A construction device and method based on real-time monitoring of asphalt pavement

Abstract

The present application provides a construction device and method based on real-time monitoring of asphalt pavement, which collects visual inspection images after paving asphalt mixture in the target construction area; extracts all asphalt aggregate particles from the collected visual inspection images, determines the particle size characteristics of each asphalt aggregate particle, and obtains aggregate groups within different particle size ranges based on all particle size characteristics; obtains the static distribution of each aggregate group, determines the static boundary information of all aggregate groups in the visual inspection image, and then determines the paving uniformity of the target construction area based on the static boundary information and all static distributions; dynamically adjusts the pitch and spiral speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction based on the paving uniformity. The scheme of the present application can be used to monitor the paving uniformity of asphalt paving in real time and automatically adjust the paving parameters during the construction of the asphalt pavement, thereby improving the overall paving uniformity of the construction process.

Description

Technical Field

[0001] The present application relates to the technical field of visual intelligent control of asphalt pavement construction, and more specifically, to a construction device and method based on real-time monitoring of asphalt pavement. Background Art

[0002] Visual intelligent control technology for asphalt pavement construction refers to the technology that analyzes real-time images or videos of the construction site based on visual detection technology during the asphalt construction process, and feeds back the detected information to the asphalt construction control system to optimize the asphalt construction process or improve the construction quality. However, among the many factors that affect the quality of asphalt pavement construction, the paving uniformity of the asphalt mixture is one of the key factors. During the paving process of the asphalt mixture paver, uneven phenomena such as aggregate segregation will occur, resulting in uneven distribution of local aggregates on the pavement, affecting the density and durability of the pavement, thereby shortening the service life of the pavement.

[0003] The existing visual intelligent control solution for asphalt pavement construction is to install a real-time monitoring system in the asphalt concrete paver, which can monitor the material layer thickness and aggregate distribution during the asphalt paving process. However, the paving parameters that affect the uniformity of aggregate distribution can only be manually adjusted and re-paved after the asphalt aggregate is paved, resulting in delays in the construction progress of the overall asphalt pavement. Therefore, how to monitor the paving uniformity of asphalt paving in real time and automatically adjust the paving parameters during the construction of the asphalt pavement, thereby improving the overall paving uniformity of the construction process and accelerating the overall construction progress has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a construction device and method based on real-time monitoring of asphalt pavement, which can realize real-time monitoring of the paving uniformity of asphalt paving and automatic adjustment of paving parameters during the construction process of asphalt pavement, thereby improving the overall paving uniformity of the construction process and further accelerating the overall construction progress.

[0005] In a first aspect, the present application provides a construction visual feedback control method based on real-time monitoring of asphalt pavement, comprising:

[0006] After starting the asphalt pavement construction, collect visual inspection images of the target construction area on the asphalt pavement after paving the asphalt mixture;

[0007] Extract all asphalt aggregate particles from the collected visual inspection images, and then determine the particle size characteristics of each asphalt aggregate particle in the target construction area based on the visual inspection mechanism. According to all the particle size characteristics, all the asphalt aggregate particles are graded to obtain aggregate groups within different particle size ranges.

[0008] Obtaining the static distribution amount when the asphalt aggregate particles in each aggregate group are uniformly distributed, determining the static boundary information of all aggregate groups in the visual detection image, and then determining the distribution deviation coefficient of each aggregate group in the visual detection image based on the static boundary information and all the static distribution amounts, and determining the paving uniformity of the target construction area on the asphalt pavement based on all the distribution deviation coefficients;

[0009] The pitch and the spiral speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction are dynamically adjusted by the paving uniformity.

[0010] In some embodiments, extracting all asphalt aggregate particles from the collected visual inspection image specifically includes:

[0011] Perform contrast enhancement on the visual inspection image to obtain a contrast image of the target construction area on the asphalt pavement;

[0012] Determining a binary image showing asphalt aggregate particles through the comparison image;

[0013] Connected domain analysis is performed on the binary image to obtain all asphalt aggregate particles in the visual inspection image.

[0014] In some embodiments, determining the particle size characteristics of each asphalt aggregate particle in the target construction area based on the visual detection mechanism specifically includes:

[0015] For each asphalt aggregate particle, contour detection is performed in the binary image to obtain a minimum circumscribed rectangle surrounding the asphalt aggregate particle;

[0016] Determining the particle size characteristics of asphalt aggregate particles based on the minimum circumscribed rectangle;

[0017] Then the particle size characteristics of each asphalt aggregate particle in the target construction area on the asphalt pavement are determined.

