Method for detecting water content of road cavity disease based on radar detection

Abstract

The application relates to the technical field of quantum radar, in particular to a road cavity water disease detection method based on radar detection, which comprises the following steps: analyzing the extreme value of the ballistic reflection wave of each position of a to-be-detected road, obtaining significant extreme values, reflection wave sub-sequences and extreme value sequences; analyzing the difference characteristics between the significant extreme values and the length distribution of the reflection wave sub-sequences, combining the element distribution of the extreme value sequences and the shape characteristics of the fitting curve to obtain a radar distribution coefficient; analyzing the similarity degree between the overall distribution characteristics of the significant extreme values and the overall distribution of the environmental humidity data to obtain a radar correlation coefficient; simulating the radar signal of each position of the to-be-detected road, statistically analyzing the signal collected in the current round, combining the threshold segmentation result of the radar correlation coefficient of a preset number of samples to obtain a disease probability value, and obtaining the detection result of the corresponding position. The application aims to improve the detection accuracy of the road cavity water disease.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quantum radar, in particular to a method for detecting water-containing cavity diseases of a road surface based on radar detection. BACKGROUND

[0002] Road disease problems are one of the important factors affecting public traffic safety, especially internal diseases of the road, which are more difficult to detect, often have higher concealment, and can cause greater risk. With the introduction of radar detection technology into road detection, more sensitive and accurate detection of internal defects of the road is achieved.

[0003] When ground penetrating radar performs nondestructive testing of internal defects of a road surface, the attenuation and reflection of electromagnetic waves in the road surface are used to determine the internal conditions of the road surface. Due to the difficulty in achieving perfect balance between detection resolution and detection depth for electromagnetic waves of different frequencies, the quality of one aspect is often sacrificed to improve the other aspect; and the existing technology often only analyzes the radar signals obtained during detection, which makes it difficult to accurately detect small diseases deep in the road. SUMMARY

[0004] In view of the above, it is necessary to provide a method for detecting water-containing cavity diseases of a road surface based on radar detection to solve the above problems.

[0005] One embodiment of the present application provides a method for detecting water-containing cavity diseases of a road surface based on radar detection, which comprises:

[0006] obtaining environmental humidity data of a road surface to be detected and monopulse reflected waves received by each position in the current round;

[0007] screening extreme values in the current round of each position to obtain significant extreme values; all extreme values of reflected waves between adjacent two significant extreme values are combined to form a reflected wave sub-column; the number of significant extreme values of each position in the current round is combined to form an extreme value sequence of each position in the current round, and the extreme value sequence is fitted to obtain a fitting curve; the difference characteristics between significant extreme values in the current round of each position, the length distribution of the reflected wave sub-column, the element distribution of the extreme value sequence obtained in the current round of each position, and the shape characteristics of the fitting curve are analyzed to obtain a radar distribution coefficient of the current round of each position;

[0008] the similarity between the overall distribution characteristics of the significant extreme values in the current round of each position and the overall distribution of the environmental humidity data is analyzed to obtain a humidity lag amount of each position, and the radar correlation coefficient of the current round of each position is obtained in combination with the radar distribution coefficient;

[0009] The radar signal of each position of the to-be-detected road surface is simulated, and statistical analysis is performed on the signal collected in the current round, combined with the threshold segmentation result of the radar correlation coefficient of a preset number of samples, to obtain a disease probability value of each position of the to-be-detected road surface, and to determine whether each position exists a cavity water disease.

[0010] The significant extreme value is obtained, and specifically:

[0011] For each position and each round of single-channel reflected wave, the absolute values of all extreme values are combined to form a reflected wave sequence, and the upper quartile of the reflected wave sequence is taken, and the elements greater than the upper quartile are recorded as significant extreme values.

[0012] The radar distribution coefficient of each position in the current round is obtained, and specifically:

[0013] The difference distribution between adjacent significant extreme values in all rounds of each position is analyzed to obtain the average change value of the reflected wave attenuation of each position in the current round;

[0014] Based on the length distribution of the reflected wave sub-column obtained in each position in all rounds, the minimum length value of the current round is obtained;

[0015] The element distribution of the extreme value sequence obtained in each position in the current round and the shape feature of the fitting curve are analyzed to obtain the extreme value number change trend of each position in the current round;

[0016] The negative correlation mapping result of the minimum length value of the current round is fused with the average change value of the reflected wave attenuation, and then added with the extreme value number change trend value of each position in the current round to obtain the radar distribution coefficient of each position in the current round.

