Asphalt test detector and control system thereof

Abstract

The invention relates to the field of material science, in particular to an asphalt test detector and a control system thereof.The asphalt test detector comprises a disease area infrared positioning module, an asphalt quality test module, a comprehensive evaluation module, a quality grading feedback module and a management database. The method comprises the following steps: respectively carrying out softening point detection, penetration detection and Englan viscosity detection on an asphalt sample to judge whether each quality index is qualified or not, carrying out comprehensive evaluation to obtain an asphalt comprehensive evaluation coefficient of each disease area of the asphalt pavement, determining a corresponding quality grade, and outputting a grading feedback result. Precise recognition and comprehensive quality detection of asphalt pavement diseases are realized, subjectivity and one-sidedness of manual detection are avoided, and a basis is provided for accurate evaluation of asphalt pavement conditions.

Description

Technical Field

[0001] The present invention relates to the field of material science, and in particular to an asphalt testing instrument and a control system thereof. Background Art

[0002] In the modern transportation system, asphalt pavement has become the mainstream choice for highway and urban road construction due to its good flatness, low noise and fast construction characteristics.

[0003] However, the performance of asphalt materials is significantly affected by temperature, load frequency and aging, which can easily cause road cracking, rutting and other defects. Therefore, accurately measuring the thermal stability, consistency, viscosity, fluidity and other performance indicators of asphalt through scientific experiments is the key to ensuring the long-term service performance of roads.

[0004] In recent years, with the widespread application of new materials and the higher requirements for road performance put forward by extreme climate and heavy traffic, asphalt testing technology is developing in the direction of refinement and intelligence, aiming to provide more reliable data support for road design and maintenance.

[0005] For example, the existing Chinese patent with publication number 202310312606.8 discloses a method for accepting waterproof asphalt. This scheme freezes and solidifies waterproof asphalt that is liquid at room temperature with fixed parameters, and then performs a tensile fracture test on the solidified waterproof asphalt under determined experimental parameters to verify whether the sample asphalt is qualified. The test can be completed in a relatively short time, effectively improving the test efficiency, adapting to the needs of large-scale waterproof asphalt quality testing, and accelerating the acceptance progress of engineering materials.

[0006] However, this plan still has the following shortcomings: First, this plan only randomly selects some locations for inspection, and there is no special link to locate the diseased areas. This may miss some areas with potential diseases. Potential diseases may gradually develop into obvious road surface diseases, such as enlarged cracks and the appearance of potholes, affecting the flatness and driving comfort of the road, and increasing the subsequent maintenance costs and difficulty.

[0007] 2. The acceptance of waterproof asphalt may lack a complete comprehensive evaluation system. It simply compares various indicators with standard values without considering the relationship between various indicators and the comprehensive impact on the overall performance of asphalt. It may be difficult to accurately judge the causes and trends of changes in asphalt performance, which will bring difficulties to subsequent maintenance and repair work. Summary of the Invention

[0008] In order to overcome the shortcomings of the background technology, an embodiment of the present invention provides an asphalt testing instrument and a control system thereof, which can effectively solve the problems involved in the above-mentioned background technology.

[0009] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an asphalt test detector, including: a diseased area infrared positioning module for scanning the asphalt pavement, locating the diseased area, and collecting asphalt samples in the diseased area.

[0010] The asphalt quality test module is used to perform softening point testing, needle penetration testing, and Engela viscosity testing on asphalt samples to determine whether various quality indicators are qualified.

[0011] The comprehensive evaluation module is used to comprehensively evaluate the asphalt comprehensive evaluation coefficient of each defect area of the asphalt pavement through the softening point, test penetration value and Engela viscosity of the asphalt samples in each defect area.

[0012] The quality grading feedback module is used to determine the corresponding quality grade based on the comprehensive evaluation coefficient of asphalt in each diseased area and output the grading feedback results.

[0013] Management database used to store diseased area data, asphalt sample data, comprehensive assessment and classification data.

[0014] Preferably, the specific analysis method of the infrared positioning module of the diseased area is: according to the set scanning range and path, the asphalt pavement is scanned area by area. During the scanning process, the reflected infrared signal is collected in real time and converted into temperature data to obtain the temperature of each area of the asphalt pavement, and the normal temperature range is set. The temperature of each area of the asphalt pavement is compared with the normal temperature range, and the areas of the asphalt pavement whose temperatures are not within the normal temperature range are screened out and recorded as abnormal temperature areas.

[0015] The temperature data features of each temperature abnormality area are extracted, and the temperature data features of each temperature abnormality area are taken as a data sample and stored in the disease candidate area data set. A judgment rule based on temperature difference is formulated, and each data sample in the disease candidate area data set is compared with the judgment rule. The diseased areas based on the judgment rule based on temperature difference are screened out to obtain the diseased areas of the asphalt pavement.

[0016] A total station is used to perform continuous mobile measurement along the boundaries of each defective area on the asphalt pavement, recording a series of coordinate points on the equipment's movement trajectory to form a boundary contour coordinate dataset of each defective area on the asphalt pavement, and adding a unique identification number to each defective area.