[0018] In some embodiments, all asphalt aggregate particles are graded according to all particle size characteristics, and the aggregate groups within different particle size ranges are obtained, specifically including:

[0019] Determine multiple different levels of particle size ranges according to engineering requirements during asphalt pavement construction;

[0020] For each size range, determine the classification center of the size range;

[0021] Determine the particle size similarity between each asphalt aggregate particle and the classification center based on all particle size characteristics;

[0022] By classifying all the particle size similarities, each asphalt aggregate particle is graded to obtain aggregate groups within a particle size range, and then to obtain aggregate groups within different particle size ranges.

[0023] In some embodiments, determining the static boundary information of all aggregate groups in the visual inspection image specifically includes:

[0024] For each aggregate group, the particle center of each asphalt aggregate particle in the aggregate group is determined by all boundary coordinates of the asphalt aggregate particles;

[0025] Determine the boundary distances and between the aggregate group and the boundary lines in different directions in the visual inspection image based on all particle centers;

[0026] The static boundary information of the aggregate groups is determined by all boundary spacings and, thus, the static boundary information of all aggregate groups in the visual inspection image is obtained.

[0027] In some embodiments, determining the distribution deviation coefficient of each aggregate group in the visual inspection image from the static boundary information and all static distribution quantities specifically includes:

[0028] For each aggregate group, determining a static difference between each boundary spacing in the static boundary information of the aggregate group and a static distribution amount of the aggregate group;

[0029] Determine the static divergence coefficient of the aggregate group based on all static differences;

[0030] The distribution deviation coefficient of the aggregate group is determined according to the static separation coefficient and the static distribution amount of the aggregate group, and then the distribution deviation coefficient of each aggregate group in the visual detection image is obtained.

[0031] In some embodiments, determining the paving uniformity of the target construction area on the asphalt pavement according to all distribution deviation coefficients specifically includes:

[0032] Determine the confidence sequence of all aggregate groups in the asphalt mixture based on pre-collected asphalt aggregate information;

[0033] The paving uniformity of the target construction area on the asphalt pavement is determined based on the confidence sequence and all distribution deviation coefficients.

[0034] In a second aspect, the present application provides a construction device based on real-time monitoring of an asphalt pavement, including an asphalt concrete paver and a construction visual feedback control system, wherein the construction visual feedback control system is used to control the asphalt concrete paver to perform asphalt pavement construction, and the construction visual feedback control system includes:

[0035] A collection module is used to collect visual inspection images of asphalt mixture paved in a target construction area on the asphalt pavement after the asphalt pavement construction is started;

[0036] A processing module is used to extract all asphalt aggregate particles from the collected visual inspection images, and then determine the particle size characteristics of each asphalt aggregate particle in the target construction area based on the visual inspection mechanism, and classify all asphalt aggregate particles according to all the particle size characteristics to obtain aggregate groups within different particle size ranges;

[0037] The processing module is used to obtain the static distribution amount when the asphalt aggregate particles in each aggregate group are evenly distributed, determine the static boundary information of all aggregate groups in the visual detection image, and then determine the distribution deviation coefficient of each aggregate group in the visual detection image based on the static boundary information and all the static distribution amounts, and determine the paving uniformity of the target construction area on the asphalt pavement according to all the distribution deviation coefficients;

[0038] The execution module is used to dynamically adjust the pitch and spiral speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction according to the paving uniformity.

[0039] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned construction visual feedback control method based on real-time monitoring of asphalt pavement.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned construction visual feedback control method based on real-time monitoring of asphalt pavement is implemented.

[0041] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0042] In the construction device and method based on real-time monitoring of asphalt pavement provided in the present application, first, after the asphalt pavement construction is started, a visual inspection image of the target construction area on the asphalt pavement after paving the asphalt mixture is collected; all the asphalt aggregate particles are extracted from the collected visual inspection image, and then the particle size characteristics of each asphalt aggregate particle in the target construction area are determined based on the visual inspection mechanism, and all the asphalt aggregate particles are graded according to all the particle size characteristics to obtain aggregate groups within different particle size ranges; the static distribution amount of the asphalt aggregate particles in each aggregate group when they are uniformly distributed is obtained, and the static boundary information of all the aggregate groups in the visual inspection image is determined, and then the distribution deviation coefficient of each aggregate group in the visual inspection image is determined based on the static boundary information and all the static distribution amounts, and the paving uniformity of the target construction area on the asphalt pavement is determined based on all the distribution deviation coefficients; the pitch and spiral speed of the spiral distributor in the asphalt concrete paver during the asphalt pavement construction are dynamically adjusted according to the paving uniformity.