[0017] The average change value of the reflected wave attenuation of each position in the current round is obtained, and specifically:

[0018] The dispersion degree of the difference between all adjacent significant extreme values of each position in each round is obtained, and the average value of the difference between the dispersion degrees of all adjacent rounds before the current round of each position is recorded as the average change value of the reflected wave attenuation of the current round.

[0019] The minimum length value of the current round is obtained, and specifically:

[0020] The minimum value of the length of the reflected wave sub-column obtained in each position in each round is obtained, and the minimum values obtained in all rounds before the current round of each position are averaged to obtain the minimum length value of the current round.

[0021] The extreme value number change trend of each position in the current round is obtained, and specifically:

[0022] Obtaining the maximum value of the extreme value sequence of each position, obtaining the proportion of the number of data points corresponding to the positive derivative value on the left curve of the maximum value, denoted as the first proportion;

[0023] Obtaining the proportion of the number of data points corresponding to the negative derivative value on the right curve of the maximum value, denoted as the second proportion;

[0024] Taking the average value of the first proportion and the second proportion as the extreme value number change trend of the current round of each position.

[0025] Wherein, the humidity hysteresis amount of each position is obtained, specifically:

[0026] The environmental humidity data of each position in all rounds is combined to form a humidity sequence;

[0027] The average value of the difference value of all adjacent significant extreme values in each round is calculated, and the obtained average value forms a decay amount sequence;

[0028] The cross-correlation analysis method is used on the humidity sequence and the decay amount sequence to obtain the cross-correlation coefficient corresponding to each hysteresis amount; the hysteresis amount corresponding to the maximum cross-correlation coefficient is taken as the humidity hysteresis amount of each position.

[0029] Wherein, the specific process of obtaining the radar correlation coefficient of the current round of each position is:

[0030] For the current round of each position, the discrete degree of all elements in the decay amount sequence is obtained, and the negative correlation mapping result of the humidity hysteresis amount is positively fused to obtain an attenuation fluctuation index;

[0031] The product of the radar correlation coefficient of the current round and the attenuation fluctuation index is taken as the radar correlation coefficient of each round.

[0032] Wherein, the specific formula for obtaining the disease probability value of each position of the road surface to be detected is: P is the disease probability value of the current round of each position, B is the radar correlation coefficient of the current round of each position, λ is the radar correlation coefficient segmentation threshold of the preset number of samples, P0 is the statistical analysis probability value of each position, and min{} represents the minimum value function.

[0033] Wherein, the specific process of determining whether there is a cavity water-containing disease in each position is:

[0034] When the disease probability value of each position in the current round is greater than the preset threshold, it is determined that the corresponding position has a cavity water-containing disease; otherwise, there is no cavity water-containing disease.

[0035] The present application has at least the following beneficial effects:

[0036] The application firstly analyzes the change characteristics of the radar signal, calculates the radar distribution coefficient combined with the development characteristics of the water-containing disease of the road cavity, and the coefficient combined with the time sequence characteristics of the radar signal and the disease development trend characteristics helps to enhance the accuracy of the detection of the water-containing disease of the road cavity; then the correlation characteristics between the radar signal change and the environmental change are combined to calculate the radar correlation coefficient, and the influence of the environmental humidity on the water-containing disease of the road cavity is combined to enhance the sensitivity and accuracy of the detection of the water-containing disease of the road cavity. In this way, the time sequence characteristics of the radar signal and the development characteristics of the water-containing disease of the road cavity are considered, and the influence of the environment is combined, so that even if the disease is small, sensitive and accurate detection can be realized according to the development situation. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flowchart of a method for detecting a water-containing disease of a road cavity based on radar detection provided by the application is provided.

[0038] Figure 2 A flowchart for obtaining a radar correlation coefficient provided by the application is provided. DETAILED DESCRIPTION

[0039] In the description of the embodiments of the application, the words "exemplary", "or", "for example" are used to mean as an example, instance, or illustration. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the application should not be interpreted as being more preferred or having greater advantages than other embodiments or design solutions. Rather, the use of "exemplary", "or", "for example" is intended to present relevant concepts in a concrete manner.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs. The terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit the application.

[0041] In addition, it should be noted that the terms "first", "second" in the application and its drawings are used to distinguish similar objects, and are not used to describe a specific order or sequence. The method disclosed in the embodiments of the application or the method shown in the flowchart includes one or more steps for implementing the method, and the execution order of the steps can be interchanged with each other without departing from the scope of the application, and some steps can also be deleted.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs.