[0017] Preferably, the specific analysis method for collecting asphalt samples is: gridding the various diseased areas of the asphalt pavement, selecting the grid intersections as sampling points, drilling complete pavement core samples at each sampling point, mixing the pavement core samples at each sampling point and placing them into an asphalt extractor, separating the asphalt from the mineral material by adding an extraction solvent, and then transferring the extracted asphalt solution to a rotary evaporator, evaporating the solvent under set temperature and vacuum conditions to obtain asphalt samples from various diseased areas of the asphalt pavement.

[0018] Preferably, the specific analysis method for the softening point detection is: the asphalt samples from each diseased area of the asphalt pavement are divided into several asphalt samples, one of which is placed in a heating container, slowly heated to melt using a water bath method, and the melted asphalt sample is injected into a copper ring. After cooling to room temperature, the asphalt above the surface of the copper ring is scraped off with a preheated hot scraper to make the surface of the asphalt sample flat and smooth.

[0019] Place the copper ring containing the asphalt sample on the softening point tester bracket, pour in the heating medium to completely immerse it, place the steel ball in the center of the sample surface and start the heating and stirring device. When the steel ball falls to the set height, record the corresponding temperature to obtain the softening point of the asphalt sample.

[0020] Conduct multiple parallel tests according to the corresponding operation, and calculate the average value of the softening points obtained from each parallel test to obtain the softening point of the asphalt sample in each defective area of the asphalt pavement. Compare it with the softening point qualified range in the pre-set asphalt quality standard. If the softening point of the asphalt sample in a defective area is not within the qualified range, it means that the asphalt sample in the defective area is unqualified.

[0021] Preferably, the specific analysis method of the needle penetration test is: for the asphalt samples of each diseased area of the asphalt pavement, take an asphalt sample from it, heat it to a fluid state and then inject it into a sample dish, place the sample dish containing the asphalt sample on the sample platform of the needle penetration tester, and use a high-definition magnifying camera to capture images of the needle tip and the asphalt sample area in real time at a set frequency to obtain images of the needle tip and the sample surface at each time point.

[0022] The edge contour and grayscale value features of the needle tip and sample surface images at each time point are extracted and compared with the corresponding preset conditions. The time points where both features meet the conditions are selected, and the first time point is recorded as the contact moment.

[0023] Through the needle penetration tester, the standard needle begins to penetrate the asphalt sample vertically at the moment of contact, and the needle penetration value is recorded synchronously, thereby obtaining the test needle penetration value of the asphalt sample in each diseased area of the asphalt pavement.

[0024] The test penetration values of the asphalt samples of each diseased area are compared with the set standard penetration range. If the test penetration value of the asphalt sample of a diseased area is not within the set standard penetration range, it means that the asphalt sample of the diseased area is unqualified.

[0025] Preferably, the preset conditions include changes in edge contours between the needle tip and the sample surface, and changes in color grayscale differences.

[0026] The images of the needle tip and the sample surface at each time point are grayscaled, and the edge contours of the needle tip and the sample surface are extracted using an edge detection algorithm. The edge point information is stored in the form of pixel coordinates to form the edge contour data at each time point.

[0027] The needle tip at adjacent time points is matched one-to-one with the edge points in the sample surface image. The horizontal and vertical offset distances of each group of edge points at adjacent time points are calculated according to the pixel coordinates. The comprehensive offset distance of the edge points at each time point is calculated using the Euclidean distance formula.

[0028] A threshold for the number of edge point offset pixels is set. If the comprehensive offset distance of the edge points at a certain time point is greater than the set threshold for the number of edge point offset pixels, the time point is recorded as a possible contact moment. In this way, the images of the needle tip and the sample surface at each adjacent time point are traversed to obtain a list of possible contact moments of edge contour changes.

[0029] The region of interest (ROI) of the needle tip contact area is delineated in the image of the needle tip and the sample surface at each time point. For the ROI, the average color grayscale value of the ROI at each time point is calculated, and the rate of change of the grayscale value of the ROI at each time point is obtained by comparing it with adjacent time points.

[0030] A grayscale change threshold is set. When the rate of change of the grayscale value of the region of interest at a certain time point is greater than the set grayscale change threshold, the time point is recorded as a possible contact moment. In this way, the images of the needle tip and the sample surface at each adjacent time point are traversed to obtain a list of possible contact moments of grayscale change.

[0031] Filter the time points that exist in both the list of possible contact moments due to edge contour changes and the list of possible contact moments due to grayscale changes, sort them in chronological order, and select the first time point as the contact moment.

[0032] Preferably, the specific analysis method of the Engla viscosity test is: take an asphalt sample, emulsify it by heating it in a water bath, pour the evenly stirred emulsified asphalt sample into the viscometer container, monitor the actual temperature of the asphalt sample in real time, and when the temperature of the asphalt sample reaches the set temperature, open the outflow hole piston and start the stopwatch at the same time. When the asphalt sample flows out and reaches the specified scale line, stop the timing to obtain the outflow time of the asphalt sample, and calculate the Engla viscosity of the asphalt sample in each diseased area of the asphalt pavement based on the calibration coefficient of the Engla viscometer and the outflow time of the asphalt sample.

