Real-time road quality assessment and optimization system

Abstract

The present invention discloses a road quality real-time evaluation and optimization system, which relates to the field of signal processing technology. The system comprises: a road penetration radar signal transmitting and collecting part, a signal processing part, a signal analysis part and a road quality evaluation and optimization part; the road penetration radar signal transmitting and collecting part is used to use a multi-spectrum ground penetration radar system to collect the reflected signal of the penetration radar signal; the signal processing part is used to perform high-order frequency domain deconvolution on the reflected signal to improve the resolution, and finally extract the signal characteristics; the signal analysis part is used to calculate the water content and density of the road surface according to the extracted signal characteristics; the road quality evaluation and optimization part is used to calculate the quality evaluation index of the road surface. The present invention provides an efficient, accurate and intelligent road quality real-time evaluation and optimization system.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a road quality real-time evaluation and optimization system. Background Art

[0002] With the rapid development of modern transportation infrastructure, the quality of the road surface directly affects the service life of the road, driving comfort and overall traffic safety. Therefore, real-time monitoring and evaluation of road quality has become a crucial link in road management and maintenance. Existing road quality assessment methods mainly rely on macroscopic and microscopic detection methods, such as visual detection, laser scanning, ultrasonic detection and radar detection. Although these technologies can reflect the surface condition and internal structure of the road surface to a certain extent, there are still many shortcomings in practical applications. First, traditional visual detection methods mainly rely on manual inspections or camera systems. Although these methods can intuitively identify obvious damage to the road surface, such as cracks and potholes, their detection results are highly dependent on the experience and judgment of the detection personnel, and it is difficult to detect potential problems inside the road surface. In addition, these methods have a long detection cycle and cannot achieve real-time monitoring and rapid response, resulting in problem discovery often lagging behind the actual occurrence of the problem, increasing the difficulty and cost of road maintenance. Secondly, laser scanning technology, as a non-contact detection method, can quickly obtain the three-dimensional geometric morphology of the road surface, thereby evaluating the flatness and structural integrity of the road surface. However, laser scanning mainly focuses on the geometric morphology analysis of the road surface, and it is difficult to deeply detect the internal structural changes of the road surface. Laser technology has limitations when processing physical parameters such as material density and water content, and cannot accurately reflect the physical state of pavement materials and their changes.

[0003] Ultrasonic testing technology is another widely used pavement testing method, especially in detecting internal defects and delamination of pavement. When ultrasonic waves pass through materials, their propagation speed and reflection characteristics are affected by the internal structure of the material, so they can detect cracks and gaps inside the pavement. However, ultrasonic testing faces the problems of beam attenuation and signal interference in practical applications. Especially in complex materials and multi-layer structures, the attenuation effect of ultrasonic waves is obvious, resulting in reduced detection accuracy. In addition, ultrasonic testing is sensitive to changes in density and elastic modulus of pavement materials, but has limited monitoring capabilities for parameters such as dielectric properties and moisture changes inside the material. Ground penetrating radar (GPR) technology, as a non-destructive testing method, has been increasingly used in pavement quality assessment. GPR detects the internal structure of the pavement by emitting electromagnetic waves and receiving reflected signals, and can provide detailed information on pavement delamination, thickness, and internal defects. However, traditional GPR systems have limitations in detection resolution and signal processing. Multiple reflections and scattering of electromagnetic waves in multi-layer structures increase the complexity of the signal, making it difficult for traditional signal processing algorithms to effectively separate and analyze these complex signals, thereby affecting detection accuracy. In addition, when dealing with materials with high water content or high conductivity, the signal attenuation of GPR technology is more serious, resulting in limited detection depth and failure to fully reflect the deep structure of the road surface. Although the above technologies have their own advantages in road surface detection, they still face a series of challenges in practical applications. Existing technical means can often only provide a single physical parameter or surface information when dealing with the complex internal structure of the road surface, and it is difficult to fully reflect the overall quality of the road surface. Especially when faced with multi-layer structures, heterogeneous materials and complex environmental conditions, existing technologies are difficult to accurately capture and analyze the comprehensive physical state of road materials. This limitation makes it difficult for road managers to make sufficient basis when making maintenance decisions, which can easily lead to inadequate maintenance measures or excessive maintenance, thereby increasing maintenance costs and affecting the service life of the road. Summary of the invention

[0004] The purpose of the present invention is to provide a road quality real-time evaluation and optimization system, which provides an efficient, accurate and intelligent road quality real-time evaluation and optimization system. The system not only solves many deficiencies in the prior art, but also significantly improves the accuracy, real-time performance and comprehensive analysis capabilities of road quality detection.

