Detection and analysis method for rapidly dividing release stages of asphalt self-healing microcapsules

Abstract

The invention relates to the technical field of road engineering, in particular to a detection and analysis method for quickly dividing release stages of asphalt self-healing microcapsules, which comprises the following steps: acquiring an asphalt thin layer sample by adopting a standardized sample preparation process, controlling the thickness of the sample by using a constant pressure device, and acquiring a microcapsule distribution image by using a fluorescence microscope; fluorescence intensity standardization is achieved through image graying, noise filtering and binarization processing in sequence, and a third-level judgment standard is established; based on the intensity distribution characteristics and the average fluorescence intensity, the release process is subdivided into five stages of surface activation, diffusion regulation, stress response, cooperative dissipation and complete release. According to the method, fluorescence intensity analysis is innovatively introduced as an auxiliary judgment condition, and the release stage of the microcapsules is judged according to the average fluorescence intensity of the microcapsules. According to the method, the release stages of the asphalt self-healing microcapsules are rapidly divided, the influence mechanism of the release behavior of the microcapsules is revealed, and the application prospect is wide.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and particularly to a detection and analysis method for rapidly dividing the release stages of asphalt self-healing microcapsules. Background Art

[0002] Asphalt materials are widely used in road engineering, but they are prone to microcracks during long-term use. These cracks will accelerate the damage of the road surface structure and significantly shorten the service life. Traditional repair methods rely on manual patching, which has defects such as low efficiency and high cost. Therefore, the development of asphalt materials with self-healing ability has become a research hotspot in recent years. Among them, microcapsule technology realizes autonomous repair by encapsulating healing agents and shows important application potential. At present, self-healing asphalt materials mainly rely on the rupture of microcapsules at cracks to release healing agents. However, the release behavior of microcapsules is affected by various factors, including the characteristics of the shell material, the mixing ratio, and environmental conditions. Existing technologies generally use destructive tests to evaluate the release effect, but such methods cannot track the dynamic process in real time and may change the original state of the microcapsules. In addition, researchers mostly focus on the recovery of macroscopic mechanical properties and lack quantitative analysis of the microscopic release mechanism, resulting in a lack of theoretical guidance for material design and optimization.

[0003] In the research on the release behavior of microcapsules, there are obvious deficiencies in existing detection means. For example, thermogravimetric analysis can only measure the overall mass change and cannot distinguish the release stages of individual microcapsules; although spectroscopic techniques can detect chemical composition changes, the spatial resolution is insufficient to locate the release position. More importantly, existing methods cannot establish a standard for dividing the release stages, making it difficult to evaluate the influence of different design parameters on the release kinetics. On the other hand, the microcapsule release process is directly related to the self-healing efficiency of asphalt. Research shows that premature or late release will reduce the repair effect, but existing technologies cannot accurately control the release timing. For example, when the microcapsule content is too high, agglomeration will cause premature release in local areas, while insufficient content may not cover the crack propagation path. These problems highlight the necessity of developing a precise detection method, which needs to dynamically track the release process at the microscale and establish a quantitative evaluation system.

[0004] At the detection technology level, fluorescence labeling method has attracted much attention due to its high sensitivity and non-invasiveness. However, when applying it to the asphalt system, it faces technical challenges: the autofluorescence of the asphalt matrix will interfere with the signal, the uneven dispersion of microcapsules leads to blurred imaging, and there is a lack of standardized image processing procedures. Existing research has tried to identify microcapsules through threshold segmentation, but it does not consider environmental noise and sample preparation differences, resulting in insufficient reliability of the analysis results.

[0005] Based on the above technical bottlenecks, a detection and analysis method for rapidly dividing the release stages of asphalt self-healing microcapsules is proposed. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a detection and analysis method for quickly dividing the release stages of asphalt self-healing microcapsules, which solves the problem that the prior art cannot accurately track the dynamic release process of microcapsules, resulting in inaccurate evaluation of self-healing efficiency; this method innovatively integrates fluorescence image processing and stress response analysis technologies to achieve a five-stage quantitative determination of the release behavior of microcapsules.

