A roadbed construction data preprocessing method and system for highways

Abstract

The present invention relates to the field of data processing technology, and specifically to a method and system for preprocessing roadbed construction data for highways. Ultrasonic data and elevation data are integrated to achieve multi-source data fusion and enhance data dimension and accuracy. Multi-scale analysis is adopted to analyze ultrasonic and elevation data in each scale window, and the initial significance value is determined based on the difference and fluctuation of ultrasonic data to characterize the abnormality of the measuring point. Considering that ultrasonic data is affected by the compaction and flatness of the roadbed, elevation data is used to characterize the local flatness. As the window scale changes, the significance of the abnormality of the measuring point changes. The initial significance value and the relative change of the local flatness are combined to determine the depression degree value, thereby improving the accuracy of abnormality recognition. Finally, the initial significance value and the local flatness are corrected according to the depression degree value to obtain the final significance value, which more accurately characterizes the abnormality of the measuring point.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for preprocessing roadbed construction data for expressways. Background Art

[0002] In the process of highway construction and maintenance, the compaction of the roadbed is crucial, and the compaction can be mainly calculated and characterized by construction data, etc. However, due to the large number of measurement points on the roadbed surface of the highway, it is usually necessary to pre-process the construction data to improve the accuracy of the data processing results, so as to more accurately judge the abnormal positions or abnormal measurement points on the roadbed surface of the highway.

[0003] When processing construction data and screening abnormal measuring points, the existing technology usually focuses more on the ultrasonic data at the measuring points, because the ultrasonic data can reflect the physical properties inside the roadbed, such as density, elastic modulus, etc., so as to judge the compaction degree and screen abnormal measuring points. However, since there are many measuring points on the roadbed surface of the highway and the ultrasonic data is easily affected by the flatness of the road surface, only processing the ultrasonic data to judge the abnormal measuring points on the surface of the highway roadbed will result in low credibility of the data processing results of some measuring points during the data processing process, which will ultimately affect the accuracy of the judgment of abnormal measuring points. Summary of the invention

[0004] In order to solve the problem that there are many measuring points on the roadbed surface of the highway, and the ultrasonic data is easily affected by the flatness of the road surface, only processing the ultrasonic data to judge the abnormal measuring points on the roadbed surface of the highway will lead to low credibility of the data processing results of some measuring points during the data processing process, which ultimately affects the accuracy of the judgment of abnormal measuring points. The purpose of the present invention is to provide a roadbed construction data preprocessing method and system for highways, and the technical scheme adopted is as follows:

[0005] Obtain ultrasonic data and elevation data at each measuring point on the roadbed surface of the expressway;

[0006] In each scale window, the initial significance value of each measuring point is determined based on the difference and fluctuation of ultrasonic data between measuring points; in each scale window, the change between the elevation data of the measuring points is analyzed to quantify the local flatness of each measuring point;

[0007] Determine the concavity value of each measuring point based on the relative changes between the initial saliency value of the measuring point in windows of all scales and the local smoothness; calculate the final saliency value at each measuring point based on the concavity value of each measuring point, the initial saliency value of each measuring point in windows of all scales and the local smoothness;

[0008] The abnormal measuring points are determined based on the final significance values ​​at each measuring point on the roadbed surface of the expressway.

[0009] Furthermore, the method for obtaining the initial significance value includes:

[0010] With each measuring point as the center, multiple windows of different scales are constructed, and in each window of each scale, other measuring points except the central measuring point are used as reference points;

[0011] Select any measuring point as the measuring point to be measured, and analyze the discrete situation between the ultrasonic data of the measuring point to be measured and all corresponding reference points in a window of any scale corresponding to the measuring point to be measured, and quantify the fluctuation factor;

[0012] The mean value of the ultrasonic data of all reference points is taken as the mean characteristic value; the absolute value of the difference between the ultrasonic data of the test point to be measured and the mean characteristic value is taken as the deviation factor;

[0013] According to the deviation factor and the fluctuation factor, an initial significance value of the measured point under the window of the scale is obtained, and the initial significance value is positively correlated with the deviation factor, and negatively correlated with the fluctuation factor, and the value of the initial significance value is a positive number.

[0014] Furthermore, the method for obtaining the fluctuation factor includes:

[0015] The square of the difference between the ultrasonic data of the measured point and each corresponding reference point is calculated as a discrete value, and the average of the discrete values ​​between the measured point and all corresponding reference points is used as the fluctuation factor.

