 pixels and an order q of -5 as an example, is calculated as the sum of the -5 powers of the proportion of 1 pixel in each block to the total number of pixels, i.e., ∑p i -5 (2) The numerical values ​​of the statistical matrix function in this embodiment are shown in the table below:

[0103] Table 1. Numerical Table of Statistical Matrix Functions

[0104]

[0105] Calculate the quality index function The calculation method involves plotting lnM(a, q) - lna curves for different block side lengths of 'a' pixels and statistical matrix order of 'q', and then calculating the slope of the curve as 'a' approaches 0. The lnM(a, q) - lna curve in this embodiment is shown below. Figure 9 As shown.

[0106] Perform the Legendre transformation calculation, the calculation formula is as follows: α(q) is the singularity exponent when the order of the statistical matrix is ​​q, f(α) is the slope of the mass exponent function τ(q) as a function of q, and f(α) is the multifractal spectrum value when the order of the statistical matrix is ​​q. The α(q)-f(α) graph for the statistical matrix of order q is plotted, and the data are shown in Table 2. The multifractal spectrum corresponding to the recycled aggregate image is obtained as follows: Figure 10 As shown.

[0107] Table 2. Numerical Table of Multifractal Spectra in Example 2

[0108]

[0109] (6): Subtract the multifractal spectrum values ​​corresponding to the maximum and minimum values ​​of the singularity index, and divide by the total number of pixels in the multifractal analysis image to obtain the multifractal spectrum feature value. In this embodiment, the value is 0.9223.

[0110] Example 3

[0111] This embodiment aims to illustrate the aggregate shaping steps. For details of the image preprocessing steps and image multifractal steps, please refer to Embodiment 1 and Embodiment 2.

[0112] The steps in this embodiment are as follows:

[0113] (1) Place the crushed recycled aggregate into a ball mill.

[0114] (2) Reverse the ball mill for 1-2 seconds to unload some of the aggregate onto the image acquisition platform.

[0115] (3) The video system acquires images of the aggregate and transmits them to the computer. After preprocessing the images, the initial multifractal spectrum feature values ​​of the aggregate images are calculated. The initial acquired image, the initial preprocessed image, and the initial multifractal spectrum are as follows: Figure 11 , 12 As shown in Figure 13, the multifractal spectrum feature value is 1.344, the number of pixels of the aggregate is 4717, and the multifractal spectrum feature value per unit aggregate pixel is 0.000285.

[0116] (4) After the calculation is completed, the image acquisition platform is lifted and the recycled aggregate is returned to the ball mill.

[0117] (5) Rotate the ball mill forward to ball mill the recycled aggregate. After ball milling for 20 minutes, reverse the ball mill to unload some of the aggregate onto the image acquisition platform. After image preprocessing, the acquired image after ball milling, the preprocessed image after ball milling, and the multifractal spectrum after ball milling are shown below. Figure 14 , 15 As shown in Figure 16, the multifractal spectrum feature value of the aggregate image after ball milling is calculated to be 1.3189, the number of pixels of the aggregate is 5772, the multifractal spectrum feature value per unit aggregate pixel is 0.000228, and the multifractal spectrum feature value per unit aggregate pixel decreases by 20% compared with that before milling.

[0118] (6) Repeat steps (4) and (5) until the multifractal spectrum feature value of the unit aggregate pixel changes by no more than 10% twice in a row. Then end the ball mill and reverse the ball mill until the recycled aggregate is unloaded.

[0119] 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 transformations or substitutions that can be understood by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of the present invention.