[0007] To achieve the above object, the present invention provides the following technical solutions: A detection and analysis method for quickly dividing the release stages of asphalt self-healing microcapsules, including the following steps,

[0008] (1) Prepare a standardized specimen. Drop the microcapsule-modified asphalt heated to a flowing state in the center of a glass slide, and press it through a constant pressure device to form a thin-layer specimen with a thickness of 20 ± 3 μm.

[0009] (2) Use a fluorescence microscope equipped with a 40x objective lens to collect the image of the thin-layer specimen as a fluorescence image, ensuring that the numerical aperture of the objective lens is 0.75 and the exposure parameters remain constant.

[0010] (3) Perform standardized processing on the collected fluorescence images, including grayscale conversion, median filtering for denoising, and binarization processing.

[0011] (4) Establish a fluorescence intensity quantification model, and divide the release state of microcapsules through the normalized gray value, where the gray value of 0 - 255 corresponds to the normalization coefficient of 0 - 1.

[0012] (5) Divide the release stages based on the average fluorescence intensity threshold of the fluorescence image, including the surface activation stage, diffusion regulation stage, stress response stage, cooperative dissipation stage, and complete release stage.

[0013] Preferably, in step (1), the parameters of the constant pressure device are set as follows: the pressure is 0.5 MPa, and the continuous pressing time is 10 s; after the obtained thin-layer specimen is cooled to 25 ± 2 °C, image acquisition is carried out.

[0014] Preferably, in step (3), the standardized processing of the fluorescence image specifically includes,

[0015] Grayscale conversion processing: Use the weighted average method, and set the weight coefficients of the R, G, and B channels to 0.299, 0.587, and 0.114 respectively.

[0016] Median filtering for denoising: Use median filtering with a 3×3 pixel matrix.

[0017] Binarization processing: Set the binarization processing threshold to the normalized intensity value of 0.3, and pixels higher than this value are determined as the effective release area.

[0018] Preferably, the standard formula for determining the validity of the fluorescence image is established as follows:

[0019] A total ×θ2 ≤ A release ≤ A total ×θ1

[0020] In the formula: A release is the area of the bright region in the image; A total is the total area of the image; θ1 and θ2 are both area ratio thresholds. θ1 is set to 0.9, that is, when the area of the bright region exceeds 90%, it is considered that the microcapsules have abnormal release; θ2 is set to 0.1, that is, when the area of the bright region is less than 10%, it is considered that the microcapsules have ineffective release; when the fluorescence image of the microcapsule modified asphalt does not meet the above conditions, the data of this image is determined to be invalid.

[0021] Preferably, in step (4), the fluorescence intensity calculation formula for a single microcapsule / target region in the fluorescence image is as follows:

[0022]

[0023] In the formula: G i is the gray value of the i-th pixel in the target region; n is the total number of pixels in the target region; I target is the normalized fluorescence intensity value, ranging from 0 to 1, and is used to quantify the release degree of a single microcapsule or target region.

[0024] Preferably, in step (4), the average fluorescence intensity calculation formula for the entire fluorescence image is as follows:

[0025]

[0026] In the formula: G j is the gray value of the j-th microcapsule in the entire image, m is the total number of microcapsules in the entire fluorescence image, I global is the normalized average fluorescence intensity value, ranging from 0 to 1, and is used to quantify the overall release degree.

[0027] Preferably, in step (5), the release stage division criteria include

[0028] Surface activation stage: The average fluorescence intensity value of the thin layer specimen is in the range of 0.01 - 0.20;

[0029] Diffusion regulation stage: The average fluorescence intensity value of the thin layer specimen is in the range of 0.21 - 0.40;

[0030] Stress response stage: The average fluorescence intensity value of the thin layer specimen is in the range of 0.41 - 0.60;

[0031] Synergistic dissipation stage: The average fluorescence intensity value of the thin layer specimen is in the range of 0.61 - 0.80;

[0032] Complete release stage: The average fluorescence intensity value of the thin-layer specimen is in the range of 0.81 - 1.00.