[0016] Furthermore, the method for obtaining the local flatness includes:

[0017] In a window of any scale corresponding to the measured point, the square of the difference between the elevation data of the measured point and each corresponding reference point is used as the height difference factor;

[0018] The value after negative correlation mapping between the measured point and the mean of the height difference factors of all corresponding reference points and normalization is taken as the local flatness of the measured point under the window of this scale.

[0019] Furthermore, the method for obtaining the depression degree value includes:

[0020] Arrange windows of all scales in ascending order according to their sizes to obtain a sorted sequence;

[0021] For every two adjacent scale windows in the sorting sequence, the ratio of the local smoothness of each measuring point in the window of the latter scale to the local smoothness of the window of the former scale is calculated as the smoothness change rate, and the ratio of the initial significance value of each measuring point in the window of the former scale to the initial significance value in the window of the latter scale is calculated as the significance change rate;

[0022] Determine the concavity factor of each measuring point according to all the flatness change rates and significance change rates corresponding to the sorting sequence;

[0023] For any measuring point, the average level of the concavity factors of all measuring points in the preset neighborhood of the measuring point is taken as the concavity degree value of the measuring point.

[0024] Furthermore, the method for obtaining the depression factor includes:

[0025] In the sorting sequence, the product of the flatness change rate and the significance change rate between every two adjacent scale windows is used as a change factor;

[0026] The average value of the change factors between all adjacent windows of two scales in the sorting sequence is used as the concave factor.

[0027] Furthermore, the method for obtaining the final significance value includes:

[0028] For any measuring point, the ratio of the initial significance value of the measuring point in each scale window to the local flatness is taken as the significance factor of the measuring point;

[0029] The average level of the significant factors of the measuring point in windows of all scales is quantified to obtain the significant eigenvalue, and the product of the concavity value of the measuring point and the significant eigenvalue is normalized to obtain the final significance value of the measuring point.

[0030] Furthermore, the determining of abnormal measuring points based on the final significance values ​​at each measuring point on the roadbed surface of the expressway includes:

[0031] Based on the saliency detection algorithm and the final saliency values ​​at each measuring point on the roadbed surface of the expressway, saliency detection is performed on all measuring points on the roadbed surface of the expressway, so as to obtain abnormal measuring points among all measuring points.

[0032] Furthermore, the preset neighborhood is set to 3×3.

[0033] A highway-oriented roadbed construction data preprocessing system comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps of a highway-oriented roadbed construction data preprocessing method are implemented.

[0034] The present invention has the following beneficial effects:

[0035] The present invention simultaneously obtains ultrasonic data and elevation data of the surface of the highway subgrade, which not only enriches the data dimension, but also improves the accuracy and comprehensiveness of data processing. In order to more accurately capture the abnormal information in the data, the present invention adopts a multi-scale analysis method to analyze the ultrasonic data and elevation data under each scale window. Since the ultrasonic data at the abnormal measuring point is compared with the ultrasonic data of the measuring points around it, there will be a more significant difference, so firstly under each scale window, based on the difference and fluctuation of the ultrasonic data between the measuring points, the initial significance value at each measuring point is determined, and the abnormal situation at each measuring point is preliminarily characterized. The change of ultrasonic data is related to the compaction degree of the subgrade. Different compaction states will affect the propagation velocity of the sound wave. At the same time, the flatness of the subgrade surface will affect the propagation path of the ultrasonic wave, thereby causing abnormal changes in the ultrasonic data, thereby covering up the actual compaction problem, and the flatness of the subgrade surface can be characterized by the change of elevation data, thereby obtaining the local flatness at each measuring point. Then, since the significance of the abnormal situation of the measuring point will change with the change of the window scale, the relative change between the initial significance value of the measuring point under the window of all scales and the local flatness is used to determine the depression value of each measuring point, which is helpful to more accurately identify the abnormal measuring point in the subsequent process. Finally, the initial significance value and local flatness of each measuring point at all scales are corrected according to the depression value, so as to obtain the final significance value at each measuring point. After the correction, the final significance value can more accurately characterize the abnormal situation at each measuring point, so data processing based on the final significance value at each measuring point on the roadbed surface of the highway can more accurately determine the abnormal measuring point. In summary, the data processing process in the process of the present invention not only takes into account the abnormal prominence of the data, but also integrates multi-source data, which can significantly improve the credibility of abnormal measuring point identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 paying creative work.

