CN117077449B - Road void area height evolution prediction method, electronic equipment and storage medium - Google Patents

Road void area height evolution prediction method, electronic equipment and storage medium Download PDF

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CN117077449B
CN117077449B CN202311336723.4A CN202311336723A CN117077449B CN 117077449 B CN117077449 B CN 117077449B CN 202311336723 A CN202311336723 A CN 202311336723A CN 117077449 B CN117077449 B CN 117077449B
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road
void area
disease
void
height
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CN117077449A (en
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孟安鑫
吴成龙
安茹
郭路
刘星
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Shenzhen Urban Transport Planning Center Co Ltd
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Abstract

A road void area height evolution prediction method, electronic equipment and storage media belong to the technical field of road detection engineering. The method aims to accurately predict the altitude evolution rule of the road void area. The invention adopts the ground penetrating radar to collect the road internal image; extracting a disease target area from the collected road internal image to obtain image data of a void area in the disease road; calculating the actual height of the void area of the disease road based on the obtained image data of the void area inside the disease road; and constructing a disease road void area height evolution model according to the calculated actual height of the disease road void area. The invention realizes the analysis of the height evolution rule of the void area in the road. The method can be used for assisting maintenance personnel in quantitatively judging the height of the void area and the change trend thereof, dividing the void of different grades and making corresponding maintenance decision schemes.

Description

Road void area height evolution prediction method, electronic equipment and storage medium
Technical Field
The invention belongs to the technical field of road detection engineering, and particularly relates to a road void area height evolution prediction method, electronic equipment and a storage medium.
Background
The road is the basis for safe driving of the vehicle. In recent years, due to the influence of factors such as leakage of underground pipe network of urban roads, rain wash, disturbance of peripheral construction and the like, loose materials can appear in the interior of roads, and gradually evolve into void. The occurrence of void can cause settlement, deformation and structural bearing capacity reduction of the road, further become a cavity, even cause events such as road collapse and the like, and become a serious threat for safe running of road vehicles. And the road collapse event has an increasing trend year by year, seriously threatens the trip safety of people, and becomes a key problem of social concern.
Industry recognizes that in the void area, a void with a clearance of 0.2 meter or less is called a void, and a void with a clearance of 0.2 meter or more is called a void. The threat of voids to the road is greater than void. Therefore, the threat degree of diseases in the road to the normal service of the road can be clarified by tracking the clearance height of the void in the road. The evolution law of the height of the void region is also an object that requires important attention compared to the height of the void region. When the height of the void area is smaller, the situation that the evolution speed of the height is higher exists, namely the change speed of the void height along with time is high, the influence of the void on the road bearing capacity is large, and the possibility of collapse at the corresponding position is higher.
The height of the void inside the road directly affects the stress complexity and the void severity of the road structure. Greater height of void results in more complex forces under vehicle loading and the void area will be subjected to greater loads and stresses. Meanwhile, the void with a larger height also means that the severity of the void is higher, and serious impact is generated when a vehicle passes through the void, so that potential threat is formed to the running safety of the vehicle.
The patent with the application number of 201811534532.8 and the invention of a quantitative identification and automatic identification method for the bridge butt strap void based on a ground penetrating radar directly adopts a radar imaging gray value to judge the void height of the bridge butt strap, and the larger the gray value is, the larger the void height is.
The patent with the application number of 201910128568.4 and the name of interlayer void identification method based on the ground penetrating radar image is obtained by sequentially carrying out median filtering and binarization processing on the ground penetrating radar image, then deleting redundant areas and extracting to obtain a void area image.
In the method, the identification of the road void area is basically limited, and the height information of the void area is difficult to quantify. Among the difficulties that exist are mainly: 1. in the ground penetrating radar image, the disease is judged based on hyperbolic shape and gray information, and the considered information is less, so that misjudgment and missed judgment of the empty hole are often caused; 2. imaging size information in the radar image is different from size information of actual void; 3. lack of attention to the high evolution rule of the void area leads to difficulty in accurately grasping the collapse latent disease in the road.