[0033] The Engla viscosity of the asphalt samples of each diseased area is compared with the set standard qualified range. If the Engla viscosity of the asphalt sample of a diseased area is not within the set standard qualified range, it means that the asphalt sample of the diseased area is unqualified.

[0034] Preferably, the specific analysis method of the comprehensive evaluation module is: obtaining the judgment results of asphalt samples in each diseased area of the asphalt pavement.

[0035] Determine whether any key quality indicator of the asphalt samples in each diseased area, including the softening point, test penetration value, and Engela viscosity, is unqualified. If any key quality indicator is unqualified, the overall quality of the asphalt is determined to be unqualified.

[0036] If the softening point, test penetration value and Engela viscosity of the asphalt samples in each defective area are all qualified, the asphalt comprehensive evaluation coefficient of each defective area of the asphalt pavement is comprehensively evaluated.

[0037] Preferably, the specific analysis method of the quality grading feedback module is: respectively obtaining the softening point, test penetration value and Engela viscosity of the asphalt samples of each diseased area of the asphalt pavement, and comprehensively evaluating through weighted calculation to obtain the asphalt comprehensive evaluation coefficient of each diseased area of the asphalt pavement.

[0038] Different coefficient intervals are set, each coefficient interval corresponds to a quality grade, and the quality grade corresponding to each diseased area of the asphalt pavement is determined according to the interval of the asphalt comprehensive evaluation coefficient of each diseased area of the asphalt pavement, and the graded feedback results are output.

[0039] Preferably, the present invention provides an asphalt test detection control system, characterized in that it includes any one of the asphalt test detectors described above.

[0040] Compared with the existing technology, the present invention has the following beneficial effects: 1. By scanning the asphalt pavement, locating the diseased area, and collecting asphalt samples, the present invention can accurately find the specific location of the problem in the asphalt pavement, providing a basis for subsequent targeted detection and treatment, avoiding blind inspection, and improving work efficiency.

[0041] 2. The present invention conducts softening point testing, needle penetration testing, and Engela viscosity testing on asphalt samples to determine whether various quality indicators are qualified. It can comprehensively evaluate the performance of asphalt from different aspects and accurately determine whether its various quality indicators meet the requirements.

[0042] 3. The present invention obtains the asphalt comprehensive evaluation coefficient of each diseased area of the asphalt pavement through comprehensive evaluation, determines the corresponding quality grade, and outputs the graded feedback result. The comprehensive evaluation takes into account multiple detection indicators, avoids the one-sidedness of single indicator evaluation, and provides a comprehensive and unified evaluation standard for asphalt pavement quality, making the asphalt pavement quality in different areas comparable. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 This is a system module connection diagram of the present invention.

[0045] Figure 2 for Figure 1 Flowchart of the infrared positioning module for diseased areas.

[0046] Figure 3 for Figure 1 Flowchart of the comprehensive assessment module. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, an asphalt test detector includes a diseased area infrared positioning module, an asphalt quality test module, a comprehensive evaluation module, a quality grading feedback module, and a management database.

[0049] The management database is connected to the infrared positioning module of the diseased area, the asphalt quality test module, the comprehensive evaluation module, and the quality grading feedback module; the asphalt quality test module is connected to the infrared positioning module of the diseased area and the comprehensive evaluation module; and the quality grading feedback module is connected to the comprehensive evaluation module.

[0050] See also Figure 2 As shown, the specific analysis method of the infrared positioning module for the diseased area is as follows: according to the set scanning range and path, the asphalt pavement is scanned area by area. During the scanning process, the reflected infrared signal is collected in real time and converted into temperature data to obtain the temperature of each area of the asphalt pavement. The normal temperature range is set, and the temperature of each area of the asphalt pavement is compared with the normal temperature range. The areas of the asphalt pavement whose temperatures are not within the normal temperature range are screened out and recorded as abnormal temperature areas. The area-by-area scanning can fully cover the asphalt pavement to ensure that no area with possible problems is missed, which provides a guarantee for the accurate discovery of diseases.

[0051] The temperature data features of each temperature anomaly area are extracted, and the temperature data features of each temperature anomaly area are taken as a data sample and stored in the disease candidate area data set. A judgment rule based on temperature difference is formulated, and each data sample in the disease candidate area data set is compared with the judgment rule. The disease areas based on the judgment rule based on temperature difference are screened out to obtain the disease areas of the asphalt pavement. The judgment rule based on temperature difference is formulated to make the screening of disease areas more scientific and accurate, reduce the interference of human factors, and improve the reliability of disease identification.

[0052] It should be noted that the temperature data features include mean, median, standard deviation, extreme value, area, temperature gradient, etc.

[0053] The judgment rule adopts a multi-rule combination method, comprehensively considering multiple temperature data features to improve the accuracy of the judgment. By reading each data sample from the disease candidate area dataset, its various temperature data features are compared with the corresponding thresholds in the judgment rule. According to the comparison results, it is judged whether the data sample meets the judgment rule. If it does, the corresponding temperature abnormal area is marked as a disease area. If not, the area is excluded.

[0054] It should be noted that, in a specific embodiment, rule combination one: considers average temperature, temperature standard deviation and regional area.

[0055] Rule 1: The average temperature is above the upper limit of the normal temperature range.