[0043] It can be seen that in the visual detection feedback system of the present application, the pitch and spiral speed of the spiral feeder during the asphalt pavement construction process are dynamically adjusted through the paving uniformity; first, the aggregate groups within different particle size ranges are determined to obtain multiple clusters composed of asphalt aggregate particles with similar particle sizes. The above steps classify the asphalt aggregate particles according to the particle size, which is helpful to calculate the distribution deviation coefficient of each cluster, thereby realizing the evaluation of the uniformity of the distribution of the mixed asphalt during the paving process, wherein the determination of the particle size characteristics can obtain geometric parameters for describing the size of the asphalt aggregate particles, thereby facilitating the reasonable classification of the asphalt aggregate particles according to the particle size characteristics of each asphalt aggregate particle, and obtaining aggregate groups within different particle size ranges; then, the paving uniformity is determined to obtain an indicator for measuring the degree of uniform distribution of the asphalt mixture laid on the road surface during the asphalt pavement construction process. The above steps help visual detection feedback by real-time monitoring of the paving uniformity. The system determines whether the asphalt mixture covers the road surface evenly according to the expected design, and automatically adjusts the paving parameters to improve the overall paving uniformity of the construction process. This can solve the problem in the prior art that the paving parameters that affect the uniformity of aggregate distribution can only be manually adjusted and re-paved after paving, resulting in delays in the overall construction progress. Among them, the determination of the distribution deviation coefficient can obtain an indicator for measuring the degree of deviation between the actual distribution and the expected distribution of the aggregate group in the visual inspection image, which provides a quantitative standard for the visual inspection feedback system, so that the visual inspection feedback system can evaluate the uniformity of the distribution of the mixed asphalt material during the paving process based on the distribution deviation coefficient, thereby realizing real-time monitoring of paving uniformity. In summary, based on the above scheme, during the construction of the asphalt pavement, the paving uniformity of the asphalt paving can be monitored in real time and the paving parameters can be automatically adjusted, thereby improving the overall paving uniformity of the construction process and accelerating the overall construction progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is an exemplary flow chart of a construction visual feedback control method based on real-time monitoring of asphalt pavement according to some embodiments of the present application;

[0045] Figure 2 is an exemplary flow chart of determining aggregate groups according to some embodiments of the present application;

[0046] Figure 3 is an exemplary flow chart for determining static boundary information according to some embodiments of the present application;

[0047] Figure 4 is a structural schematic diagram of a construction visual feedback control system according to some embodiments of the present application;

[0048] Figure 5It is a structural schematic diagram of a computer device for implementing a construction visual feedback control method based on real-time monitoring of asphalt pavement according to some embodiments of the present application. DETAILED DESCRIPTION

[0049] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0050] refer to Figure 1 , which is an exemplary flow chart of a construction visual feedback control method based on real-time monitoring of asphalt pavement according to some embodiments of the present application. The construction visual feedback control method 100 based on real-time monitoring of asphalt pavement mainly includes the following steps:

[0051] In step 101, after the asphalt pavement construction is started, a visual inspection image of the target construction area on the asphalt pavement after the asphalt mixture is paved is collected.

[0052] It should be noted that, in the present application, the visual inspection image refers to an image that records the state of the asphalt mixture after paving on the road surface. The visual inspection image can be used to analyze the distribution and paving conditions of asphalt aggregate particles on the road surface; the target construction area refers to the asphalt pavement area paved by the asphalt paver within a unit time (one minute by default).

[0053] In specific implementation, after starting the asphalt pavement construction, the visual inspection camera can be fixed above the asphalt paver, so that the visual inspection camera can monitor the target construction area in real time, and the visual inspection camera can be set to automatically collect images of the paved target construction area every one minute to obtain a visual inspection image of the target construction area after the loose asphalt mixture is paved. The visual inspection camera can be a high-resolution industrial camera, which is not specifically limited here.

[0054] In step 102, all asphalt aggregate particles are extracted from the collected visual inspection image, and then the particle size characteristics of each asphalt aggregate particle in the target construction area are determined based on the visual inspection mechanism. All asphalt aggregate particles are graded according to all the particle size characteristics to obtain aggregate groups within different particle size ranges.

[0055] In some embodiments, extracting all asphalt aggregate particles from the collected visual inspection image can be achieved by the following steps:

[0056] Perform contrast enhancement on the visual inspection image to obtain a contrast image of the target construction area on the asphalt pavement;

[0057] Determining a binary image showing asphalt aggregate particles through the comparison image;

[0058] Connected domain analysis is performed on the binary image to obtain all asphalt aggregate particles in the visual inspection image.

[0059] It should be noted that, in the present application, asphalt aggregate particles refer to solid granular materials in asphalt mixtures, which are usually composed of aggregates such as crushed stone, gravel or sand. These particles can be used as the skeleton material of the pavement when paving asphalt pavement, and play a role in enhancing the strength, stability and durability of the pavement; contrast image refers to a visual detection image that has been contrast enhanced to make the edges of asphalt aggregate particles clearer, which is helpful for subsequent particle extraction and analysis; binary image refers to a visual detection image that contains only two pixel values ​​0 and 255 (i.e., black and white), which is used to distinguish between asphalt aggregate particles and the background, wherein white pixels (pixel value is 255) represent asphalt aggregate particles, and black pixels (pixel value is 0) represent the background.