[0037] Figure 1 A method flow chart of a method for preprocessing highway roadbed construction data provided by an embodiment of the present invention;

[0038] Figure 2 A method flow chart of a method for obtaining an initial significance value provided by an embodiment of the present invention;

[0039] Figure 3 A method flow chart of a method for obtaining a concavity value provided by an embodiment of the present invention;

[0040] Figure 4 A system block diagram of a highway-oriented roadbed construction data preprocessing system provided by an embodiment of the present invention;

[0041] Figure 5 A schematic diagram of the system structure of a highway-oriented roadbed construction data preprocessing system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] The following is a detailed description of a specific scheme of a highway-oriented roadbed construction data preprocessing method and system provided by the present invention in conjunction with the accompanying drawings.

[0045] See also Figure 1 , which shows a method flow chart of a method for preprocessing roadbed construction data for highways provided by an embodiment of the present invention, the method comprising the following steps:

[0046] Step S1: Acquire ultrasonic data and elevation data at each measuring point on the roadbed surface of the expressway.

[0047] During the construction of the highway subgrade, real-time monitoring is required to facilitate timely dynamic adjustments. During the construction of the subgrade, more attention is often paid to the compaction degree, which directly affects the stability and durability of the subgrade. Since the propagation velocity of sound waves in different media is related to the physical properties of the medium (density, elastic modulus, etc.), the compaction degree can usually be reflected based on ultrasonic data. Therefore, in the embodiment of the present invention, an ultrasonic detector is used to obtain ultrasonic data at each measuring point on the subgrade surface of the highway, and the propagation velocity of sound waves at measuring points at different positions is recorded, thereby reflecting the compaction information at each position.

[0048] However, when there are obvious potholes or depressions on the roadbed surface, the propagation path of the ultrasonic wave will be affected, thereby affecting the strength and characteristics of its return signal, which will make the result of only processing the ultrasonic data to determine the abnormal measurement point less reliable; while the elevation data represents the height information of the roadbed surface, which can be used to correct the error of the ultrasonic data caused by the uneven road surface, so in the embodiment of the present invention, by combining these two types of construction data, a more accurate judgment of the compaction state of the roadbed is achieved. Therefore, a laser scanner is used to obtain the elevation data at each measurement point on the roadbed surface of the highway, and the height information at each measurement point on the roadbed surface is recorded to facilitate the subsequent analysis of the flatness of the roadbed surface.

[0049] It should be noted that the location distribution of the measuring points should be uniform and able to fully cover the roadbed surface of the expressway. The number and density can be adjusted according to the width, length and complexity of the shape of the roadbed, and there is no restriction here. After the ultrasonic data and elevation data are collected, they need to be processed synchronously to ensure that the ultrasonic data and elevation data of each measuring point can correspond one to one.

[0050] Step S2: Under each scale window, based on the difference and fluctuation of ultrasonic data between measuring points, determine the initial significance value at each measuring point; under each scale window, analyze the change between the elevation data of the measuring points and quantify the local flatness at each measuring point.

[0051] In order to more accurately capture abnormal information in the data, the embodiment of the present invention adopts a multi-scale analysis method to analyze the ultrasonic data and elevation data in each scale window. At the same time, since abnormal measuring points often indicate compaction problems or other potential defects of the roadbed, and the propagation speed of sound waves in media of different physical states will change, when the compaction of the roadbed is uneven or there are defects, the ultrasonic data will change. Therefore, in each scale window, based on the difference and fluctuation of the ultrasonic data between the measuring points, the initial significance value at each measuring point is determined. The initial significance value can preliminarily characterize the significant difference between each measuring point and other measuring points in ultrasonic characteristics (compactness). The quantification of local flatness is calculated based on the change of elevation data. The purpose of this step is to evaluate the morphological changes of the roadbed surface, especially uneven phenomena such as depressions or potholes. This information is crucial to understanding the abnormal changes in ultrasonic data, because uneven road surfaces may affect the propagation path of ultrasonic waves and the strength of the return signal, thereby causing errors in ultrasonic data.