[0056] Rule 2: If the temperature standard deviation is greater than the set value, it means that the temperature distribution in the area is uneven.

[0057] Rule 3: If the area of the region is greater than the minimum area threshold, it means that the disease has affected a certain area.

[0058] Judgment: When a temperature abnormality area satisfies rules 1, 2 and 3 at the same time, the area is judged as a diseased area.

[0059] A total station is used to conduct continuous mobile measurements along the boundaries of each defective area on the asphalt pavement, recording a series of coordinate points on the equipment's movement trajectory to form a boundary contour coordinate data set for each defective area on the asphalt pavement. A unique identification number is added to each defective area, facilitating the classification, management, and tracking of different defective areas. In subsequent maintenance and review work, specific defective areas can be quickly and accurately located, improving management efficiency.

[0060] The specific analysis method for collecting asphalt samples is as follows: gridding each diseased area of the asphalt pavement, selecting grid intersections as sampling points, drilling complete pavement core samples at each sampling point, mixing the pavement core samples at each sampling point and placing them into an asphalt extractor, separating the asphalt from the mineral material by adding an extraction solvent, and then transferring the extracted asphalt solution to a rotary evaporator. Under set temperature and vacuum conditions, the solvent is evaporated to obtain asphalt samples from each diseased area of the asphalt pavement. The method can accurately separate and extract asphalt from the pavement core samples to obtain pure asphalt samples, providing high-quality samples for subsequent tests.

[0061] The specific analysis method for the softening point detection is as follows: asphalt samples from each diseased area of the asphalt pavement are divided into several asphalt samples, one of which is placed in a heating container, slowly heated in a water bath until melted, and the melted asphalt sample is injected into a copper ring. After cooling to room temperature, the asphalt above the surface of the copper ring is scraped off with a preheated hot scraper to make the surface of the asphalt sample flat and smooth. Preparing a flat and smooth asphalt sample is conducive to ensuring good contact between the sample and the test equipment during the softening point test and other processes, ensuring the accuracy of the test results, and also facilitating operation and observation.

[0062] Place the copper ring containing the asphalt sample on the softening point tester bracket, pour in the heating medium to completely immerse it, place the steel ball in the center of the sample surface and start the heating and stirring device. When the steel ball falls to the set height, record the corresponding temperature to obtain the softening point of the asphalt sample; measuring the softening point according to the standard test method can accurately reflect the performance changes of asphalt under heating conditions. The softening point is an important indicator to measure the high-temperature stability of asphalt. By accurately measuring the softening point, it can provide key data for evaluating the performance of asphalt pavement in high-temperature environments.

[0063] Conduct multiple parallel tests according to the corresponding operation, and calculate the average value of the softening points obtained from each parallel test to obtain the softening point of the asphalt sample in each diseased area of the asphalt pavement, and compare it with the qualified range of the softening point in the pre-set asphalt quality standard. If the softening point of the asphalt sample in a diseased area is not within the qualified range, it means that the asphalt sample in the diseased area is unqualified; conducting multiple parallel tests and calculating the average value can reduce experimental errors and improve the reliability and accuracy of the softening point measurement results.

[0064] The specific analysis method of the needle penetration test is as follows: for the asphalt samples in each diseased area of the asphalt pavement, an asphalt sample is taken from the asphalt sample, heated to a fluid state and then injected into a sample dish, the sample dish containing the asphalt sample is placed on the sample platform of the needle penetration tester, and a high-definition magnifying camera is used to capture images of the needle tip and the asphalt sample area in real time at a set frequency to obtain images of the needle tip and the sample surface at each time point; the high-definition magnifying camera captures images in real time, which can accurately record image information during the contact process between the needle tip and the asphalt sample, providing a detailed data basis for subsequent accurate analysis of the needle penetration and helping to capture subtle changes and features.

[0065] The edge contour and grayscale value features of the needle tip and sample surface images at each time point are extracted and compared with the corresponding preset conditions. The time points where both features meet the conditions are screened out, and the first time point is recorded as the contact moment. By extracting the edge contour and grayscale value features and comparing them with the preset conditions, the contact moment between the needle tip and the asphalt sample can be accurately determined. This is a key step in accurately measuring needle penetration, avoiding errors in human judgment and improving the accuracy of needle penetration detection.

[0066] Through the needle penetration tester, the standard needle begins to penetrate the asphalt sample vertically at the moment of contact, and the needle penetration value is recorded synchronously, thereby obtaining the test needle penetration value of the asphalt sample in each diseased area of the asphalt pavement; after the contact moment is accurately determined, the needle penetration measurement is performed and the needle penetration value is recorded synchronously, which can accurately obtain the needle penetration data of the asphalt sample. The needle penetration reflects the hardness and consistency of the asphalt, providing an important indicator for evaluating the performance of asphalt.

[0067] The test penetration values of the asphalt samples of each diseased area are compared with the set standard penetration range. If the test penetration value of the asphalt sample of a diseased area is not within the set standard penetration range, it means that the asphalt sample of the diseased area is unqualified. Comparing the test penetration value with the standard range provides a clear basis for judging the quality of the asphalt sample, which helps to promptly discover asphalt samples that do not meet the quality requirements so that appropriate measures can be taken to deal with them.