[0060] In specific implementation, first, the existing image grayscale method (for example, weighted average method) can be used to grayscale the visual inspection image to obtain a grayscale image of the target construction area on the asphalt pavement, and then the number of pixels of each grayscale level in the grayscale image is calculated. For each grayscale level in the grayscale image, the ratio of the sum of the number of pixels from zero grayscale to grayscale to the total number of pixels in the grayscale image is multiplied by the grayscale range of the grayscale image, and the obtained value is used as the new grayscale value of the grayscale level. The new grayscale value of each grayscale level in the grayscale image can be obtained in the above manner, and the pixel value of each pixel in the grayscale image is replaced with the new grayscale value of the corresponding grayscale level, and the obtained new image is used as is a contrast image of the target construction area on the asphalt pavement; then, the contrast image is binarized by an image binarization method in the prior art (for example, an adaptive threshold method) to obtain a binary image showing asphalt aggregate particles; finally, all pixels in the binary image are traversed, and all connected white pixel areas in the binary image are marked in an eight-connected manner by an existing search algorithm (for example, a depth-first algorithm), each white pixel area represents an asphalt aggregate particle, thereby obtaining all asphalt aggregate particles in the visual inspection image of the target construction area on the asphalt pavement, wherein the grayscale range refers to the difference between the maximum grayscale level in the grayscale image and 1, which is usually 255.

[0061] In some embodiments, determining the particle size characteristics of each asphalt aggregate particle in the target construction area based on the visual detection mechanism can be achieved by the following steps:

[0062] For each asphalt aggregate particle, contour detection is performed in the binary image to obtain a minimum circumscribed rectangle surrounding the asphalt aggregate particle;

[0063] Determining the particle size characteristics of asphalt aggregate particles based on the minimum circumscribed rectangle;

[0064] Then the particle size characteristics of each asphalt aggregate particle in the target construction area on the asphalt pavement are determined.

[0065] It should be noted that, in the present application, the particle size feature refers to the geometric parameter used to describe the size of asphalt aggregate particles; in specific implementation, first, for each asphalt aggregate particle, the existing contour detection algorithm (for example: Canny edge detection algorithm) can be used in the binary image to identify the boundary of the asphalt aggregate particle, and the boundary coordinates of all boundary points on the boundary are output, and then the distance between every two boundary coordinates is calculated, and the line between the two boundary points with the farthest distance is used as the diagonal of the minimum circumscribed rectangle surrounding the asphalt aggregate particle. The diagonal line and the boundary coordinates of the two boundary points with the farthest distance can be used to calculate the minimum circumscribed rectangle. The minimum circumscribed rectangle that surrounds the asphalt aggregate particles. The minimum circumscribed rectangle refers to the minimum area rectangle that can completely surround the asphalt aggregate particles. Since some asphalt aggregate particles are not regular circles, the diameter of the asphalt aggregate particles cannot be directly used to replace the particle size. The minimum circumscribed rectangle is used to approximate the particle size of the asphalt aggregate particles. Then, the area of ​​the minimum circumscribed rectangle is calculated, and the square root of the ratio of four times the area to π is taken as the particle size characteristic of the asphalt aggregate particles. Finally, the particle size characteristics of each asphalt aggregate particle in the target construction area on the asphalt pavement can be obtained in the above manner.

[0066] In some embodiments, reference Figure 2 , which is an exemplary flow chart of determining aggregate groups according to some embodiments of the present application. In the present application, all asphalt aggregate particles are graded according to all particle size characteristics, and the aggregate groups within different particle size ranges can be obtained by the following steps:

[0067] In step 1021, multiple different levels of particle size ranges are determined according to engineering requirements during asphalt pavement construction;

[0068] In step 1022, for each particle size range, a classification center of the particle size range is determined;

[0069] In step 1023, the particle size similarity between each asphalt aggregate particle and the classification center is determined based on all particle size characteristics;

[0070] In step 1024, each asphalt aggregate particle is graded according to all particle size similarities to obtain aggregate groups within a particle size range, and then obtain aggregate groups within different particle size ranges.

[0071] It should be noted that, in the present application, an aggregate group is a cluster of asphalt aggregate particles of similar particle sizes; a particle size range is used to describe the intervals of different size ranges of asphalt aggregate particles, which can be used to classify asphalt aggregate particles of different sizes; a classification center refers to the center point of the particle size range when classifying asphalt aggregate particles, which can be used as a reference value to help classify the asphalt aggregate particles into the corresponding particle size range; particle size similarity refers to the degree of difference between the particle size characteristics of the asphalt aggregate particles and the classification center, which can be used to allocate the asphalt aggregate particles to the corresponding particle size range.