[0043] The application provides a road hollow water disease detection method based on radar detection, applied to the technical field of quantum radar, and refers to the accompanying drawings Figure 1 The method comprises the following steps:

[0044] S1: acquiring environmental humidity data of a road to be detected and single-channel reflected waves received by each position in a current round.

[0045] The application considers that the development and change process of the disease has time sequence characteristics and has a strong connection with various physical parameters, and further introduces the influence of these factors in the analysis of the radar signal, thereby further improving the detection sensitivity and accuracy of the road hollow water disease.

[0046] Firstly, a detection vehicle equipped with a ground penetrating radar detection device is used to non-destructively detect a road section to be detected, a Ricker wavelet with a center frequency of 1500MHz is used as an excitation source, the transmitting-receiving distance is 2cm, the time window is 12ns, electromagnetic waves are emitted to the road direction every 0.5m, and single-channel reflected waves are received along the road section.

[0047] S2: screening the extreme values in the ballistic reflected waves of each position in the current round to obtain significant extreme values; all extreme values of the reflected waves between two adjacent significant extreme values are combined to form a reflected wave sub-column; the number of significant extreme values of each position in the current round is combined to form an extreme value sequence of each position in the current round, and the extreme value sequence is fitted to obtain a fitting curve; the difference characteristics between the significant extreme values in the current round of each position, the length distribution of the reflected wave sub-column, the element distribution of the extreme value sequence obtained in the current round of each position and the shape characteristics of the fitting curve are combined to obtain a radar distribution coefficient of each position in the current round.

[0048] The ground penetrating radar emits electromagnetic waves and measures the change of reflection intensity and waveform when the electromagnetic waves meet road structure layers with different dielectric constants, so as to realize the detection of underground structures. For a normal road, the material distribution in the road is relatively uniform, even if it is not uniform, the difference in dielectric constant is relatively small, so that the reflected waves in the radar signal of the normal road are relatively few, and the relative electromagnetic wave attenuation is relatively consistent.

[0049] When the road cavity water disease occurs, the dielectric constant will change greatly due to the change of humidity, so that the reflection and signal attenuation at the disease site will change obviously. In the early stage of road cavity disease, there may be multiple small cavities inside the road, and the distribution of these cavities has high discreteness. At the same time, due to the small size of the cavity, the electromagnetic wave attenuation caused by the cavity is also small. As the cavity gradually develops, the small cavities will gradually merge into larger cavities. The appearance of larger cavities will cause less reflection of electromagnetic waves, but the electromagnetic wave attenuation in the cavity is larger. At the same time, due to the deposition of water and air in the larger cavity, there is a large difference in the dielectric constant of the two media, and the impurity content in the air and water is less than that in the solid road material. Therefore, there will be obvious reflection and attenuation difference in the suspected cavity corresponding wave band, but the overall fluctuation of the wave band except the interface of the two media is more stable.

[0050] In a single reflection wave, the absolute values of all peak values and valley values are arranged in time order of the horizontal coordinate to form a reflection wave sequence. The upper quartile of the reflection wave sequence is taken, and the wave peak or wave valley greater than the upper quartile is recorded as a significant extreme value. The upper quartile is a known technology and will not be described in detail. The peak values and valley values of the reflection wave between any two adjacent significant extreme values are arranged in time order to form a reflection wave subsequence between the two significant extreme values.

[0051] When analyzing the time sequence characteristics of the radar signal, the historical detection results of each position on the to-be-detected road section need to be used for analysis. The significant extreme values of each historical detection result of a detection position are obtained in the above manner, and the number of significant extreme values in the radar signal at each detection time is arranged in time order to form an extreme value sequence of the position. Then, the elements in the extreme value sequence are taken as the vertical coordinates, and the indices of the elements are taken as the horizontal coordinates for quadratic curve fitting. The fitting curve function is output. Quadratic curve fitting is a known technology and will not be described in detail.