Claims

1. A method for recognizing the shaping effect of recycled aggregate based on multifractal spectrum feature values, characterized in that, Includes the following steps: S1. Place the crushed recycled aggregate into a ball mill; S2, reverse the ball mill for 1-2 seconds, and unload part of the aggregate onto the image acquisition platform; S3. The video system acquires images of the aggregate, transmits them to the computer, preprocesses the images, and calculates the initial multifractal spectrum feature values ​​of the aggregate images. S4. After the calculation is completed, the image acquisition platform is lifted and the recycled aggregate is returned to the ball mill; S5. The ball mill rotates forward to ball mill the recycled aggregate. After ball milling for 20 minutes, the ball mill is rotated backward to unload some of the aggregate onto the image acquisition platform. After preprocessing the image, the multifractal spectrum feature value of the aggregate image after ball milling is calculated, the number of pixels of the aggregate is counted, and the multifractal spectrum feature value is divided by the number of pixels of the aggregate to obtain the multifractal spectrum feature value of the aggregate image per unit pixel. S6. Repeat steps S4 and S5 until the multifractal spectrum feature value of the unit pixel aggregate image changes by no more than 10% in two consecutive cycles. Then, stop the ball mill and reverse the ball mill until the recycled aggregate is completely unloaded. The image preprocessing process in steps S3 and S5 is as follows: P1. Acquire images of recycled aggregates through a video system and save them in JPG format; P2. Cut the image into squares, ensuring that the total number of pixels in the image is a power of 2. P3. Read the red, blue and green channel values ​​of each pixel in the image after segmentation in step P2, and use them to convert the image to grayscale. P4. Perform binarization processing on the grayscale image obtained in step P3, update the grayscale value of pixels with a grayscale value less than 122 to 0, and update the grayscale value of pixels with a grayscale value greater than 122 to 1. At this time, the part of the pixel with a grayscale value of 1 is the aggregate position. P5. Perform filtering and noise reduction processing on the binarized grayscale image obtained in step P4 to obtain a multifractal analysis image. The calculation of the multifractal spectrum eigenvalues ​​in steps S3 and S5 includes: M1. Divide the multifractal analysis image obtained in step P5 into N blocks with a side length of a pixels, where the value of a is [1, 8]. M2, Statistical analysis of the total number of pixels with a gray level of 1 in the image (AG) sum AG is the number of pixels with a gray level of 1 in each block. i (i=1, 2, 3...N-1, N), calculate the percentage of pixels with a gray level of 1 in each block out of the total number of pixels. ; M3. Define statistical matrix functions , where q is the order of the statistical matrix. When q is greater than 1, the statistical matrix function exhibits the singular characteristics of a densely distributed region. When q is less than 1, the statistical matrix function exhibits the characteristics of a sparsely distributed region. M(a,q) represents the value of the statistical matrix function when the block side length is a pixels and the order of the statistical matrix is ​​q. M4, Calculate the quality index function The calculation method involves drawing different blocks with side length 'a' pixels and a statistical matrix of order 'q'. Find the slope of the curve as a approaches 0; M5. Perform Legendre transformation calculation; the calculation formula is as follows: ， The singularity index is the mass index function for a statistical matrix of order q. The slope as a function of q The multifractal spectrum values ​​of the statistical matrix with order q are plotted for the values ​​of the statistical matrix with order q. The image is used to obtain the multifractal spectrum corresponding to the image of recycled aggregate; M6. Subtract the multifractal spectrum values ​​corresponding to the maximum and minimum singularity indices, and divide by the total number of pixels in the multifractal analysis image to obtain the multifractal spectrum feature values.

2. The method for recognizing the reshaping effect of recycled aggregate based on multifractal spectrum feature values ​​according to claim 1, characterized in that, The image acquisition platform is supported by an adjustable bracket for returning the sampled aggregate to the ball mill.

3. The method for identifying the reshaping effect of recycled aggregate based on multifractal spectrum feature values ​​according to claim 1, characterized in that, In step P3, the grayscale value is calculated using the sum of 0.39 times the red channel value, 0.5 times the green channel value, and 0.11 times the blue channel value.

4. The method for identifying the reshaping effect of recycled aggregate based on multifractal spectrum feature values ​​according to claim 1, characterized in that, The order q of the statistical matrix takes the value of an integer between -10 and 10.

5. The method for identifying the reshaping effect of recycled aggregate based on multifractal spectrum feature values ​​according to claim 1, characterized in that, The video system in step S3 uses a high-resolution camera.

Images