Disclosure of Invention
The invention aims to accurately predict a road void area height evolution law and provides a road void area height evolution prediction method, electronic equipment and a storage medium.
In order to achieve the above purpose, the present invention is realized by the following technical scheme:
a road void area height evolution prediction method comprises the following steps:
s1, acquiring an image of the interior of a road by adopting a ground penetrating radar;
s2, extracting a disease target area from the road internal image acquired in the step S1 to obtain image data of a void area in the disease road;
s3, calculating the actual height of the void area of the disease road based on the image data of the void area inside the disease road obtained in the step S2;
s4, constructing a disease road void area height evolution model according to the actual height of the disease road void area calculated in the step S3.
Further, the specific implementation method of the step S2 includes the following steps:
s2.1, performing binarization processing on the road internal image acquired in the step S1 by adopting a maximum inter-class variance method to obtain a binarized road internal image;
s2.2, counting the total number of pixels in all the connected areas of the road internal image after the binarization processing in the step S2.1, deleting the connected areas with the total number of pixels smaller than 500, reserving the connected areas with the total number of pixels larger than or equal to 500, and numbering the connected areas of the road internal image after the pixel deleting processing as N1, N2 … Ni … Nc;
s2.3, establishing a coordinate system for the road internal image processed by deleting pixels in the step S2.2, taking the top point of the left upper corner of the image as a coordinate origin, taking the horizontal right as the positive x-axis direction and the vertical downward as the positive y-axis direction, extracting coordinate points of pixels in all connected areas of the road internal image processed by deleting pixels, and setting the coordinate of the leftmost pixel point of the connected area Ni as the coordinate of the leftmost pixel point of the connected area NiThe coordinate of the rightmost pixel point is +.>The coordinate of the topmost pixel point is +.>The coordinate expression is defined as:
；
；
；
wherein,is the leftmost pixel point of the connected region NixAxis coordinates->Is the leftmost pixel point of the connected region NiyAn axis coordinate;
s2.4, screening out a region with hyperbolic characteristics by adopting a method for calculating the inclination angle, and respectively obtaining the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel pointAg 1 AndAg 2 the expression is:
；
；
then extracting the maximum value of the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel point, and marking the maximum value asThe expression is:
；
setting critical angleExtracting +.>Is a hyperbolic region +.>；
S2.5, extracting hyperbolic areas of all the connected areas, and renumbering to beWherein->Is the total number of hyperbolic areas;
s2.6, sequentially extracting single-channel waveform time domain data of all hyperbolic areas passing through the vertex coordinates based on the vertex coordinates of all hyperbolas obtained in the step S2.5, and sequentially recording as:，
wherein,，/>is->Single-channel waveform time domain data of single hyperbolic area passing vertex coordinates,/and method for generating the same>Is->No. I in single-channel waveform time domain data of single hyperbolic region over vertex coordinates>Amplitude data;
s2.7, converting the single-channel waveform time domain data of the hyperbolic region over-vertex coordinates obtained in the step S2.6 into frequency domain data by adopting a wavelet transformation method, wherein the calculation expression is as follows:
；
；
wherein,is->Single-channel waveform frequency domain data of peak coordinate crossing of hyperbola region, < ->For the scale of +>For translation amount->Is a basic wavelet;
then calculate the firstPhase of single-track waveform with peak coordinate crossing of hyperbolic region +.>The computational expression is:
；
wherein,is->Imaginary part of->Is->The real part of (2);
s2.8, setting the electromagnetic wave at the transmitting position as based on the electromagnetic wave theoryElectromagnetic wave at the reflection position is +.>Constructing a direction function->The expression is:
；
calculating a direction function whenWhen the electromagnetic wave is transmitted from the high dielectric constant to the low dielectric constant medium, the dielectric constant of the material at the disease position is smaller than that at the emission position, and the first judgment is madeiThe hyperbolic areas are void areas in the disease road;
s2.9, calculating the phase of a single-channel waveform of the over-vertex coordinates of all hyperbolic areas, and judging the road internal void condition of all the hyperbolic areas to obtain image data of the void area in the damaged road.