[0072] In specific implementation, first, according to the engineering specifications and requirements of asphalt pavement construction (for example: "Technical Specifications for Highway Asphalt Pavement Construction"), multiple particle size ranges of different gears can be set, such as 5.5-9.5mm, 9.5-13.5mm, 13.5-17.5mm, etc. In other embodiments, it can also be set to other intervals, which are not specifically limited here; secondly, for the particle size range of each gear, the median of the corresponding interval is used as the classification center of the particle size range; then, the difference between the particle size characteristics of each asphalt aggregate particle and the classification center can be used as the particle size similarity between the asphalt aggregate particle and the classification center; finally, all asphalt aggregate particles with a particle size similarity less than 2 are assigned to the particle size range of the corresponding gear to obtain an aggregate group within the particle size range. Aggregate groups within different particle size ranges can be obtained through the above steps.

[0073] In step 103, the static distribution amount of asphalt aggregate particles in each aggregate group when they are uniformly distributed is obtained, and the static boundary information of all aggregate groups in the visual detection image is determined. Then, the distribution deviation coefficient of each aggregate group in the visual detection image is determined based on the static boundary information and all the static distribution amounts, and the paving uniformity of the target construction area on the asphalt pavement is determined based on all the distribution deviation coefficients.

[0074] It should be noted that in this application, the static distribution amount refers to the sum of the ideal distances from all asphalt aggregate particles in each aggregate group to the pavement boundary when asphalt aggregate particles in different particle size ranges are theoretically evenly distributed. This static distribution amount can help evaluate the difference between the distribution of asphalt aggregate particles in the actual paving process and the theoretical ideal state.

[0075] In specific implementation, the average distance between each asphalt aggregate particle and the pavement boundary when the asphalt aggregate particles in the aggregate group with different particle size ranges are theoretically evenly distributed in the standard graded asphalt mixture can be obtained, and the sum of the average distances of all asphalt aggregate particles is taken as the static distribution amount when the asphalt aggregate particles in the aggregate group are evenly distributed, wherein the standard graded asphalt mixture refers to the AC-13 standard graded asphalt mixture. In other embodiments, it can also be other standard graded asphalt mixtures, which is not specifically limited here.

[0076] In some embodiments, reference Figure 3 , which is an exemplary flow chart of determining static boundary information according to some embodiments of the present application. In the present application, determining the static boundary information of all aggregate groups in the visual inspection image can be implemented by the following steps:

[0077] In step 1031, for each aggregate group, the particle center of each asphalt aggregate particle in the aggregate group is determined by all boundary coordinates of the asphalt aggregate particles;

[0078] In step 1032, the boundary distances between the aggregate group and the boundary lines in different directions in the visual inspection image are determined based on all the particle centers;

[0079] In step 1033, static boundary information of the aggregate group is determined by summing all boundary distances, thereby obtaining static boundary information of all aggregate groups in the visual inspection image.

[0080] It should be noted that, in the present application, static boundary information is a data set used to describe the position distribution of aggregate groups in visual detection images; the particle center refers to the geometric center of the asphalt aggregate particles, which can be used to further calculate the distance and position relationship between the asphalt aggregate particles and other elements; the boundary spacing refers to the sum of the distances from the geometric centers of all asphalt aggregate particles in the aggregate group to the boundary of the visual detection image, which can evaluate the spatial distribution of the aggregate group in the visual detection image and the edge of the visual detection image.

[0081] In the specific implementation, first, for each aggregate group, the particle center of each asphalt aggregate particle in the aggregate group is determined by all the boundary coordinates of the asphalt aggregate particles; then, the vertical distance from the particle center of each asphalt aggregate particle in the aggregate group to the four upper, lower, left and right boundary lines of the visual detection image is calculated, and the sum of the vertical distances from all asphalt aggregate particles to the upper boundary is used as the sum of the boundary spacings between the aggregate group and the upper boundary of the visual detection image, the sum of the vertical distances from all asphalt aggregate particles to the lower boundary is used as the sum of the boundary spacings between the aggregate group and the lower boundary of the visual detection image, the sum of the vertical distances from all asphalt aggregate particles to the left boundary is used as the sum of the boundary spacings between the aggregate group and the left boundary of the visual detection image, and the sum of the vertical distances from all asphalt aggregate particles to the right boundary is used as the sum of the boundary spacings between the aggregate group and the right boundary of the visual detection image, thereby obtaining the sum of the boundary spacings between the aggregate group and the boundary lines in different directions in the visual detection image; finally, the set of the sum of the boundary spacings between the aggregate group and the four boundary lines of the visual detection image is used as the static boundary information of the aggregate group. The static boundary information of all aggregate groups in the visual detection image can be obtained in the above manner.

[0082] In some preferred embodiments, determining the particle center of each asphalt aggregate particle in the aggregate group by all boundary coordinates of the asphalt aggregate particles can be achieved in the following manner, namely: for each asphalt aggregate particle in the aggregate group, first obtain all boundary coordinates of the asphalt aggregate particle in step 102, and then use the average position coordinate of all boundary coordinates as the particle center of the asphalt aggregate particle. The particle center of each asphalt aggregate particle in the aggregate group can be obtained in the above manner.