[0068] The preset conditions include changes in edge contours between the needle tip and the sample surface, and changes in color grayscale differences.

[0069] The images of the needle tip and the sample surface at each time point are grayscaled, and the edge contours of the needle tip and the sample surface are extracted using an edge detection algorithm. The edge point information is stored in the form of pixel coordinates to form the edge contour data of each time point. Grayscale processing and edge contour extraction of the images are performed and stored as edge contour data, which helps to further analyze the interaction process between the needle tip and the sample, provides data support for optimizing the needle penetration detection method and improving the detection accuracy, and also facilitates the comparison and analysis of needle penetration data of different diseased areas.

[0070] It should be noted that the specific analysis method of the edge detection algorithm is: calculating the gradient amplitude and direction of the needle tip and sample surface image at each time point, and determining the gradient intensity and direction information of each pixel point.

[0071] Apply non-maximum suppression to refine edge lines, remove non-edge pixels, and retain true edges.

[0072] Using a dual-threshold algorithm, we set two thresholds, high and low, to determine and connect edges. Pixels with gradient amplitudes greater than the high threshold are identified as edge points, while pixels with gradient amplitudes less than the low threshold are excluded. Pixels between the two are determined to be edge points based on their connection with the high-threshold edge points.

[0073] It should be noted that the gradient calculation uses the Sobel operator, which performs convolution operations on the seam area image in the horizontal and vertical directions through two 3×3 convolution kernels to obtain the approximate gradient values of the seam area image in the horizontal and vertical directions. , through the formula Calculate the gradient magnitude, where Indicates the The number of the seam area image, .

[0074] It should be noted that the specific analysis method of the non-maximum suppression is: starting from the upper left corner pixel of the needle tip and the sample surface image at each time point, traverse each pixel point row by row and column by column, and for each traversed pixel point, determine the gradient direction interval in which it is located according to the gradient amplitude and direction of the needle tip and the sample surface image at each time point, and compare the gradient amplitude of the pixel point with that of the adjacent pixel points in the gradient direction. If the gradient amplitude of the current pixel point is greater than the gradient amplitudes of the two adjacent pixel points in the gradient direction, retain the pixel point as an edge point; otherwise, judge the pixel point as a non-edge point and set its gradient amplitude to 0.

[0075] Set high and low thresholds for the gradient amplitude respectively, traverse the pixel points of the needle tip and sample surface image at each time point after non-maximum suppression, mark the pixel points with gradient amplitude greater than the high threshold as strong edge points, mark the pixel points with gradient amplitude between the low and high thresholds as weak edge points, and mark the pixel points with gradient amplitude less than the low threshold as non-edge points.

[0076] If a weak edge point is connected to at least one strong edge point, the weak edge point is retained as an edge point; otherwise, it is regarded as a noise point and removed from the edge. The edge profile of the needle tip and the sample surface at each time point is obtained by connecting the edge points that are above the low threshold and connected to the edge that is above the high threshold.

[0077] The edge contours of the needle tip and the sample surface at each time point in the image are identified, and the edge point information is stored in the form of pixel coordinates to form edge contour data.

[0078] The needle tip at adjacent time points is matched one-to-one with the edge points in the sample surface image, and the horizontal and vertical offset distances of each group of edge points at adjacent time points are calculated based on the pixel coordinates. The comprehensive offset distance of the edge points at each time point is calculated using the Euclidean distance formula. This can accurately quantify the relative position changes between the needle tip and the sample surface at different times, providing a reliable data basis based on geometric position changes for subsequent judgment of possible contact moments, helping to capture subtle morphological changes on the sample surface and improving the accuracy of judgments on contact-related moments.

[0079] It should be noted that the specific analysis method of the comprehensive offset distance of edge points at each time point is as follows: the offset distances of each group of edge points at adjacent time points in the horizontal direction and the vertical direction are recorded as ,in Indicates the The number of the edge point of the group, , through the formula Get the comprehensive offset distance of edge points at each time point .

[0080] A threshold for the number of edge point offset pixels is set. If the comprehensive offset distance of the edge points at a certain time point is greater than the set threshold for the number of edge point offset pixels, the time point is recorded as a possible contact moment. In this way, the images of the needle tip and the sample surface at each adjacent time point are traversed to obtain a list of possible contact moments when the edge contour changes. The set threshold is used to screen possible contact moments, and the moments when the edge contour changes significantly and contact may occur can be quickly identified from a large amount of time point data. The formation of a list is convenient for subsequent further analysis and processing, which improves the efficiency and pertinence of contact moment judgment and eliminates the interference of time points with small and negligible edge point offsets.

[0081] The region of interest of the needle tip contact area is delineated in the image of the needle tip and the sample surface at each time point. For the region of interest, the average color grayscale value of the region of interest at each time point is calculated respectively, and the rate of change of the grayscale value of the region of interest at each time point is obtained by comparing it with the adjacent time points. Delineating the region of interest and calculating the grayscale value and its rate of change can analyze the interaction between the needle tip and the sample surface from the color grayscale characteristics of the image. The change in grayscale value reflects, to a certain extent, the change in surface properties during the contact process. By calculating the rate of change, the dynamic process of this change can be captured more keenly, providing another dimension of analysis basis for judging the possible contact moment, and increasing the accuracy and reliability of the judgment.