[0052] When using ultrasonic data to detect the compaction at the measuring point, the propagation velocity of sound waves in the medium is related to the physical properties of the medium. Therefore, the compaction of the roadbed can be reflected by the propagation velocity of sound waves in the medium. Different compaction states will lead to different propagation velocities and reflection characteristics of ultrasonic waves in the material, and these changes may show different abnormal characteristics at different spatial scales. However, since the material in an area is consistent and its compaction degree does not change much within a certain range, windows of different scales are set, and then under each scale window, based on the difference and fluctuation of ultrasonic data between measuring points, the initial significance value at each measuring point is determined to preliminarily characterize the abnormal compaction at each measuring point.

[0053] Preferably, in one embodiment of the present invention, the method for obtaining the initial significance value includes:

[0054] See also Figure 2 , which shows a method flow chart of a method for obtaining an initial significance value in one embodiment of the present invention, the method comprising the following steps:

[0055] Step S201: construct windows of different scales corresponding to each measuring point, and determine the reference point corresponding to each measuring point.

[0056] Building windows of different scales can analyze the data differences between measurement points in different spatial ranges, which helps to capture the data change characteristics at different scales. Therefore, multiple windows of different scales are built with each measurement point as the center. At the same time, in order to facilitate the evaluation of the abnormal situation at each measurement point, in each scale window, other measurement points except the central measurement point are used as reference points. In the subsequent process, the abnormal situation of the central measurement point is quantified by comparing the central measurement point with the reference point.

[0057] It should be noted that, in this embodiment of the present invention, windows of different scales are respectively set to 3×3, 5×5, 7×7, 9×9, and 11×11. The specific sizes and quantities can be adjusted according to the implementation scenario and are not limited here.

[0058] Step S202: Select any measuring point as the measuring point to be measured, and analyze the discrete situation between the ultrasonic data of the measuring point to be measured and all corresponding reference points in a window of any scale corresponding to the measuring point to be measured, and quantify the fluctuation factor.

[0059] The discreteness between ultrasonic data can reflect the non-uniformity of the internal physical characteristics of the roadbed, so the square of the difference between the ultrasonic data of the measured point and each corresponding reference point is calculated as the discrete value. The larger the discrete value, the more obvious the difference between the ultrasonic data of the measured point and its corresponding reference point. Then the mean of the discrete values ​​between the measured point and all corresponding reference points is taken as the fluctuation factor, recorded as BD. At this time, the larger the fluctuation factor, the higher the degree of dispersion of the ultrasonic data of all reference points around the measured point.

[0060] Step S203: Determine a deviation factor according to the difference between the ultrasonic data of the to-be-measured point and all corresponding reference points.

[0061] The mean of the ultrasonic data of all reference points is taken as the mean eigenvalue. The mean eigenvalue represents the general level of the ultrasonic data of all reference points corresponding to the measured point, and can be used as a reference value. Therefore, the absolute value of the difference between the ultrasonic data of the measured point and the mean eigenvalue is taken as the deviation factor, denoted as PC. At this time, the larger the deviation factor is, the greater the difference between the average level of the measured point and its surrounding points is, and the more significant its prominence is, and the higher the possibility of abnormal compaction at its location is.

[0062] Step S204: The fluctuation factor and the deviation factor are combined to determine the initial significance value of the test point in the window of the scale.

[0063] According to the deviation factor and the fluctuation factor, the initial significance value of the test point in the window of this scale is obtained. The formula model of the initial significance value includes:

[0064]

[0065] in, Represents the initial significance value of the test point in a window of any scale; Indicates the deviation factor corresponding to the measured point in a window of any scale; Indicates the fluctuation factor corresponding to the measured point in a window of any scale; Indicates the preset first parameter; Indicates the preset second parameter.

[0066] In the formula model of the initial significance value, based on the above analysis, it can be seen that the larger the deviation factor, the greater the difference between the average level of the test point and its surrounding test points, the more significant its prominence is, and the higher the possibility of abnormal compaction at its location; the larger the fluctuation factor, the higher the discreteness of the ultrasonic data of all reference points around the test point, so the significant abnormality of the test point is more likely to be a random fluctuation of the data rather than a real abnormality, so the deviation factor is positively correlated with the initial significance value representing the abnormal situation of the test point, and the fluctuation factor is negatively correlated with the initial significance value representing the abnormal situation of the test point, so the fluctuation factor is used as the denominator and the deviation factor is used as the numerator to obtain the initial significance value of the test point in a window of any scale, and the larger the initial significance value, the higher the possibility of abnormal compaction at the test point.