Further, the specific implementation method of the step S3 includes the following steps:
s3.1, drilling a disease road by adopting a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S2 on the site of the disease road to obtain the void area of the disease road;
s3.2, the endoscope is penetrated into the void area of the disease road, and the void area is determined by an endoscope displayThe top plate position and the bottom plate position of the disease road void area are measured, and the distance between the top plate position and the bottom plate position is the actual height of the disease road void area；
S3.3, adopting an image binarization method to the image data of the empty region in the damaged road verified in the step S3.1 to obtain the image data of the empty region in the damaged road after the image binarization treatment, and then extracting the coordinates of the top pixel point of the hyperbola in the image data of the empty region in the damaged road after the binarization treatmentExcessive->Making a straight line parallel to the y-axis and intersecting the hyperbola at +.>And->Then->，/>Obtaining the height of the void area of the damaged road>The calculation formula of (2) is as follows:
；
s3.4, selecting 10 disease road void areas, and repeating the steps S3.1-S3.3 to sequentially obtain the actual height of the disease road void areas，/>Calculating the height of the void area of the damaged road>，/>；
S3.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S3.4And->The actual height calculation expression of the disease road void area is obtained as follows:
；
wherein,、/>、/>respectively calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual height of the disease road void region;
s3.6, calculating the actual height of the disease road void area based on the actual height calculation expression of the disease road void area obtained in the step S3.5.
Further, the specific implementation method of the step S4 includes the following steps:
s4.1, collecting different momentsRadar images of the void area inside the disease road, and calculating the height of the void area inside the disease road according to the method of the step S3>Calculating the actual height of the disease road void region according to the actual height calculation expression of the disease road void region>；
S4.2 unreliable degree function Using Weibull distributionFitting->And (3) withConstructing a disease road void area height evolution model according to the relation of the road void area height evolution model;
weber distribution uncertainty functionThe calculated expression of (2) is:
；
wherein,、/>、/>sequentially an offset parameter, a scale parameter and a shape parameter;
obtaining a disease road void area height evolution model, wherein the calculation expression is as follows:
。
the electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the road void area height evolution prediction method when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of predicting the altitude evolution of a road void area.
The invention has the beneficial effects that:
according to the road void area height evolution prediction method, the disease identification method based on the road internal ground penetrating radar image is adopted, so that the accuracy of void area identification is improved; then, by establishing a relation model of the image size information and the actual void area size information, accurate calculation of the void area height is realized; based on the altitude information of the void area obtained by calculation at different times, a method for analyzing the evolution rule of the void area in the road is provided by combining with Weibull distribution, so that the analysis of the evolution rule of the void area in the road is realized. The method can be used for assisting maintenance personnel in quantitatively judging the height of the void area and the change trend thereof, dividing the void of different grades and making corresponding maintenance decision schemes.
According to the road void area height evolution prediction method, the degree of severity of the void area can be evaluated through calculation and evolution prediction analysis of the void area height, and future development conditions can be predicted.
According to the road void area height evolution prediction method, the road stress complexity can be indirectly estimated through calculation of the void area height, and the road operation safety degree is analyzed.
Drawings
Fig. 1 is a flowchart of a road void area height evolution prediction method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and detailed description. It should be understood that the embodiments described herein are for purposes of illustration only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a wide variety of different configurations, and the present invention can have other embodiments as well.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
For further understanding of the invention, the following detailed description is to be taken in conjunction with the accompanying drawings 1.