[0082] It should be noted that the specific analysis method for the rate of change of the grayscale value of the region of interest at each time point is: compare the average grayscale value of the region of interest at each time point with the average grayscale value of the previous time point and the next time point, calculate the difference between the average grayscale value of the current time point and the adjacent time point, then divide the difference by the average grayscale value of the previous time point, and finally multiply by 100% to obtain the rate of change of the grayscale value of the region of interest at each time point.

[0083] A grayscale change threshold is set. When the rate of change of the grayscale value of the region of interest at a certain time point is greater than the set grayscale change threshold, the time point is recorded as the possible contact moment. In this way, the images of the needle tip and the sample surface at each adjacent time point are traversed to obtain a list of possible contact moments with grayscale changes. Similar to the edge point offset, a grayscale change threshold is set to screen possible contact moments. From the perspective of grayscale change, the time points where possible contact may occur can be quickly screened out to form a list, making the analysis based on the grayscale change characteristics of the image more systematic and efficient, helping to further narrow the range of possible contact moments and providing richer information for accurately determining the contact moment.

[0084] The time points that exist in both the list of possible contact moments of edge contour changes and the list of possible contact moments of grayscale changes are screened, sorted in chronological order, and the first time point is selected as the contact moment; by integrating the possible contact moment lists obtained from two different dimensions (edge contour changes and grayscale changes), the time points that meet both conditions are screened out, thereby improving the accuracy and reliability of determining the contact moment, because the judgment of a single dimension may lead to misjudgment or interference, while the integration of the two dimensions can verify and complement each other, and the first time point is selected in chronological order as the contact moment, which provides a clear and reasonable standard for determining the contact moment, making the determination of the contact moment more scientific and standardized.

[0085] The specific analytical method for the Engla viscosity test is as follows: an asphalt sample is emulsified by heating in a water bath, the uniformly stirred emulsified asphalt sample is poured into the viscometer container, and the actual temperature of the asphalt sample is monitored in real time. When the asphalt sample temperature reaches the set temperature, the outflow stopcock is opened and a stopwatch is started. When the asphalt sample has flowed out and reaches the specified scale line, the stopwatch is stopped to obtain the outflow time of the asphalt sample. The Engla viscosity of the asphalt sample in each defective area of the asphalt pavement is calculated based on the calibration coefficient of the Engla viscometer and the outflow time of the asphalt sample. The detailed and standardized process of the Engla viscosity test, from sample preparation (emulsification, stirring, etc.) to temperature monitoring, timing, and final viscosity calculation, ensures the accuracy and repeatability of the test process, resulting in relatively consistent results. This provides a reliable basis for accurately assessing the Engla viscosity of asphalt samples in various defective areas of asphalt pavement, and furthermore, provides an important parameter for judging the quality of asphalt samples.

[0086] The Engla viscosity of the asphalt samples in each diseased area is compared with a set standard qualified range. If the Engla viscosity of the asphalt sample in a diseased area is not within the set standard qualified range, it means that the asphalt sample in the diseased area is unqualified. By comparing with the standard qualified range, it is possible to quickly and intuitively determine whether the Engla viscosity of the asphalt sample in each diseased area is qualified, thereby making a preliminary screening and judgment on the quality of the asphalt sample, helping to timely discover unqualified asphalt samples, providing a basis for subsequent processing (such as further testing or replacement, etc.), ensuring that the quality of the asphalt material used meets the requirements, and is of great significance to the quality control of road projects.

[0087] See also Figure 3 As shown, the specific analysis method of the comprehensive evaluation module is: obtaining the judgment results of asphalt samples in each diseased area of the asphalt pavement.

[0088] Determine whether any of the key quality indicators (softening point, test penetration value, and Engla viscosity) of the asphalt samples in each diseased area fail to meet the standards. If any of the key quality indicators fail to meet the standards, the asphalt is deemed to be unqualified overall. Comprehensively assess the asphalt quality based on multiple key quality indicators (softening point, test penetration value, and Engla viscosity). If any one indicator fails to meet the standards, the asphalt is deemed to be unqualified overall. This rigorous assessment standard can more comprehensively and accurately reflect the quality of the asphalt, avoiding possible quality misjudgments that may occur by considering only a single indicator. It helps ensure that the asphalt materials used in asphalt pavements meet the requirements in all key performance aspects, thereby improving the quality and durability of road projects.

[0089] If the softening point, test penetration value and Engela viscosity of the asphalt samples in each defective area are all qualified, the asphalt comprehensive evaluation coefficient of each defective area of the asphalt pavement is comprehensively evaluated.

[0090] In a preferred embodiment of the present invention, the specific method of comprehensively evaluating the asphalt comprehensive evaluation coefficient of each defective area of the asphalt pavement is as follows: extracting the softening point, test needle penetration value, and Engela viscosity of the asphalt samples of each defective area of the asphalt pavement, and then summing them up according to the weights to obtain the asphalt comprehensive evaluation coefficient of each defective area of the asphalt pavement.