[0052] Based on the above analysis, the radar distribution coefficient is calculated to measure the influence of the disease distribution inside the road surface on the radar signal. Specifically, the dispersion degree of the difference between all adjacent significant extreme values of each position in each round is obtained, the average value of the difference between the dispersion degrees of all adjacent rounds before the current round of each position is obtained, and the average value of the reflection wave attenuation change is recorded as the current round of the reflection wave attenuation change. The minimum value of the reflection wave sub-column length obtained in each round of each position is obtained, and the average of the minimum values obtained in all rounds before the current round of each position is obtained to obtain the minimum length value of the current round. The maximum value of the extreme value sequence of each position is obtained, the proportion of the number of data points with positive derivative values on the left curve of the maximum value is obtained, and the proportion is recorded as the first proportion. The proportion of the number of data points with negative derivative values on the right curve of the maximum value is obtained, and the proportion is recorded as the second proportion. The average value of the first proportion and the second proportion is taken as the extreme value number change trend of each position in the current round. The negative correlation mapping result of the minimum length value of the current round is positively fused with the average value of the reflection wave attenuation change, and then added with the extreme value number change trend value of each position in the current round to obtain the radar distribution coefficient of each position in the current round. In this embodiment, the dispersion degree of each variable is calculated by using the standard deviation; the calculation method of positive fusion of multiple variables is multiplication; and the negative correlation mapping result of the variable is calculated by using the reciprocal of the variable.

[0053] It can be understood that for a normal road surface, the attenuation between each reflection wave in the radar detection is relatively consistent, and the number of reflection waves corresponding to the significant extreme value does not have an obvious monotonicity trend, and because the impurity situation in the solid material is relatively complex, there are more reflection waves, i.e., the length of the reflection wave sub-column is relatively long, so that the corresponding radar distribution coefficient is relatively small. For a road section with a hollow water disease, because the disease will gradually become more serious over time, as the disease changes, the number of significant extreme values shows a trend of first increasing and then decreasing, and as the hollow becomes larger, the water-containing hollow will cause the attenuation of the electromagnetic wave to become more and more serious; but because the composition medium in the hollow is mostly water and air, the impurity content of the two is much smaller than that in the solid, so that the number of reflection waves in the corresponding wave band gradually decreases, so that the corresponding radar distribution coefficient is relatively large.

[0054] S3: Analyze the similarity degree between the overall distribution characteristics of the difference of the significant extreme values in all rounds of each position and the overall distribution of the environmental humidity data to obtain the humidity lag amount of each position, and combine the radar distribution coefficient to obtain the radar correlation coefficient of each position in the current round.

[0055] The radar distribution coefficient attempts to reveal the distribution and timing changes of the internal cavities of the road by analyzing the changing direction of the radar signal. However, due to the complex internal structure of the road and the difficulty in accurately predicting the changes, simply relying on the fluctuations of the radar signal is not enough to accurately detect the cavities and water-containing diseases of the road surface. The main reason for the change of the radar signal is the change of the dielectric constant in different media, among which the change of humidity can cause a significant change in the dielectric coefficient, and humidity also has a serious impact on cavities. And there is a high similarity between humidity and environmental factors, so it is also important to measure the relationship between radar signal and environmental changes to improve the accuracy and sensitivity of the detection of water-containing diseases of road surface cavities.

[0056] For normal road surface, due to the relatively complete structure, low porosity and slow water migration, the humidity change range is small, and generally the closer to the surface, the more severe the humidity change, resulting in the closer to the surface, the greater the change rate of electromagnetic wave attenuation; secondly, due to the slow water migration, the response speed to environmental humidity change is slow, and the humidity change is relatively lagging. For water-containing diseases of road surface cavities, due to the existence of cavities and water-containing areas in the position of road surface cavity diseases, the structure is loose, the porosity is high, and the water is easy to accumulate and migrate, so the humidity change range is large, and due to the existence of cavities, a closed or semi-closed space is provided for water accumulation, and as water gradually accumulates, the humidity of deeper positions may also be significantly affected; secondly, due to the loose structure of the position of road surface cavity diseases, the water is easily affected by the environment, and the response speed is fast.

[0057] The environmental humidity data collected at each position in all radar detection rounds is arranged in the order of rounds to form a humidity sequence; the mean value of the difference values of all adjacent significant extreme values in each round is calculated, and the obtained mean value is arranged in the order of rounds to form an attenuation amount sequence; then the humidity sequence and the attenuation amount sequence are taken as inputs, and the cross-correlation analysis method is used to output the cross-correlation coefficient corresponding to each lag amount, and the lag amount corresponding to the maximum cross-correlation coefficient is taken as the humidity lag amount of each position. The cross-correlation analysis method is a known technology and will not be described in detail.

[0058] Based on the above analysis, the radar correlation coefficient is calculated to measure the influence of environmental changes on the radar signal. Specifically: the dispersion degree of all elements in the attenuation amount sequence is obtained, and the negative correlation mapping result of the humidity lag amount of each position is forward fused to obtain an attenuation fluctuation index; and the product of the radar correlation coefficient of the current round and the attenuation fluctuation index is taken as the radar correlation coefficient of each round.