[0033] Preferably, based on the proportion of the surface activation stage, the microcapsule modified asphalt is optimized:

[0034] When the proportion of the surface activation stage > 80%, it is determined that the microcapsule sealing is qualified, and no unexpected release occurs before the internal microcracks of the asphalt.

[0035] When the proportion of the surface activation stage is 50 - 80%, the preparation process parameters of the microcapsules need to be optimized, including adjusting the crosslinking agent concentration, increasing the curing temperature, or adding a surfactant to increase the proportion of the surface activation stage to > 80%.

[0036] When the proportion of the surface activation stage < 50%, it is determined that the integrity of the microcapsule fails, and the capsule wall material needs to be replaced or the asphalt mixing process needs to be adjusted, including using nano-silica to strengthen the capsule wall or reducing the stirring rate to 100 - 150 rpm to avoid mechanical damage.

[0037] The present invention provides an asphalt self-healing microcapsule release analysis system, including:

[0038] An image acquisition module, which consists of a fluorescence microscope, a CCD camera, and a thermostatic stage. The temperature control accuracy of the stage is ±0.5°C. The CCD camera is configured to collect 5 images per second, and the spatial resolution is not less than 2048×1536 pixels.

[0039] An image processing module that executes algorithm programs for gray conversion, noise filtering, and binarization processing.

[0040] A data analysis module with a built-in database corresponding to fluorescence intensity - release stage, including threshold intervals and typical image features of 5 stages.

[0041] The present invention provides a detection and analysis method for quickly dividing the release stage of asphalt self-healing microcapsules, which has the following beneficial effects compared with the prior art:

[0042] The present invention accurately divides the microcapsule release stage through the fluorescence intensity threshold, solving the defect of the traditional method relying on manual experience judgment. By dividing into five stages through the fluorescence intensity threshold, standardized and quantitative analysis is realized, subjective errors are avoided, and the detection cycle is shortened. Integrating specimen preparation, image processing, and stage determination simplifies the operation process. Compared with the traditional qualitative evaluation only, the present invention directly guides the adjustment of the microcapsule preparation process through the microcapsule fluorescence intensity, improving the product quality consistency. Combining the fluorescence microscope with the thermostatic stage, dynamic monitoring of the release stage is realized, and the agglomeration warning or process adjustment is triggered in a timely manner, realizing the full-process automation from detection to optimization, reducing the need for manual intervention, and improving the engineering applicability. Description of the Drawings

[0043] The accompanying drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0044] Figure 1 It is the apparent appearance of the microcapsules observed by the environmental scanning electron microscope of the present invention;

[0045] Figure 2 It is the fluorescence image of the standard sample of microcapsule modified asphalt in Example 3 of the present invention;

[0046] Figure 3 It is the fluorescence distribution of the standard sample of microcapsule modified asphalt in the surface activation stage of Example 3 of the present invention;

[0047] Figure 4 It is the fluorescence distribution of the standard sample of microcapsule modified asphalt in the diffusion regulation stage in Comparative Example 1 of the present invention;

[0048] Figure 5 It is the fluorescence distribution of the standard sample of microcapsule modified asphalt in the stress response stage in Comparative Example 1 of the present invention;

[0049] Figure 6 It is the fluorescence distribution of the standard sample of microcapsule modified asphalt in the cooperative dissipation stage in Comparative Example 1 of the present invention;

[0050] Figure 7 It is the fluorescence distribution of the standard sample of microcapsule modified asphalt in the complete release stage in Comparative Example 1 of the present invention;

[0051] Figure 8 It is the fluorescence image of the asphalt sample with an excessive microcapsule dosage in Comparative Example 2 of the present invention;

[0052] Figure 9 It is the equipment diagram of the fluorescence microscope of the present invention. Detailed implementation manners

[0053] The following examples are used to illustrate in detail the implementation manners of the present application, so as to fully understand the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects and implement accordingly.