[0091] For example, the weights corresponding to the softening point, test penetration value, and Engela viscosity of the asphalt sample are .

[0092] The specific analysis method of the quality grading feedback module is as follows: the softening point, test penetration value, and Engela viscosity of asphalt samples in each diseased area of the asphalt pavement are obtained respectively, and a comprehensive evaluation is performed through weighted calculation to obtain a comprehensive evaluation coefficient for asphalt in each diseased area of the asphalt pavement. The comprehensive evaluation coefficient is obtained by weighted calculation of multiple key quality indicators, which can fully consider the different degrees of influence of each indicator on asphalt quality and comprehensively reflect the overall quality of the asphalt. Compared with single indicator evaluation, the comprehensive evaluation method is more scientific and comprehensive, can more accurately quantify the quality level of asphalt, and provide a more reliable basis for subsequent quality grading.

[0093] Different coefficient intervals are set, and each coefficient interval corresponds to a quality grade. The quality grade corresponding to each diseased area of the asphalt pavement is determined based on the interval of the asphalt comprehensive evaluation coefficient of each diseased area of the asphalt pavement, and the graded feedback results are output; by setting the coefficient intervals and the corresponding quality grades, the comprehensive evaluation coefficients are converted into intuitive quality grades, which is convenient for users to quickly understand the quality of asphalt in each diseased area. The output of the graded feedback results can provide a clear reference for relevant decision-making, which helps to improve the overall quality and management efficiency of the asphalt pavement.

[0094] It should be noted that the present invention provides an asphalt test detection control system, characterized in that it includes any one of the asphalt test detectors described above.

[0095] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered by the scope of protection of the present invention.

Claims

1. An asphalt test detector, characterized in that: include: The infrared positioning module for the damaged area is used to scan the asphalt pavement, locate the damaged area, and collect asphalt samples in the damaged area; Asphalt quality test module, used to test the softening point, needle penetration and Engela viscosity of asphalt samples, so as to determine whether the various quality indicators are qualified; The comprehensive evaluation module is used to comprehensively evaluate the asphalt comprehensive evaluation coefficient of each defect area of the asphalt pavement based on the softening point, test penetration value and Engela viscosity of the asphalt samples in each defect area; The quality grading feedback module is used to determine the corresponding quality grade based on the comprehensive evaluation coefficient of asphalt in each diseased area and output the grading feedback results; Management database used to store diseased area data, asphalt sample data, comprehensive assessment and classification data.

2. The asphalt test detector according to claim 1, characterized in that: The specific analysis method of the diseased area infrared positioning module is as follows: The asphalt pavement is scanned area by area according to the set scanning range and path. During the scanning process, the reflected infrared signals are collected in real time and converted into temperature data to obtain the temperature of each area of the asphalt pavement. The normal temperature range is set, and the temperature of each area of the asphalt pavement is compared with the normal temperature range. The areas of the asphalt pavement where the temperature is not within the normal temperature range are screened out and recorded as temperature abnormal areas. Extract the temperature data features of each temperature abnormality area, treat each temperature abnormality area as a data sample, store it in the disease candidate area data set, formulate a judgment rule based on temperature difference, compare each data sample in the disease candidate area data set with the judgment rule, screen out the disease areas based on the temperature difference judgment rule, and obtain the disease areas of the asphalt pavement; A total station is used to perform continuous mobile measurement along the boundaries of each defective area on the asphalt pavement, recording a series of coordinate points on the equipment's movement trajectory to form a boundary contour coordinate dataset of each defective area on the asphalt pavement, and adding a unique identification number to each defective area.

3. The asphalt test detector according to claim 2, characterized in that: The specific analysis method for collecting asphalt samples is as follows: The diseased areas of the asphalt pavement are divided into grids, and the grid intersections are selected as sampling points. Complete pavement core samples are drilled at each sampling point. The pavement core samples at each sampling point are mixed and placed in an asphalt extractor. The asphalt is separated from the mineral material by adding an extraction solvent. The extracted asphalt solution is then transferred to a rotary evaporator. Under the set temperature and vacuum conditions, the solvent is evaporated to obtain asphalt samples from various diseased areas of the asphalt pavement.

4. The asphalt test detector according to claim 1, characterized in that: The specific analysis method of the softening point detection is: Asphalt samples from each defective area of the asphalt pavement are divided into several asphalt samples. One of them is placed in a heating container and slowly heated in a water bath until it melts. The melted asphalt sample is poured into a copper ring. After cooling to room temperature, the asphalt above the copper ring surface is scraped off with a preheated hot scraper to make the asphalt sample surface flat and smooth. Place the copper ring containing the asphalt sample on the support of the softening point tester, pour in the heating medium to completely immerse it, place the steel ball at the center of the sample surface and start the heating and stirring device. When the steel ball falls to the set height, record the corresponding temperature to obtain the softening point of the asphalt sample; Conduct multiple parallel tests according to the corresponding operation, and calculate the average value of the softening points obtained from each parallel test to obtain the softening point of the asphalt sample in each defective area of the asphalt pavement. Compare it with the softening point qualified range in the pre-set asphalt quality standard. If the softening point of the asphalt sample in a defective area is not within the qualified range, it means that the asphalt sample in the defective area is unqualified.