[0059] In this embodiment, the radar correlation coefficient of each position in the current round is denoted as B, and its formula form is: In the formula, B is the radar correlation coefficient, A is the radar distribution coefficient, σ is the variance of the attenuation amount sequence, and Z is the humidity lag amount. The attenuation fluctuation index is denoted as.

[0060] The flowchart for obtaining the radar correlation coefficient is shown in Figure 2

[0061] It can be understood that the radar distribution coefficient of the normal road surface is small, and the fluctuation of the dielectric constant of the road surface caused by humidity is relatively small due to the small fluctuation range of humidity, so that the fluctuation of the electromagnetic wave in different humidity environments is relatively small, and the response speed of the normal road surface to the environmental change is slow, so that the lag is relatively large; on the contrary, when the road surface has a cavity water disease, the more serious the disease, the larger the radar correlation coefficient.

[0062] S4: Simulate the radar signal of each position of the road surface to be detected, and statistically analyze the signal collected in the current round, combine the threshold segmentation result of the radar correlation coefficient of the preset number of samples, obtain the disease probability value of each position of the road surface to be detected, and judge whether each position has a cavity water disease.

[0063] Collecting the ground penetrating radar detection signals of the samples, the samples include normal road surfaces and road surface cavity water disease road surfaces with different severity and water content, and calculating the radar correlation coefficient of each position. It should be noted that the samples refer to each detection position on the road section to be detected, and in this embodiment, the value of is 200, which can be set by the implementer according to the actual situation. The radar correlation coefficients of the samples are taken as input, and the Otsu threshold segmentation method is used to output the segmentation threshold. The Otsu threshold segmentation method is a known technology and will not be described in detail.

[0064] When detecting the road surface cavity water disease of a position, firstly, the finite time domain difference method is used for simulation, and the simulation radar signal data is output; then the simulation radar signal data and the radar signal collected by the ground penetrating radar are statistically analyzed to obtain the probability that the position has a road surface cavity water disease. In this embodiment, the statistical analysis is obtained by using the Bayesian statistical method, and the Bayesian statistics is a known technology, which will not be described herein. The above-mentioned probability only considers the characteristics of the radar signal, and the accuracy of the characteristics of the micro-disease is still to be improved when the environment is greatly affected, so the radar correlation coefficient is combined for further optimization: In the formula, P is the disease probability value of each position in the current round, B is the radar correlation coefficient of each position in the current round, is the radar correlation coefficient segmentation threshold of the preset number of samples, P0 is the probability value of the statistical analysis of each position, and min{} represents the minimum value function. When the adjusted probability value is greater than the preset threshold value, it is determined that there is a cavity water disease, otherwise, there is no cavity water disease; in this embodiment, the preset threshold value is 0.7, which can be set by the implementer according to the actual situation. ​

[0065] The computer program product of the present application can be a computer program implemented on one or more computers. The program instructions can be stored on a computer-readable medium, such as a hard disk, optical disk, online data storage, or other storage device. The program instructions can be downloaded from a network, such as the Internet, or can be uploaded from a computer readable medium. The program instructions can be implemented in a variety of programming languages, including assembly language programmed specifically for the computer, a high-level programming language that is compiled or interpreted into machine language, or a combination of the two. The program instructions can be implemented in a variety of ways, such as a stand-alone program, a module, a component, or a subroutine, which can be called by other programs or threads.