[0054] Example 1

[0055] This example is the self-healing microcapsule of the present invention, and its preparation method is as follows:

[0056] Place 300 g of fly ash cenospheres (80 - 120 mesh) samples in an oven at 60 °C and dry them to a constant weight. Then take the samples out of the drying oven and wait until the surface temperature cools below 30 °C. Place the samples in a sealed vacuum drying oven and pretreat them for 40 min in an environment with a negative pressure of 0.1 MPa. Put the pretreated samples into a 200 - mesh mesh bag, and then immerse them in a container filled with epoxy soybean oil. Transfer the container to the vacuum drying oven, evacuate it to a vacuum state, and let it stand for 20 min in an environment with a negative pressure of 0.1 MPa until the fly ash cenospheres reach a saturated state. When the fly ash cenospheres are saturated, take the samples out of the solution and wipe the surface clear liquid with a wet cloth until they reach a surface - dry state. Sprinkle an aqueous solution of polyvinyl alcohol on the samples using a high - pressure sprayer for encapsulation treatment, and dry them at room temperature for 24 h to obtain the required asphalt self - healing microcapsule products. Observe the apparent appearance of the microcapsules using an environmental scanning electron microscope, as shown in Figure 1 。

[0057] Example 2

[0058] A method for preparing modified asphalt using self - healing microcapsules for asphalt, comprising:

[0059] Put 200 g of 70# base asphalt samples into a high - speed shearer, heat them to 140 °C, and then raise the temperature to 160 °C. Adjust the rotation speed of the high - speed shearer to 1500 rpm, and then slowly pour 6 g of self - healing microcapsules (prepared in Example 1) into it. After the self - healing microcapsules (prepared in Example 1) are completely added, adjust the rotation speed to 2000 rpm and maintain shear stirring for 20 min. After shearing is completed, put the obtained stirred material into an oven at 80 °C for swelling and development for 30 min to obtain modified asphalt with self - healing function.

[0060] Example 3

[0061] A detection and analysis method for quickly dividing the release stage of asphalt self - healing microcapsules, comprising:

[0062] (1) Prepare a standardized specimen. Drop the microcapsule - modified asphalt heated to a flowing state in the center of a glass slide, and press it through a constant - pressure device to form a thin - layer specimen with a thickness of 20 ± 3 μm. The parameters of the constant - pressure device are set as a pressure of 0.5 MPa and a continuous pressing time of 10 s. After the obtained thin - layer specimen cools to 25 ± 2 °C, perform image acquisition.

[0063] (2) Use a fluorescence microscope equipped with a 40 - fold objective lens to collect images of the thin - layer specimen as fluorescence images, as shown in Figure 2 shown, ensuring that the numerical aperture of the objective lens is 0.75 and the exposure parameters remain constant.

[0064] (3) Standardize the collected fluorescence images, including grayscale conversion, median filtering for denoising, and binarization; for grayscale conversion, the weighted average method is adopted, and the weight coefficients of the R, G, and B channels are set to 0.299, 0.587, and 0.114 respectively; for median filtering for denoising, median filtering with a 3×3 pixel matrix is used; the binarization threshold is set to the normalized intensity value of 0.3, and pixels higher than this value are determined as the effective release area.

[0065] Establish the following standard formula for determining the validity of fluorescence images:

[0066] A total ×θ2 ≤ A release ≤ A total ×θ1

[0067] In the formula: A release is the area of the bright area in the image; A total is the total area of the image; θ1 and θ2 are both area ratio thresholds, θ1 is set to 0.9 (i.e., when the bright area exceeds 90%, it is considered that the microcapsules have abnormal release); θ2 is set to 0.1 (i.e., when the bright area is less than 10%, it is considered that the microcapsules have ineffective release); when the fluorescence image of the microcapsule modified asphalt does not meet the above conditions, the data of this image is determined to be invalid.