[0067] It should be noted that the first parameter is preset The function is to prevent the denominator from being 0, and the value can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here; the second parameter is preset The role of It is 0, which is convenient for calculation in the subsequent process, so the preset value of the second parameter can be 1. The specific value can be adjusted according to the implementation scenario and is not limited here, but it is necessary to ensure that the final initial significance value is greater than 0.

[0068] The compaction of the roadbed of a highway is a key factor affecting its stability and durability. Although ultrasonic data can provide indirect information about compaction, the overall structure may have potholes due to insufficient compaction over a large area, which may affect the path propagation of the ultrasonic wave and cause the characteristics of the return signal to change. This phenomenon may mask the actual compaction problem. The elevation data directly reflects the morphological changes of the roadbed surface. Therefore, by analyzing the changes between the elevation data of the measuring points under windows of different scales to calculate the local flatness at each measuring point, the flatness of the roadbed surface can be intuitively evaluated, and then the uniformity of the compaction can be inferred, which helps to more accurately judge the abnormal and significant situation at each measuring point.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining local flatness includes:

[0070] The elevation data can reflect the height of each measuring point. Therefore, in a window of any scale corresponding to the measuring point to be measured, the square of the difference between the elevation data of the measuring point to be measured and each corresponding reference point is used as the height difference factor. At this time, the larger the height difference factor is, the more obvious the height difference between the measuring point to be measured and its corresponding reference point is, and the more uneven the local area where the measuring point to be measured is. Therefore, the height difference factor between the measuring point to be measured and all the corresponding reference points is integrated, and the mean of the height difference factor between the measuring point to be measured and all the corresponding reference points is negatively correlated and normalized to achieve logical relationship correction, and the value after negative correlation mapping and normalization is used as the local flatness of the measuring point to be measured in the window of this scale. At this time, the larger the local flatness is, the flatter the local position of the measuring point to be measured is. It should be noted that the negative correlation mapping and normalization method here adopts Function, where Indicated by natural constant is an exponential function with , where x represents the independent variable.

[0071] At this point, step S2 preliminarily analyzes the ultrasonic data and elevation data at each measuring point on the roadbed surface of the expressway to obtain the initial significance value at each measuring point, which is used to quantify the abnormal protrusion, and the local flatness at each measuring point, which is used to quantify the flatness; this provides an important basis for the subsequent identification and processing of abnormal measuring points.

[0072] Step S3: Determine the concavity value of each measuring point based on the initial saliency value of the measuring point in windows of all scales and the relative change between the local flatness; calculate the final saliency value at each measuring point based on the concavity value of each measuring point, the initial saliency value of each measuring point in windows of all scales and the local flatness.

[0073] In step S2, the initial significance value and local smoothness of each measuring point in windows of different scales can be obtained, and in step S3, the results in windows of all scales can be integrated to obtain a more accurate final significance value. Because the roadbed surface of the highway may be uneven in a local area, which may lead to errors in the acquisition of ultrasonic data, local smoothness can be included in the calculation process of significance analysis to correct the errors of ultrasonic data and ensure the accuracy of the results. Specifically, given that the initial significance value characterizes the abnormality of the ultrasonic data at each measuring point compared with the surrounding measuring points, as the scale of the window increases, the number of measuring points contained in the window increases, and the abnormality at the measuring point is gradually diluted, and the initial significance value will show a decreasing trend; similarly, for local flatness, as the scale of the window increases, if the local flatness becomes larger, it means that within a small range, there is a higher possibility of local depressions or potholes at the measuring point. Therefore, the depression degree value of each measuring point can be determined based on the initial significance value of the measuring point under windows of all scales and the relative change between the local flatness; then, based on the depression degree value, the initial significance value and the local flatness of the measuring point are corrected to calculate the final significance value at each measuring point. The final significance value can better reflect the actual condition of the roadbed surface of the highway, improve the detection accuracy, and is of great value for subsequent data processing to determine abnormal measuring points.

[0074] Preferably, in one embodiment of the present invention, the method for obtaining the concavity value includes:

[0075] See also Figure 3 , which shows a method flow chart of a method for obtaining a concavity value in one embodiment of the present invention, the method comprising the following steps:

[0076] Step S301: sort windows of all scales to obtain a sorted sequence.

[0077] Based on the above analysis, in order to quantify the concavity value, it is necessary to analyze the changes of local flatness and initial significance value with the window scale. Therefore, windows of all scales are arranged in ascending order according to their sizes to obtain a sorted sequence.