[0083] In some embodiments, determining the distribution deviation coefficient of each aggregate group in the visual inspection image from the static boundary information and all static distribution quantities can be achieved by using the following steps:

[0084] For each aggregate group, determining a static difference between each boundary spacing in the static boundary information of the aggregate group and a static distribution amount of the aggregate group;

[0085] Determine the static divergence coefficient of the aggregate group based on all static differences;

[0086] The distribution deviation coefficient of the aggregate group is determined according to the static separation coefficient and the static distribution amount of the aggregate group, and then the distribution deviation coefficient of each aggregate group in the visual detection image is obtained.

[0087] It should be noted that the distribution deviation coefficient is an indicator used to measure the degree of deviation between the actual distribution of aggregate groups in visual inspection images and the expected distribution. The distribution deviation coefficient can be used to evaluate the uniformity of the distribution of mixed asphalt during the paving process.

[0088] In the specific implementation, first, for each aggregate group, the static distribution amount of the aggregate group and all the boundary spacings in the static boundary information can be obtained, and the difference between each boundary spacing and the static distribution amount is used as the static difference between the corresponding boundary spacing and the static distribution amount of the aggregate group, thereby obtaining the static difference between each boundary spacing in the static boundary information and the static distribution amount of the aggregate group. The static difference is a parameter that measures the degree of deviation between the spatial distance of all asphalt aggregate particles in the aggregate group to the boundary of the visual detection image and the spatial distance to the boundary of the pavement when the asphalt aggregate particles are theoretically uniformly distributed. By taking the difference between the actual boundary spacing and the ideal static distribution amount as the static difference, the degree of deviation of the aggregate group under the current distribution can be reflected; then, The sum of the squares of all static differences is divided by 4 and then the square root is taken, and the resulting value is used as the static deviation coefficient of the aggregate group; finally, by dividing the static deviation coefficient by the static distribution amount, the relative ratio of the actual deviation to the ideal expectation can be calculated. This ratio reflects the degree to which the aggregate group actually deviates from the uniform distribution in the visual inspection image, and provides a quantifiable standard to measure whether the distribution of the aggregate group meets expectations. It can be used as the distribution deviation coefficient of the aggregate group. The distribution deviation coefficient of each aggregate group in the visual inspection image can be obtained in the above manner. The distribution deviation coefficient can be applied to aggregate groups with different particle size ranges, allowing particle groups of different sizes and shapes to be evaluated under the same standard, which is particularly applicable to multi-scale and multi-particle size pavement paving conditions.

[0089] It should be noted that, in the present application, the static deviation coefficient is a parameter that measures the degree of difference between the overall spatial distribution of all asphalt aggregate particles in the aggregate group on the visual inspection image and the spatial distribution of the asphalt aggregate particles when they are theoretically uniformly distributed; wherein, by squaring the static difference, the influence of positive and negative differences can be eliminated, so that all differences are positive, which helps to better measure the overall deviation of all static differences; because the visual inspection image has four boundary lines, by dividing the sum of squares by 4, a value related to the average difference can be obtained to ensure that the result is not affected by the size of the aggregate group; finally, the result of the sum of squares increases the value of the difference, and this value can be reduced by taking the square root, making it closer to the actual difference, thereby avoiding excessive results.

[0090] In some embodiments, determining the paving uniformity of the target construction area on the asphalt pavement according to all distribution deviation coefficients can be achieved by using the following steps:

[0091] Determine the confidence sequence of all aggregate groups in the asphalt mixture based on pre-collected asphalt aggregate information;

[0092] The paving uniformity of the target construction area on the asphalt pavement is determined based on the confidence sequence and all distribution deviation coefficients.

[0093] It should be noted that, in the present application, paving uniformity is an indicator to measure the uniformity of distribution of asphalt mixture laid on the road surface during the asphalt pavement construction process. The paving uniformity can be used to determine whether the asphalt mixture covers the road surface evenly according to the expected design; the confidence sequence is a sequence composed of the confidences of multiple aggregate groups. The confidence is a parameter to measure the degree of influence of asphalt aggregate particles in different aggregate groups on the uniformity of asphalt aggregate paving during the asphalt pavement construction process. The greater the confidence, the greater the influence of the asphalt aggregate particles in the aggregate group on the uniformity of asphalt aggregate paving during the asphalt pavement construction process, and the greater the credibility of the distribution deviation coefficient of the aggregate group in the process of calculating the overall paving uniformity.