[0068] (4) Establish a fluorescence intensity quantization model, and divide the release state of microcapsules by the grayscale value after normalization, where the grayscale value of 0 - 255 corresponds to the normalization coefficient of 0 - 1; the fluorescence intensity calculation formula for a single microcapsule / target area in the fluorescence image is as follows:

[0069]

[0070] In the formula: G i is the grayscale value of the i-th pixel in the target area; n is the total number of pixels in the target area; I target is the normalized fluorescence intensity value, ranging from 0 to 1, and is used to quantify the release degree of a single microcapsule or target area.

[0071] The calculation formula for the average fluorescence intensity of the entire fluorescence image is as follows:

[0072]

[0073] In the formula: G j is the grayscale value of the j-th microcapsule in the entire image, m is the total number of microcapsules in the entire fluorescence image, and I global is the normalized average fluorescence intensity value, ranging from 0 to 1, and is used to quantify the overall release degree.

[0074] (5) Divide the release stage based on the average fluorescence intensity threshold of the fluorescence image, including the surface activation stage, diffusion regulation stage, stress response stage, cooperative dissipation stage, and complete release stage; see the fluorescence intensity distribution in Figure 3 as shown;

[0075] When the average fluorescence intensity value of the thin-layer specimen is in the range of 0.01 - 0.20, it is determined that the microcapsules are in the surface activation stage at this time;

[0076] When the average fluorescence intensity value of the thin-layer specimen is in the range of 0.21 - 0.40, it is determined that the microcapsules are in the diffusion regulation stage at this time;

[0077] When the average fluorescence intensity value of the thin-layer specimen is in the range of 0.41 - 0.60, it is determined that the microcapsules are in the stress response stage at this time;

[0078] When the average fluorescence intensity value of the thin-layer specimen is in the range of 0.61 - 0.80, it is determined that the microcapsules are in the cooperative dissipation stage at this time;

[0079] When the average fluorescence intensity value of the thin-layer specimen is in the range of 0.81 - 1.00, it is determined that the microcapsules are in the complete release stage at this time.

[0080] Example 4

[0081] An asphalt self-healing microcapsule release analysis system, comprising:

[0082] An image acquisition module, composed of a fluorescence microscope, a CCD camera, and a thermostatic stage, with the temperature control accuracy of the stage being ±0.5°C, the CCD camera configured to acquire 5 frames of images per second, and the spatial resolution being not less than 2048×1536 pixels;

[0083] An image processing module, executing algorithm programs for gray-scale conversion, noise filtering, and binarization processing;

[0084] A data analysis module, with a built-in fluorescence intensity - release stage corresponding database, including the threshold intervals and typical image features of 5 stages (surface activation stage: the average fluorescence intensity value of the thin-layer specimen is in the range of 0.01 - 0.20; diffusion regulation stage: the average fluorescence intensity value of the thin-layer specimen is in the range of 0.21 - 0.40; stress response stage: the average fluorescence intensity value of the thin-layer specimen is in the range of 0.41 - 0.60; cooperative dissipation stage: the average fluorescence intensity value of the thin-layer specimen is in the range of 0.61 - 0.80; complete release stage: the average fluorescence intensity value of the thin-layer specimen is in the range of 0.81 - 1.00).

[0085] When the coefficient of variation of the microcapsule spacing distribution in the thin-layer specimen is ≥15%, the system automatically triggers an agglomeration warning, and its determination condition is that the proportion of the aggregation area where the microcapsule spacing ≤10μm in the local area is ≥20%.