Detailed description of the preferred embodiments
A road void area height evolution prediction method comprises the following steps:
s1, acquiring an image of the interior of a road by adopting a ground penetrating radar;
s2, extracting a disease target area from the road internal image acquired in the step S1 to obtain image data of a void area in the disease road;
further, the specific implementation method of the step S2 includes the following steps:
s2.1, performing binarization processing on the road internal image acquired in the step S1 by adopting a maximum inter-class variance method to obtain a road internal image after the binarization processing;
s2.2, counting the total number of pixels in all the connected areas of the road internal image after the binarization processing in the step S2.1, deleting the connected areas with the total number of pixels smaller than 500, reserving the connected areas with the total number of pixels larger than or equal to 500, and numbering the connected areas of the road internal image after the pixel deleting processing as N1, N2 … Ni … Nc;
s2.3, deleting pixels in step S2.2The method comprises the steps of establishing a coordinate system of a road internal image after pixel deletion, taking the top point of the left upper corner of the image as a coordinate origin, taking the horizontal right direction as an x-axis positive direction, taking the vertical downward direction as a y-axis positive direction, extracting coordinate points of pixels in all connected areas of the road internal image after pixel deletion processing, and setting the coordinate of the leftmost pixel point of the connected area Ni as the coordinate of the leftmost pixel point of the connected area NiThe coordinate of the rightmost pixel point is +.>The coordinate of the topmost pixel point is +.>The coordinate expression is defined as:
；
；
；
wherein,is the leftmost pixel point of the connected region NixAxis coordinates->Is the leftmost pixel point of the connected region NiyAn axis coordinate;
s2.4, screening out a region with hyperbolic characteristics by adopting a method for calculating the inclination angle, and respectively obtaining the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel pointAg 1 AndAg 2 the expression is:
；
；
then extracting the maximum value of the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel point, and marking the maximum value asThe expression is:
；
setting critical angleExtracting +.>Is a hyperbolic region +.>；
S2.5, extracting hyperbolic areas of all the connected areas, and renumbering to beWherein->Is the total number of hyperbolic areas;
s2.6, sequentially extracting single-channel waveform time domain data of all hyperbolic areas passing through the vertex coordinates based on the vertex coordinates of all hyperbolas obtained in the step S2.5, and sequentially recording as:，
wherein,，/>is->Single-channel waveform time domain data of single hyperbolic area passing vertex coordinates,/and method for generating the same>Is->No. I in single-channel waveform time domain data of single hyperbolic region over vertex coordinates>Amplitude data;
s2.7, converting the single-channel waveform time domain data of the hyperbolic region over-vertex coordinates obtained in the step S2.6 into frequency domain data by adopting a wavelet transformation method, wherein the calculation expression is as follows:
；
；
wherein,is->Single-channel waveform frequency domain data of peak coordinate crossing of hyperbola region, < ->For the scale of +>For translation amount->Is a basic wavelet;
then calculate the firstPhase of single-track waveform with peak coordinate crossing of hyperbolic region +.>The computational expression is:
；
wherein,is->Imaginary part of->Is->The real part of (2);
s2.8, setting the electromagnetic wave at the transmitting position as based on the electromagnetic wave theoryElectromagnetic wave at the reflection position is +.>Constructing a direction function->The expression is:
；
calculating a direction function whenWhen the electromagnetic wave is transmitted from the high dielectric constant to the low dielectric constant medium, the dielectric constant of the material at the disease position is smaller than that at the emission position, and the +.>The hyperbolic areas are void areas in the disease road;
s2.9, calculating the phase of a single-channel waveform of the vertex passing coordinates of all hyperbolic areas, and judging the internal void condition of the road of all the hyperbolic areas to obtain image data of the void area inside the damaged road;
s3, calculating the actual height of the void area of the disease road based on the image data of the void area inside the disease road obtained in the step S2;
further, the specific implementation method of the step S3 includes the following steps:
s3.1, drilling a disease road by adopting a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S2 on the site of the disease road to obtain the void area of the disease road;
s3.2, the endoscope is deeply penetrated into the disease road void area, the top plate position and the bottom plate position of the disease road void area are determined through the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the actual height of the disease road void area；