[0094] In specific implementation, first, for each aggregate group, the weight of all asphalt aggregate particles within the particle size range corresponding to the aggregate group in the standard graded asphalt mixture can be obtained from the pre-collected asphalt aggregate information, and the ratio of this weight to the total weight of the standard graded asphalt mixture is used as the confidence of the aggregate group in the asphalt mixture. The confidence of each aggregate group in the asphalt mixture can be obtained in the above manner, and then all the confidences are arranged in order from small to large according to the particle size range corresponding to the aggregate group, to obtain a confidence sequence of all aggregate groups in the asphalt mixture; then, the sum of the accumulation of each confidence in the confidence sequence and the distribution deviation coefficient of the corresponding aggregate group is used as the paving uniformity of the target construction area on the asphalt pavement, wherein the standard graded asphalt mixture refers to the AC-13 standard graded asphalt mixture. In other embodiments, it can also be other standard graded asphalt mixtures, which are not specifically limited here.

[0095] In step 104, the pitch and the screw speed of the screw distributor in the asphalt concrete paver during the asphalt pavement construction are dynamically adjusted according to the paving uniformity.

[0096] In specific implementation, the pitch and spiral speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction are dynamically adjusted by the paving uniformity, which can be achieved in the following manner, namely: in the construction visual feedback control system, the pitch and spiral speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction are dynamically adjusted by the paving uniformity to improve the paving uniformity of the next target construction area.

[0097] It should be noted that in the present application, the asphalt concrete paver is a kind of construction equipment commonly used in the asphalt pavement construction process, which is mainly used to evenly distribute the asphalt mixture on the road surface for paving and compaction, and the spiral spreading machine is a component in the asphalt concrete paver, which transports the asphalt material from the feeding device to the construction area through the rotating spiral blades to ensure that the asphalt mixture is evenly distributed in the width direction. Among them, the pitch and spiral speed are the key parameters that affect the uniformity of the asphalt mixture distribution during the operation of the spiral spreading machine. The spiral speed is the rotation speed of the spiral blades. Too fast or too slow spiral speed will cause uneven distribution of the asphalt mixture. The pitch is the spacing between the spiral blades, which determines the amount of material distributed each time the spiral blades rotate. Too large or too small a pitch will also cause uneven distribution of the asphalt mixture.

[0098] In addition, in another aspect of the present application, in some embodiments, the present application provides a construction device based on real-time monitoring of asphalt pavement, the construction device based on real-time monitoring of asphalt pavement includes: an asphalt concrete paver and a construction visual feedback control system, the construction visual feedback control system is used to control the asphalt concrete paver to perform asphalt pavement construction, reference Figure 4 , which is a schematic diagram of the structure of a construction visual feedback control system according to some embodiments of the present application. The construction visual feedback control system 400 includes: a collection module 401, a processing module 402 and an execution module 403, which are described as follows:

[0099] The acquisition module 401 in this application is mainly used to collect visual inspection images of asphalt mixture paved in the target construction area on the asphalt pavement after the asphalt pavement construction is started;

[0100] Processing module 402, in this application, is mainly used to extract all asphalt aggregate particles from the collected visual inspection image, and then determine the particle size characteristics of each asphalt aggregate particle in the target construction area based on the visual inspection mechanism, and classify all asphalt aggregate particles according to all the particle size characteristics to obtain aggregate groups within different particle size ranges;

[0101] It should be noted that the processing module 402 in the present application is also used to obtain the static distribution amount when the asphalt aggregate particles in each aggregate group are evenly distributed, determine the static boundary information of all aggregate groups in the visual detection image, and then determine the distribution deviation coefficient of each aggregate group in the visual detection image based on the static boundary information and all the static distribution amounts, and determine the paving uniformity of the target construction area on the asphalt pavement based on all the distribution deviation coefficients;

[0102] Execution module 403, in the present application, execution module 403 is mainly used to dynamically adjust the pitch and spiral speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction according to the paving uniformity.

[0103] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned construction visual feedback control method based on real-time monitoring of asphalt pavement.

[0104] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a construction visual feedback control method based on real-time monitoring of asphalt pavement according to some embodiments of the present application. The construction visual feedback control method based on real-time monitoring of asphalt pavement in the above embodiment can be achieved by Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0105] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0106] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0107] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0108] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The construction visual feedback control method based on real-time monitoring of asphalt pavement in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0109] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0110] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0111] The above-mentioned computer device can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0112] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned construction visual feedback control method based on real-time monitoring of asphalt pavement.