[0086] Example 5

[0087] Based on the proportion of the surface activation stage, an optimization measure for microcapsule modified asphalt is provided:

[0088] When the proportion of the surface activation stage > 80%, it is determined that the microcapsule sealing is qualified, and no unexpected release occurs before the internal microcracks of the asphalt are generated;

[0089] When the proportion of the surface activation stage is 50 - 80%, the preparation process parameters of the microcapsules need to be optimized, including adjusting the crosslinking agent concentration, increasing the curing temperature, or adding a surfactant to increase the proportion of the surface activation stage to > 80%;

[0090] When the proportion of the surface activation stage < 50%, it is determined that the integrity of the microcapsule fails, and the capsule wall material needs to be replaced or the asphalt mixing process needs to be adjusted, including using nano - silica to strengthen the capsule wall or reducing the stirring rate to 100 - 150 rpm to avoid mechanical damage.

[0091] Comparative Example 1

[0092] The microcapsule modified asphalt specimen after being loaded and recovered by a dynamic shear rheometer was analyzed according to the detection and analysis method for quickly dividing the release stage of asphalt self - healing microcapsules in Example 3. The fluorescence intensity distribution is shown in Figures 4 - 7 as follows.

[0093] Comparative Example 2

[0094] The 5% dosage microcapsule modified asphalt specimen was analyzed according to the detection and analysis method for quickly dividing the release stage of asphalt self - healing microcapsules in Example 3. The fluorescence image is shown in Figure 8 as follows.

[0095] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A detection and analysis method for quickly dividing the release stage of asphalt self-healing microcapsules, characterized in that, It includes the following steps: (1) Prepare a standardized specimen. Drop the microcapsule-modified asphalt heated to a flowing state at the center of a glass slide, and press it through a constant-pressure device to form a thin-layer specimen with a thickness of 20 ± 3 μm; (2) Use a fluorescence microscope equipped with a 40× objective lens to collect the image of the thin-layer specimen as a fluorescence image, ensuring that the numerical aperture of the objective lens is 0.75 and the exposure parameters are kept constant; (3) Perform standardized processing on the collected fluorescence image, including grayscale conversion, median filtering for noise reduction, and binarization; (4) Establish a fluorescence intensity quantification model. Divide the microcapsule release state through the normalized grayscale value, where the grayscale value of 0 - 255 corresponds to the normalization coefficient of 0 - 1; (5) Divide the release stage based on the average fluorescence intensity threshold of the fluorescence image, including the surface activation stage, diffusion regulation stage, stress response stage, cooperative dissipation stage, and complete release stage.

2. The detection and analysis method for rapidly dividing the release stages of asphalt self-healing microcapsules according to claim 1, characterized in that, In step (1), the parameters of the constant-pressure device are set as follows: the pressure is 0.5 MPa, and the continuous pressing time is 10 s; after the obtained thin-layer specimen is cooled to 25 ± 2 °C, image acquisition is carried out.

3. The detection and analysis method for rapidly dividing the release stages of asphalt self-healing microcapsules according to claim 1, characterized in that, In step (3), the standardized processing of the fluorescence image specifically includes Grayscale conversion processing: Use the weighted average method, and set the weight coefficients of the R, G, and B channels to 0.299, 0.587, and 0.114 respectively; Median filtering for noise reduction: Use median filtering with a 3×3 pixel matrix; Binarization: The binarization threshold is set to the normalized intensity value of 0.3, and the pixels higher than this value are determined as the effective release area.

4. A detection and analysis method for quickly dividing the release stage of asphalt self-healing microcapsules according to claim 3, characterized in that The formula for establishing the validity determination criterion of the fluorescence image is as follows: A total ×θ2 ≤ A release ≤ A total ×θ1 Where: A release is the area of the bright region in the image; A total is the total area of the image; θ1 and θ2 are both area ratio thresholds. θ1 is set to 0.9, that is, when the area of the bright region exceeds 90%, it is considered that the microcapsules have abnormal release; θ2 is set to 0.1, that is, when the area of the bright region is less than 10%, it is considered that the microcapsules have ineffective release; when the fluorescence image of the microcapsule modified asphalt does not meet the above conditions, the data of this image is determined to be invalid.