S3.3, adopting an image binarization method to the image data of the empty region in the damaged road verified in the step S3.1 to obtain the image data of the empty region in the damaged road after the image binarization treatment, and then extracting the coordinates of the top pixel point of the hyperbola in the image data of the empty region in the damaged road after the binarization treatmentExcessive->Making a straight line parallel to the y-axis and intersecting the hyperbola at +.>And->Then->，/>Obtaining the height of the void area of the damaged road>The calculation formula of (2) is as follows:
；
s3.4, selecting 10 disease road void areas, and repeating the steps S3.1-S3.3 to sequentially obtain the actual height of the disease road void areasCalculating the height of the void area of the damaged road；
S3.5, fitting by adopting a quadratic function based on the 10 disease road void areas selected in the step S3.4And->The actual height calculation expression of the disease road void area is obtained as follows:
；
wherein,、/>、/>respectively calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual height of the disease road void region;
s3.6, calculating the actual height of the disease road void area based on the actual height calculation expression of the disease road void area obtained in the step S3.5;
s4, constructing a disease road void area height evolution model according to the actual height of the disease road void area calculated in the step S3;
further, the specific implementation method of the step S4 includes the following steps:
s4.1, collecting different momentsRadar images of the void area inside the disease road, and calculating the height of the void area inside the disease road according to the method of the step S3>Calculating the actual height of the disease road void region according to the actual height calculation expression of the disease road void region>；
S4.2 unreliable degree function Using Weibull distributionFitting->And (3) withConstructing a disease road void area height evolution model according to the relation of the road void area height evolution model;
weber distribution uncertainty functionThe calculated expression of (2) is:
；
wherein,、/>、/>sequentially an offset parameter, a scale parameter and a shape parameter;
obtaining a disease road void area height evolution model, wherein the calculation expression is as follows:
。
in the related research of road internal void detection, the method is basically limited to the identification of the road void region, and the height information of the void region is difficult to quantify. Among the difficulties that exist are mainly: 1. in the ground penetrating radar image, the disease is judged based on hyperbolic shape and gray information, and the considered information is less, so that misjudgment and missed judgment of the empty hole are often caused; 2. imaging size information in the radar image is different from size information of actual void; 3. lack of attention to the high evolution rule of the void area leads to difficulty in accurately grasping the collapse latent disease in the road. Based on the consideration, in the embodiment, the disease identification method based on the road internal ground penetrating radar image is adopted, so that the accuracy of identifying the void area is improved; then, by establishing a relation model of the image size information and the actual void area size information, accurate calculation of the void area height is realized; and based on the altitude information of the void area obtained by calculation at different times, combining with Weibull distribution, providing a method for analyzing the evolution rule of the void altitude.
Detailed description of the preferred embodiments
The electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the road void area height evolution prediction method when executing the computer program.
The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit. And the processor is used for realizing the steps of the road void area height evolution prediction method when executing the computer program stored in the memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Detailed description of the preferred embodiments
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of predicting the altitude evolution of a road void area.
The computer readable storage medium of the present invention may be any form of storage medium that is read by a processor of a computer device, including but not limited to a nonvolatile memory, a volatile memory, a ferroelectric memory, etc., on which a computer program is stored, and when the processor of the computer device reads and executes the computer program stored in the memory, the steps of a road void area height evolution prediction method described above may be implemented.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although the present application has been described hereinabove with reference to specific embodiments, various modifications thereof may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the features of the embodiments disclosed in this application may be combined with each other in any way as long as there is no structural conflict, and the exhaustive description of these combinations is not given in this specification merely for the sake of omitting the sake of brevity and saving resources. Therefore, it is intended that the present application not be limited to the particular embodiments disclosed, but that the present application include all embodiments falling within the scope of the appended claims.