[0113] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0114] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A construction visual feedback control method based on real-time monitoring of asphalt pavement, used to control an asphalt concrete paver to perform asphalt pavement construction, characterized in that: The steps include: After starting the asphalt pavement construction, collect visual inspection images of the target construction area on the asphalt pavement after paving the asphalt mixture; Extract all asphalt aggregate particles from the collected visual inspection images, and then determine the particle size characteristics of each asphalt aggregate particle in the target construction area based on the visual inspection mechanism. According to all the particle size characteristics, all the asphalt aggregate particles are graded to obtain aggregate groups within different particle size ranges. Obtaining the static distribution amount when the asphalt aggregate particles in each aggregate group are uniformly distributed, determining the static boundary information of all aggregate groups in the visual detection image, and then determining the distribution deviation coefficient of each aggregate group in the visual detection image based on the static boundary information and all the static distribution amounts, and determining the paving uniformity of the target construction area on the asphalt pavement based on all the distribution deviation coefficients; Dynamically adjust the pitch and speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction by using the paving uniformity; Wherein, determining the static boundary information of all aggregate groups in the visual inspection image specifically includes: For each aggregate group, the particle center of each asphalt aggregate particle in the aggregate group is determined by all boundary coordinates of the asphalt aggregate particles; Determine the boundary distances and between the aggregate group and the boundary lines in different directions in the visual inspection image based on all particle centers; The static boundary information of all aggregate groups in the visual inspection image is obtained by determining the static boundary information of the aggregate groups based on all boundary spacings; Wherein, determining the distribution deviation coefficient of each aggregate group in the visual inspection image based on the static boundary information and all static distribution quantities specifically includes: For each aggregate group, determining a static difference between each boundary spacing in the static boundary information of the aggregate group and a static distribution amount of the aggregate group; Determine the static divergence coefficient of the aggregate group based on all static differences; Determining the distribution deviation coefficient of the aggregate group according to the static separation coefficient and the static distribution amount of the aggregate group, and then obtaining the distribution deviation coefficient of each aggregate group in the visual detection image; Among them, the paving uniformity of the target construction area on the asphalt pavement is determined based on all distribution deviation coefficients, including: Determine the confidence sequence of all aggregate groups in the asphalt mixture based on pre-collected asphalt aggregate information; The paving uniformity of the target construction area on the asphalt pavement is determined based on the confidence sequence and all distribution deviation coefficients.

2. The method according to claim 1, characterized in that Extract all asphalt aggregate particles from the collected visual inspection images, including: Perform contrast enhancement on the visual inspection image to obtain a contrast image of the target construction area on the asphalt pavement; Determining a binary image showing asphalt aggregate particles through the comparison image; Connected domain analysis is performed on the binary image to obtain all asphalt aggregate particles in the visual inspection image.

3. The method according to claim 2, characterized in that The particle size characteristics of each asphalt aggregate particle in the target construction area are determined based on the visual detection mechanism, including: For each asphalt aggregate particle, contour detection is performed in the binary image to obtain a minimum circumscribed rectangle surrounding the asphalt aggregate particle; Determining the particle size characteristics of asphalt aggregate particles based on the minimum circumscribed rectangle; Then the particle size characteristics of each asphalt aggregate particle in the target construction area on the asphalt pavement are determined.

4. The method according to claim 1, characterized in that According to all the particle size characteristics, all the asphalt aggregate particles are graded to obtain aggregate groups within different particle size ranges, including: Determine multiple different levels of particle size ranges according to engineering requirements during asphalt pavement construction; For each size range, determine the classification center of the size range; Determine the particle size similarity between each asphalt aggregate particle and the classification center based on all particle size characteristics; By classifying all the particle size similarities, each asphalt aggregate particle is graded to obtain aggregate groups within a particle size range, and then to obtain aggregate groups within different particle size ranges.

5. A construction device based on real-time monitoring of asphalt pavement, which adopts the method according to any one of claims 1 to 4 to perform construction visual feedback control, and the construction device comprises: An asphalt concrete paver and a construction visual feedback control system, wherein the construction visual feedback control system is used to control the asphalt concrete paver to perform asphalt pavement construction, and is characterized in that the construction visual feedback control system includes: A collection module is used to collect visual inspection images of asphalt mixture paved in a target construction area on the asphalt pavement after the asphalt pavement construction is started; A processing module is used to extract all asphalt aggregate particles from the collected visual inspection images, and then determine the particle size characteristics of each asphalt aggregate particle in the target construction area based on the visual inspection mechanism, and classify all asphalt aggregate particles according to all the particle size characteristics to obtain aggregate groups within different particle size ranges; The processing module is used to obtain the static distribution amount when the asphalt aggregate particles in each aggregate group are evenly distributed, determine the static boundary information of all aggregate groups in the visual detection image, and then determine the distribution deviation coefficient of each aggregate group in the visual detection image based on the static boundary information and all the static distribution amounts, and determine the paving uniformity of the target construction area on the asphalt pavement according to all the distribution deviation coefficients; The execution module is used to dynamically adjust the pitch and spiral speed of the spiral distributor in the asphalt concrete paver during asphalt pavement construction according to the paving uniformity.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the construction visual feedback control method based on real-time monitoring of asphalt pavement described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the construction visual feedback control method based on real-time monitoring of asphalt pavement as described in any one of claims 1 to 4 are implemented.

Images