[0078] Step S302: In the sorting sequence, analyze the change of the local flatness and the initial significance value of the measuring point, and determine the significance change rate and the flatness change rate.

[0079] For every two adjacent scale windows in the sorting sequence, the ratio of the local flatness of each measuring point in the window of the latter scale to the local flatness of the window of the previous scale is calculated as the flatness change rate. At this time, the smaller the flatness change rate, the smaller the local flatness of the measuring point becomes when the scale increases, which means that more uneven areas are added to the window, and it can be regarded that the local flatness of the measuring point may be better at a small scale; on the contrary, the larger the flatness change rate, the greater the local flatness of the measuring point from a small scale to a large scale, that is, the local flatness of the measuring point in a large-scale window is higher than that in a small-scale window, which means that more flat areas are added to the window, which can be regarded as the worse the flatness of the measuring point in a small-scale window (in a small local range), that is, the more prominent its unevenness is, and the higher the possibility of depression or potholes.

[0080] Similarly, the ratio of the initial significance value of each measuring point in the window of the previous scale to the initial significance value in the window of the next scale is calculated as the significance change rate. At this time, the smaller the significance change rate, the greater the significance of the abnormality of the measuring point when the scale increases, so the abnormality of the measuring point in the small-scale window is more likely to be a normal data fluctuation. On the contrary, the larger the significance change rate, the more significant the abnormality of the measuring point in the small-scale window is relative to the abnormality in the large-scale window. It can be regarded as the higher the possibility of abnormality of the measuring point in the small-scale window (in a small local range), and the poorer the consistency with the ultrasonic data in the large-scale window, that is, the more likely there is interference from defects such as depressions and potholes in the local area.

[0081] Step S303: All flatness change rates and significance change rates corresponding to the sorted sequence are comprehensively considered to determine the concavity factor of each measuring point.

[0082] In step S302, the change relationship between the initial significance value and the local flatness is analyzed. Here, the change of the two can be combined.

[0083] In the sorting sequence, the product of the flatness change rate and the significance change rate between each two adjacent scale windows is used as the change factor. The larger the change factor, the more likely it is that there is a depression or pothole at the measuring point. Then the average of the change factors between all two adjacent scale windows in the sorting sequence is used as the depression factor. At this time, the larger the depression factor, the higher the possibility of the presence of a depression at the measuring point.

[0084] Step S304: In a preset neighborhood corresponding to each measuring point, the concavity factors corresponding to all measuring points are integrated to determine the concavity degree value of each measuring point.

[0085] In order to avoid accidental anomalies, for any measuring point, the average level of the concavity factors of all measuring points in the preset neighborhood of the measuring point, that is, the average value of the concavity factors of all measuring points in the preset neighborhood is taken as the concavity degree value of the measuring point. At this time, the concavity degree value combines the concavity factors of all measuring points in the neighborhood, so it can avoid misjudgment when only a single measuring point is used for calculation. The larger the concavity degree value, the greater the possibility of concavity at the measuring point. It should be noted that the size of the preset neighborhood here is 3×3.

[0086] After obtaining the concavity value at each measuring point, the concavity value can be used to correct the initial significance value and local flatness of each measuring point at all scales, so as to obtain the final significance value at each measuring point and more accurately measure the abnormality at each measuring point.

[0087] Preferably, in one embodiment of the present invention, the method for obtaining the final significance value includes:

[0088] For any measuring point, the ratio of the initial significance value of the measuring point in each scale window to the local flatness is taken as the significance factor of the measuring point. The larger the significance factor is, the worse the flatness at the measuring point is, and the worse the consistency with the ultrasonic data of the surrounding measuring points is, that is, the higher the possibility of abnormality is.

[0089] Then, the average level of the significant factors of the measuring point under all scale windows is quantified, and the mean value of the significant factors of the measuring point under all scale windows is used as the significant characteristic value. The larger the significant characteristic value, the higher the abnormality at the measuring point. Since the larger the concave degree value, the greater the possibility of concave at the measuring point, the product of the concave degree value of the measuring point and the significant characteristic value is finally normalized as the final significance value at the measuring point. At this time, the larger the final significance value, the more obvious the abnormality at the measuring point. It should be noted that normalization is a technical means well known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0090] Step S4: determining abnormal measuring points based on the final significance values ​​at each measuring point on the roadbed surface of the expressway.