5. The detection and analysis method for quickly dividing the release stages of asphalt self-healing microcapsules according to claim 1, wherein, In step (4), the formula for calculating the fluorescence intensity of a single microcapsule / target area in the fluorescence image is as follows: where: G i is the gray value of the i-th pixel in the target area; n is the total number of pixels in the target area; I target is the normalized fluorescence intensity value, ranging from 0 to 1, and is used to quantify the release degree of a single microcapsule or the target area.

6. The detection and analysis method for rapidly dividing the release stages of asphalt self-healing microcapsules according to claim 1, characterized in that, In step (4), the formula for calculating the average fluorescence intensity of the entire fluorescence image is as follows: Where: G j is the gray value of the j-th microcapsule in the whole image, m is the total number of microcapsules in the whole fluorescence image, I global is the normalized average fluorescence intensity value, ranging from 0 to 1, which is used to quantify the overall release degree.

7. A detection and analysis method for quickly dividing the release stage of asphalt self-healing microcapsules according to claim 1, characterized in that, In step (5), the release stage division criteria include Surface activation stage: The average fluorescence intensity value of the thin-layer specimen is in the range of 0.01 - 0.20; Diffusion regulation stage: The average fluorescence intensity value of the thin-layer specimen is in the range of 0.21 - 0.40; Stress response stage: The average fluorescence intensity value of the thin-layer specimen is in the range of 0.41 - 0.60; Cooperative dissipation stage: The average fluorescence intensity value of the thin-layer specimen is in the range of 0.61 - 0.80; Complete release stage: The average fluorescence intensity value of the thin-layer specimen is in the range of 0.81 - 1.

00.

8. A detection and analysis method for quickly dividing the release stage of asphalt self-healing microcapsules according to any one of claims 1-7, characterized in that Use an asphalt self-healing microcapsule release analysis system, including: Image acquisition module, which consists of a fluorescence microscope, a CCD camera, and a thermostatic stage. The temperature control accuracy of the stage is ±0.5 °C, the CCD camera is configured to collect 5 frames of images per second, and the spatial resolution is not less than 2048×1536 pixels; Image processing module, which executes the algorithm programs for grayscale conversion, noise filtering, and binarization; Data analysis module, which has a built-in fluorescence intensity - release stage corresponding database, including the threshold intervals and typical image features of 5 stages.

9. A method for detecting and analyzing the release stage of asphalt self-healing microcapsules according to claim 8, characterized in that, When the coefficient of variation of the microcapsule spacing distribution in the thin-layer specimen is ≥15%, the system automatically triggers an agglomeration warning, and its determination condition is that the proportion of the aggregation area where the microcapsule spacing is ≤10 μm in the local area is ≥20%; 10. A method for detecting and analyzing the release stage of asphalt self-healing microcapsules according to claim 7, characterized in that, Optimize the microcapsule-modified asphalt based on the proportion of the surface activation stage: When the proportion of the surface activation stage > 80%, it is determined that the microcapsule sealing is up to standard, and no unexpected release occurs before the internal microcracks of the asphalt are generated; When the proportion of the surface activation stage is 50 - 80%, it is necessary to optimize the microcapsule preparation process parameters, including adjusting the crosslinking agent concentration, increasing the curing temperature, or adding a surfactant, to increase the proportion of the surface activation stage to > 80%; When the proportion of the surface activation stage < 50%, it is determined that the integrity of the microcapsule fails, and it is necessary to replace the capsule wall material or adjust the asphalt mixing process, including using nano-silica to strengthen the capsule wall or reducing the stirring rate to 100 - 150 rpm to avoid mechanical damage.

Images