5. The asphalt test detector according to claim 4, characterized in that: The specific analysis method of the needle penetration test is: Take an asphalt sample from each defective area of the asphalt pavement, heat it to a fluid state, and then pour it into a sample dish. The sample dish containing the asphalt sample is placed on the sample platform of the needle penetration tester. Use a high-definition magnifying camera to capture images of the needle tip and the asphalt sample area in real time at a set frequency to obtain images of the needle tip and the sample surface at each time point. The edge contour and grayscale value features of the needle tip and sample surface images at each time point are extracted and compared with the corresponding preset conditions. The time point where both features meet the conditions is selected and the first time point is recorded as the contact moment. Through the needle penetration tester, the standard needle begins to vertically penetrate the asphalt sample at the moment of contact, and the needle penetration value is recorded synchronously, thereby obtaining the test needle penetration value of the asphalt sample in each diseased area of the asphalt pavement; The test penetration values of the asphalt samples of each diseased area are compared with the set standard penetration range. If the test penetration value of the asphalt sample of a diseased area is not within the set standard penetration range, it means that the asphalt sample of the diseased area is unqualified.

6. The asphalt tester according to claim 5, characterized in that: The preset conditions include changes in edge contours between the needle tip and the sample surface, and changes in color grayscale differences. Grayscale processing is performed on the images of the needle tip and the sample surface at each time point, and the edge contours of the needle tip and the sample surface are extracted using an edge detection algorithm. The edge point information is stored in the form of pixel coordinates to form the edge contour data at each time point; The needle tip at adjacent time points is matched one-to-one with the edge points in the sample surface image. The offset distances of each group of edge points in the horizontal and vertical directions at adjacent time points are calculated based on the pixel coordinates. The comprehensive offset distance of the edge points at each time point is calculated using the Euclidean distance formula. A threshold value for the number of pixel offsets of edge points is set. If the integrated offset distance of edge points at a certain time point is greater than the set threshold value for the number of pixel offsets of edge points, this time point is recorded as a possible contact moment. In this way, the images of the needle tip and the sample surface at each adjacent time point are traversed to obtain a list of possible contact moments of edge contour changes. Delineating a region of interest (ROI) where the needle tip contacts the sample surface at each time point in the image. Calculating the average grayscale value of the ROI at each time point and comparing it with adjacent time points yields a rate of change in the grayscale value of the ROI at each time point. A grayscale change threshold is set. When the grayscale change rate of the region of interest at a certain time point is greater than the set grayscale change threshold, the time point is recorded as a possible contact moment. In this way, the images of the needle tip and the sample surface at each adjacent time point are traversed to obtain a list of possible contact moments with grayscale changes. Filter the time points that exist in both the list of possible contact moments due to edge contour changes and the list of possible contact moments due to grayscale changes, sort them in chronological order, and select the first time point as the contact moment.

7. The asphalt test detector according to claim 6, characterized in that: The specific analysis method of the Ngualla viscosity test is: An asphalt sample is emulsified by heating it in a water bath. The evenly stirred emulsified asphalt sample is poured into the viscometer container. The actual temperature of the asphalt sample is monitored in real time. When the asphalt sample temperature reaches the set temperature, the outflow port stopcock is opened and a stopwatch is started. When the asphalt sample has flowed out and reaches the specified scale line, the stopwatch is stopped. The outflow time of the asphalt sample is obtained. The Engla viscosity of the asphalt sample in each defective area of the asphalt pavement is calculated based on the Engla viscometer's calibration coefficient and the outflow time of the asphalt sample. The Engla viscosity of the asphalt samples of each diseased area is compared with the set standard qualified range. If the Engla viscosity of the asphalt sample of a diseased area is not within the set standard qualified range, it means that the asphalt sample of the diseased area is unqualified.

8. The asphalt testing instrument according to claim 1, characterized in that: The specific analysis method of the comprehensive evaluation module is: Obtain the judgment results of asphalt samples in various diseased areas of asphalt pavement; Determine whether any key quality indicator of the asphalt samples in each diseased area, including the softening point, test penetration value, and Engela viscosity, is unqualified. If any key quality indicator is unqualified, the asphalt is determined to be unqualified overall; If the softening point, test penetration value and Engela viscosity of the asphalt samples in each defective area are all qualified, the asphalt comprehensive evaluation coefficient of each defective area of the asphalt pavement is comprehensively evaluated.

9. The asphalt testing instrument according to claim 8, characterized in that: The specific analysis method of the quality grading feedback module is: The softening point, test penetration value, and Engela viscosity of asphalt samples in each defective area of the asphalt pavement were obtained respectively, and a comprehensive evaluation coefficient of asphalt in each defective area of the asphalt pavement was obtained through weighted calculation. Different coefficient intervals are set, each coefficient interval corresponds to a quality grade, and the quality grade corresponding to each diseased area of the asphalt pavement is determined according to the interval of the asphalt comprehensive evaluation coefficient of each diseased area of the asphalt pavement, and the graded feedback results are output.

10. An asphalt test detection and control system, characterized in that: It comprises an asphalt testing instrument as described in any one of claims 1-9.

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