[0091] Data processing of the final significance values ​​at each measuring point on the roadbed surface of the highway can accurately identify the locations where abnormalities exist on the roadbed surface, providing important decision-making basis for subsequent maintenance, reinforcement or reconstruction projects.

[0092] Preferably, in one embodiment of the present invention, determining abnormal measuring points based on the final significance values ​​at each measuring point on the roadbed surface of the expressway includes:

[0093] The significance detection (CA) algorithm is a commonly used anomaly detection algorithm. The principle of the algorithm is to find data points that are significantly different from the entire data sequence. In this embodiment of the present invention, based on the significance detection algorithm and the final significance values ​​at each measurement point on the roadbed surface of the highway, significance detection is performed on all measurement points on the roadbed surface of the highway, so that abnormal measurement points among all measurement points can be obtained. It should be noted that the specific process of the significance detection algorithm is a well-known technology and will not be described in detail here.

[0094] In other embodiments of the present invention, the final significance value of each measuring point may also be normalized, and then the normalized value is compared with the preset abnormal threshold. If it is greater than or equal to the preset abnormal threshold, the measuring point is considered to be an abnormal measuring point, and if it is less than the preset abnormal threshold, the measuring point is considered to be a normal measuring point. It should be noted that normalization is a technical means well known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here. The preset abnormal threshold is 0.6, and the specific value can be adjusted according to the implementation scenario, which is not limited here.

[0095] In summary, the present invention simultaneously obtains ultrasonic data and elevation data of the surface of the highway subgrade, and realizes the fusion of multi-source data. This fusion not only enriches the data dimension, but also improves the accuracy and comprehensiveness of data processing. In order to more accurately capture the abnormal information in the data, the present invention adopts a multi-scale analysis method to analyze the ultrasonic data and elevation data under each scale window. Since the ultrasonic data at the abnormal measuring point will have more significant fluctuations and differences compared with the ultrasonic data of the measuring points around it, firstly under each scale window, based on the difference and fluctuation of the ultrasonic data between the measuring points, the initial significance value at each measuring point is determined, and the abnormal situation at each measuring point is preliminarily characterized. The change of ultrasonic data is related to the compaction degree of the subgrade. Different compaction states will affect the propagation velocity of the sound wave. At the same time, the flatness of the subgrade surface will affect the propagation path of the ultrasonic wave, thereby causing abnormal changes in the ultrasonic data, thereby covering up the actual compaction problem, and the flatness of the subgrade surface can be characterized by the change of elevation data, thereby obtaining the local flatness at each measuring point. Then, since the significance of the abnormal situation of the measuring point will change with the change of the window scale, the relative change between the initial significance value of the measuring point under the window of all scales and the local flatness is used to determine the depression value of each measuring point, which is helpful to more accurately identify the abnormal measuring point in the subsequent process. Finally, the initial significance value and local flatness of each measuring point at all scales are corrected according to the depression value, so as to obtain the final significance value at each measuring point. After the correction, the final significance value can more accurately characterize the abnormal situation at each measuring point, so data processing based on the final significance value at each measuring point on the roadbed surface of the highway can more accurately determine the abnormal measuring point. In summary, the data processing process in the process of the present invention not only takes into account the abnormal prominence of the data, but also integrates multi-source data, which can significantly improve the credibility of abnormal measuring point identification.

[0096] The present invention also provides a highway-oriented roadbed construction data preprocessing system, see Figure 4 , which shows a system block diagram, including: a data acquisition module 401, used to implement step S1 in the above method; an initial abnormal situation analysis module 402, used to implement step S2 in the above method; an abnormal degree correction module 403, used to implement step S3 in the above method; an abnormal measurement point determination module 404, used to implement step S4 in the above method.

[0097] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the roadbed construction data preprocessing system for highways and the roadbed construction data preprocessing method for highways provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0098] See also Figure 5 , which shows a system structure diagram of a highway-oriented roadbed construction data preprocessing system provided by an embodiment of the present invention, including a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; wherein the memory 501 may include a high-speed random access memory, the bus 502 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 500 may be an integrated circuit chip with signal processing capability; the memory 501 stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps in a highway-oriented roadbed construction data preprocessing method are implemented.