Claims (5)

1. The method for predicting the altitude evolution of the road void area is characterized by comprising the following steps:
s1, acquiring an image of the interior of a road by adopting a ground penetrating radar;
s2, extracting a disease target area from the road internal image acquired in the step S1 to obtain image data of a void area in the disease road;
the specific implementation method of the step S2 comprises the following steps:
s2.1, performing binarization processing on the road internal image acquired in the step S1 by adopting a maximum inter-class variance method to obtain a binarized road internal image;
s2.2, counting the total number of pixels in all the connected areas of the road internal image after the binarization processing in the step S2.1, deleting the connected areas with the total number of pixels being less than 500, reserving the connected areas with the total number of pixels being more than or equal to 500, numbering the connected areas of the road internal image after the pixel deletion processing as N1, N2 … Ni … Nc, wherein c is the total number of the connected areas of the road internal image after the pixel deletion processing;
s2.3, establishing a coordinate system for the road internal image processed by deleting pixels in the step S2.2, taking the top point of the left upper corner of the image as a coordinate origin, taking the horizontal right as the positive x-axis direction and the vertical downward as the positive y-axis direction, extracting coordinate points of pixels in all connected areas of the road internal image processed by deleting pixels, and setting the coordinate of the leftmost pixel point of the connected area Ni as Co le The coordinate of the rightmost pixel point is Co ri The coordinate of the topmost pixel point is Co tp The coordinate expression is defined as:
Co le ＝(x le ,y le )
Co ri ＝(x ri ,y ri )
Co tp ＝(x tp ,y tp )
wherein x is le The x-axis coordinate, y of the leftmost pixel point of the connected region Ni le The y-axis coordinate of the leftmost pixel point of the communication area Ni; x is x ri The x-axis coordinate, y of the rightmost pixel point of the connected region Ni ri The y-axis coordinate of the rightmost pixel point of the communication area Ni; x is x tp The x-axis coordinate, y of the topmost pixel point of the connected region Ni tp The y-axis coordinate of the topmost pixel point of the communication region Ni;
s2.4, screening out a region with hyperbolic characteristics by adopting a method of calculating the inclination angle, and respectively obtaining the inclination angles Ag from the top pixel point to the leftmost pixel point and the rightmost pixel point 1 And Ag 2 The expression is:
then extracting the maximum value of the inclination angles from the top pixel point to the leftmost pixel point and the rightmost pixel point, and marking the maximum value as Ag max The expression is:
Ag max ＝max(Ag 1 ,Ag 2 )；
setting critical angle Ag r Extracting Ag from the connected region Ni max ≥Ag r Is a hyperbolic region N i '；
S2.5, extracting hyperbolic areas of all the connected areas, and renumbering to be N 1 ',N' 2 ,...,N i ',...,N' c' Wherein c' is the total number of hyperbolic areas;
s2.6, sequentially extracting single-channel waveform time domain data of all hyperbolic areas passing through the vertex coordinates based on the vertex coordinates of all hyperbolas obtained in the step S2.5, and sequentially recording as: d (D) tp1 ,D tp2 ,...,D tpi ,...,D tpc' ，
Wherein D is tpi ＝[d tp1 d tp2 ...d tpl ...d tpn ] T ，D tpi Single-channel waveform time domain data of vertex coordinate passing for ith hyperbola region, d tpl The ith amplitude data in the single-channel waveform time domain data of which the ith hyperbolic area passes the vertex coordinates, d tpn The nth amplitude data in the single-channel waveform time domain data of the hyperbolic region passing through the vertex coordinates is transposed;
s2.7, converting the single-channel waveform time domain data of the hyperbolic region over-vertex coordinates obtained in the step S2.6 into frequency domain data by adopting a wavelet transformation method, wherein the calculation expression is as follows:
wherein F is tpi Single-channel waveform frequency domain data of vertex coordinates of an ith hyperbolic region are obtained, alpha is a scale, tau is a translation amount, and phi is a basic wavelet;
then calculate the phase theta of the single-channel waveform of the ith hyperbola area passing the vertex coordinates tpi The computational expression is:
wherein Im [ F ] tpi ]Is F tpi Imaginary part, re [ F ] tpi ]Is F tpi The real part of (2);
s2.8, setting the electromagnetic wave at the transmitting position to be theta based on the electromagnetic wave theory tri The electromagnetic wave at the reflecting position is theta fli Constructing a direction function DR i The expression is:
DR i ＝θ tri ·θ fli
calculate the direction function, when DR i When the dielectric constant of the material at the disease position is more than 0, the dielectric constant of the material at the disease position is less than the emission position, and the ith hyperbolic area is judged to be a void area in the disease road;
s2.9, calculating the phase of a single-channel waveform of the vertex passing coordinates of all hyperbolic areas, and judging the internal void condition of the road of all the hyperbolic areas to obtain image data of the void area inside the damaged road;
s3, calculating the actual height of the void area of the disease road based on the image data of the void area inside the disease road obtained in the step S2;
s4, constructing a disease road void area height evolution model according to the actual height of the disease road void area calculated in the step S3.