[0066] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for detecting a water disease in a cavity of a road surface based on radar detection, characterized by, The method comprises the following steps: Obtaining environmental humidity data of a to-be-detected road surface and single-channel reflected waves received by each position in a current round; Filtering extreme values in the single-channel reflected waves of each position in the current round to obtain significant extreme values; all extreme values between adjacent two significant extreme values are combined to form a reflected wave sub-column; the number of significant extreme values of each position in all current rounds is combined to form an extreme value sequence of each position in the current round, and the extreme value sequence is fitted to obtain a fitting curve; the difference characteristics between the significant extreme values in all current rounds of each position and the length distribution of the reflected wave sub-column are analyzed, and the element distribution of the extreme value sequence and the shape characteristics of the fitting curve obtained in the current round of each position are combined to obtain a radar distribution coefficient of each position in the current round; The similarity degree between the overall distribution characteristics of the significant extreme values in all current rounds of each position and the overall distribution of the environmental humidity data is analyzed to obtain a humidity lag amount of each position, and the radar correlation coefficient of each position in the current round is obtained in combination with the radar distribution coefficient; The radar signal of each position of the to-be-detected road surface is simulated, and statistical analysis is performed on the signal collected in the current round, and the radar correlation coefficient of a preset number of samples is segmented according to a threshold value to obtain a disease probability value of each position of the to-be-detected road surface, and it is judged whether each position has a cavity water disease; The humidity lag amount of each position is obtained, specifically as follows: The environmental humidity data of each position in all current rounds is combined to form a humidity sequence; The mean value of the difference values of all adjacent significant extreme values in each round is calculated, and the obtained mean value is combined to form an attenuation amount sequence; The cross-correlation analysis method is used on the humidity sequence and the attenuation amount sequence to obtain a cross-correlation coefficient corresponding to each lag amount; the lag amount corresponding to the maximum cross-correlation coefficient is taken as the humidity lag amount of each position; The disease probability value of each position of the road to be detected is obtained, and the specific formula is: , is the disease probability value of each position in the current round, is the radar correlation coefficient of each position in the current round, is the radar correlation coefficient segmentation threshold of the preset number of samples, is the statistical analysis probability value of each position, and min{} represents the minimum value function.

2. The radar detection based method of detecting water-related diseases in road cavities according to claim 1, characterized in that, The significant extreme values are obtained, specifically as follows: For the single-channel reflected waves of each position in each round, the absolute values of all extreme values are combined to form a reflected wave sequence, the upper quartile of the reflected wave sequence is taken, and the elements greater than the upper quartile are recorded as significant extreme values.

3. The radar detection based method of detecting water-related diseases in road cavities according to claim 1, characterized in that, The radar distribution coefficient of each position in the current round is obtained, specifically as follows: The difference distribution between adjacent significant extreme values in all current rounds of each position is analyzed to obtain a reflected wave attenuation average change value of each position in the current round; Based on the length distribution of the reflected wave sub-column obtained in all current rounds of each position, a minimum length value of the current round is obtained; The element distribution of the extreme value sequence and the shape characteristics of the fitting curve obtained in the current round of each position are analyzed to obtain an extreme value number change trend of each position in the current round; The negative correlation mapping result of the minimum length value of the current round is fused with the reflected wave attenuation average change value, and then added to the extreme value number change trend value of each position in the current round to obtain the radar distribution coefficient of each position in the current round.

4. The radar detection based method of detecting water-related diseases in road cavities according to claim 3, characterized in that, The reflected wave attenuation average change value of each position in the current round is obtained, specifically as follows: The average value of the difference between the dispersion degrees of all adjacent significant extreme values of each position in each round before the current round is recorded as the average change value of the reflection wave attenuation of the current round.

5. The radar detection based method of detecting water-related diseases in road cavities according to claim 3, characterized in that, The minimum length value of the current round is obtained, specifically: The minimum value of the length of the reflection wave sub-column obtained in each round of each position is obtained, and the average of the minimum values obtained in all rounds before the current round of each position is obtained to obtain the minimum length value of the current round.

6. The radar detection based method of detecting water-related diseases in road cavities according to claim 3, characterized in that, The extreme value quantity change trend of the current round of each position is obtained, specifically: The maximum value of the extreme value sequence of each position is obtained, and the proportion of the number of data points with positive derivative values on the left curve of the maximum value is obtained, which is recorded as the first proportion; The proportion of the number of data points with negative derivative values on the right curve of the maximum value is obtained, which is recorded as the second proportion; The average value of the first proportion and the second proportion is taken as the extreme value quantity change trend of the current round of each position.

7. The radar detection based method of detecting water-related diseases in road cavities according to claim 1, characterized in that, The specific process of obtaining the radar correlation coefficient of the current round of each position is as follows: For the current round of each position, the dispersion degree of all elements in the attenuation amount sequence is obtained, and the positive correlation mapping result of the humidity lag amount of each position is obtained, and the attenuation fluctuation index is obtained by forward fusion. The product of the radar correlation coefficient of the current round and the attenuation fluctuation index is taken as the radar correlation coefficient of each round.

8. The radar detection based method of detecting water-related diseases in road cavities according to claim 1, characterized in that, The specific process of determining whether there is a cavity water disease in each position is as follows: When the disease probability value of the current round of each position is greater than a preset threshold, it is determined that the corresponding position has a cavity water disease; otherwise, there is no cavity water disease.

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