[0099] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for preprocessing roadbed construction data for expressways, characterized in that: The method comprises: Obtain ultrasonic data and elevation data at each measuring point on the roadbed surface of the expressway; In each scale window, the initial significance value of each measuring point is determined based on the difference and fluctuation of ultrasonic data between measuring points; in each scale window, the change between the elevation data of the measuring points is analyzed to quantify the local flatness of each measuring point; Determine the concavity value of each measuring point based on the relative changes between the initial saliency value of the measuring point in windows of all scales and the local smoothness; calculate the final saliency value at each measuring point based on the concavity value of each measuring point, the initial saliency value of each measuring point in windows of all scales and the local smoothness; Determine abnormal measuring points based on the final significance values ​​at each measuring point on the roadbed surface of the expressway; The method for obtaining the depression degree value comprises: Arrange windows of all scales in ascending order according to their sizes to obtain a sorted sequence; For every two adjacent scale windows in the sorting sequence, the ratio of the local smoothness of each measuring point in the window of the latter scale to the local smoothness of the window of the former scale is calculated as the smoothness change rate, and the ratio of the initial significance value of each measuring point in the window of the former scale to the initial significance value in the window of the latter scale is calculated as the significance change rate; Determine the concavity factor of each measuring point according to all the flatness change rates and significance change rates corresponding to the sorting sequence; For any measuring point, the average level of the concavity factors of all measuring points in the preset neighborhood of the measuring point is taken as the concavity degree value of the measuring point; The method for obtaining the depression factor includes: In the sorting sequence, the product of the flatness change rate and the significance change rate between every two adjacent scale windows is used as a change factor; The average of the change factors between all adjacent windows of two scales in the sorting sequence is used as the concave factor; The method for obtaining the final significance value includes: For any measuring point, the ratio of the initial significance value of the measuring point in each scale window to the local flatness is taken as the significance factor of the measuring point; The average level of the significant factors of the measuring point in windows of all scales is quantified to obtain the significant eigenvalue, and the product of the concavity value of the measuring point and the significant eigenvalue is normalized to obtain the final significance value of the measuring point.

2. A method for preprocessing highway roadbed construction data according to claim 1, characterized in that: The method for obtaining the initial significance value includes: With each measuring point as the center, multiple windows of different scales are constructed, and in each window of each scale, other measuring points except the central measuring point are used as reference points; Select any measuring point as the measuring point to be measured, and analyze the discrete situation between the ultrasonic data of the measuring point to be measured and all corresponding reference points in a window of any scale corresponding to the measuring point to be measured, and quantify the fluctuation factor; The mean value of the ultrasonic data of all reference points is taken as the mean characteristic value; the absolute value of the difference between the ultrasonic data of the test point to be measured and the mean characteristic value is taken as the deviation factor; According to the deviation factor and the fluctuation factor, an initial significance value of the measured point under the window of the scale is obtained, and the initial significance value is positively correlated with the deviation factor, and negatively correlated with the fluctuation factor, and the value of the initial significance value is a positive number.

3. A method for preprocessing highway roadbed construction data according to claim 2, characterized in that: The method for obtaining the fluctuation factor includes: The square of the difference between the ultrasonic data of the measured point and each corresponding reference point is calculated as a discrete value, and the average of the discrete values ​​between the measured point and all corresponding reference points is used as the fluctuation factor.

4. A method for preprocessing highway roadbed construction data according to claim 2, characterized in that: The method for obtaining the local flatness includes: In a window of any scale corresponding to the measured point, the square of the difference between the elevation data of the measured point and each corresponding reference point is used as the height difference factor; The value after negative correlation mapping between the measured point and the mean of the height difference factors of all corresponding reference points and normalization is taken as the local flatness of the measured point in the window of this scale.

5. The method for preprocessing highway roadbed construction data according to claim 1, characterized in that: The method of determining abnormal measuring points based on the final significance values ​​at each measuring point on the roadbed surface of the expressway comprises: Based on the saliency detection algorithm and the final saliency values ​​at each measuring point on the roadbed surface of the expressway, saliency detection is performed on all measuring points on the roadbed surface of the expressway, so as to obtain abnormal measuring points among all measuring points.

6. A method for preprocessing highway roadbed construction data according to claim 1, characterized in that: The preset neighborhood is set to 3×3.

7. A roadbed construction data preprocessing system for expressways, characterized in that: The method comprises a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps of a method for preprocessing roadbed construction data for highways as described in any one of claims 1 to 6 are implemented.

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