2. The method for predicting the altitude evolution of a road void area according to claim 1, wherein the specific implementation method of step S3 comprises the following steps:
s3.1, drilling a disease road by adopting a drilling machine, and verifying the image data of the void area inside the disease road obtained in the step S2 on the site of the disease road to obtain the void area of the disease road;
s3.2, the endoscope is deeply penetrated into the disease road void area, the top plate position and the bottom plate position of the disease road void area are determined through the endoscope display, and the distance between the top plate position and the bottom plate position is measured to obtain the actual height H of the disease road void area ha ；
S3.3, adopting an image binarization method to the image data of the void area inside the damaged road verified in the step S3.1 to obtain the image data of the void area inside the damaged road after the image binarization treatment, and then extracting the void area inside the damaged road after the binarization treatmentCoordinates Co of topmost pixel point of hyperbola in empty region image data tp Co-passing tp Making a straight line parallel to the y-axis and intersecting the hyperbola with Co tp And Co tp2 Co, then tp ＝(x tp ,y tp )，Co tp2 ＝(x tp2 ,y tp2 ) Obtaining the height H of the void area of the damaged road hc The calculation formula of (2) is as follows:
H hc ＝|y tp -y tp2 |；
s3.4, selecting 10 disease road void areas, and repeating the steps S3.1-S3.3 to sequentially obtain the actual height H of the disease road void areas ha1 ，H ha2 …H ha10 Calculating the height H of the void area of the damaged road hc1 ，H hc2 …H hc10 ；
S3.5, fitting H by adopting a quadratic function based on the 10 disease road void areas selected in the step S3.4 ha And H is hc The actual height calculation expression of the disease road void area is obtained as follows:
H ha ＝a hc (H hc ) 2 +b hc H hc +c hc
wherein a is hc 、b hc 、c hc Respectively calculating a secondary term parameter, a primary term parameter and a constant term parameter of an expression for the actual height of the disease road void region;
s3.6, calculating the actual height of the disease road void area based on the actual height calculation expression of the disease road void area obtained in the step S3.5.
3. The method for predicting the altitude evolution of a road void area according to claim 2, wherein the specific implementation method of step S4 comprises the following steps:
s4.1, collecting different moments t 1 、t 2 、…、t n Radar images of the void area in the disease road, and calculating the height of the void area in the disease road according to the method of the step S3Calculating the actual height of the disease road void region according to the actual height calculation expression of the disease road void region>
S4.2 unreliable degree function F using Weibull distribution N Fitting t 1 、t 2 、…、t n And (3) withConstructing a disease road void area height evolution model according to the relation of the road void area height evolution model;
weber distribution uncertainty function F N The calculated expression of (2) is:
wherein t is 0 The eta and beta are offset parameters, scale parameters and shape parameters in sequence;
obtaining a disease road void area height evolution model, wherein the calculation expression is as follows:
4. an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a road void area height evolution prediction method according to any one of claims 1-3 when executing the computer program.
5. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a road void area height evolution prediction method according to any one of claims 1-3.
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