[0005] In order to solve the above technical problems, the present invention provides a road quality real-time evaluation and optimization system, which includes: a road penetration radar signal transmission and collection part, a signal processing part, a signal analysis part and a road quality evaluation and optimization part; the road penetration radar signal transmission and collection part is used to use a multi-spectrum ground penetration radar system to transmit a penetration radar signal to the target road surface, and then collect the reflection signal of the penetration radar signal; the signal processing part is used to perform high-order frequency domain deconvolution on the reflection signal to improve the resolution, and then apply a nonlinear adaptive signal enhancement algorithm to enhance it, and finally extract the signal features; the signal analysis part is used to extract the signal features based on the nonlinear inversion. The algorithm estimates the nonlinear medium characteristic parameters of the pavement material; uses the multiple scattering theory and the nonlinear least squares method to estimate the pavement layered structure and obtain the thickness of each layer in the pavement layered structure; uses the multi-physical field coupling model to calculate the moisture content and density of the pavement; the pavement quality evaluation and optimization part is used to calculate the quality evaluation index of the pavement according to the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement; respectively calculates the absolute values ​​of the differences between the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement and their corresponding reference values, and takes the item corresponding to the maximum absolute value as the quality optimization item.

[0006] Furthermore, the multi-spectrum ground penetration radar system includes at least 5 different center frequency antennas, whose time resolution is less than or equal to 0.1 nanoseconds and spatial resolution is less than or equal to 1 centimeter; the multi-spectrum ground penetration radar system is amplitude calibrated using a standard reflector; the multi-spectrum ground penetration radar system is time calibrated using a uniform medium of known thickness; the multi-spectrum ground penetration radar system has a scanning interval of 5 centimeters, a time window of 100 nanoseconds, and a stacking number of 32 times / channel; the multi-spectrum ground penetration radar system is grid-scanned along a predetermined route of the target road surface, and each grid point is collected using all available frequencies to obtain a reflected signal.

[0007] Furthermore, the reflected signal is expressed using the following formula:

[0008] ;

[0009] in, For the frequency ,depth and time The reflected signal; For the The amplitude of a frequency component; For the The wave number of the frequency component; For the The attenuation coefficient of the frequency component; is the total number of frequency components; for Bessel functions of order; For the The parameters of the Bessel function; is the total order of Bessel function; For the The intensity of the relaxation process; For the The characteristic time of a relaxation process; For the The distribution parameters of the relaxation process; is the total number of relaxation processes; and All are integer subscript indices.

[0010] Furthermore, the signal processing part performs high-order frequency domain deconvolution on the reflected signal to improve the resolution through the following formula, and then applies a nonlinear adaptive signal enhancement algorithm to enhance it:

[0011] ;

[0012] in, is the enhanced signal; for Fourier transform of is the system transfer function; for The conjugate complex function of ; is the noise-to-signal ratio; is the regularization parameter; is the phase correction function; is the nonlinear enhancement factor; For the Harmonic enhancement coefficients; For the The center frequency of each harmonic; For the The shape parameters of the harmonics; is the total number of harmonics; the enhanced signal is analyzed using a convolutional neural network Perform feature extraction to obtain signal features.

[0013] Furthermore, the signal analysis part estimates the nonlinear medium characteristic parameters of the pavement material based on the nonlinear inversion algorithm according to the extracted signal characteristics through the following formula:

[0014] ;

[0015] ;

[0016] in, The frequency is , the temperature is and pressure Nonlinear medium characteristic parameters of pavement materials at ; is the high frequency limiting dielectric constant; For the Temperature-dependent dielectric strength of each relaxation process; For the The pressure-dependent characteristic time of each relaxation process; For the The temperature-dependent broadening parameter of the relaxation process; and are the third-order nonlinear polarizability and the fifth-order nonlinear polarizability; is the electric field strength; is the characteristic time under standard conditions; is the activation energy; is the activation volume; is the Boltzmann constant; Representation calculation The signal amplitude of Representation calculation signal frequency.

[0017] Furthermore, the signal analysis part estimates the pavement layered structure using the multiple scattering theory and the nonlinear least squares method through the following formula to obtain the thickness of each layer in the pavement layered structure:

[0018] ;

[0019] ;

[0020] ;

[0021] in, For the Thickness of the layer; is the speed of light in a vacuum; For the Complex reflection coefficient at the top of the layer; For the The frequency of the layer is Nonlinear medium characteristic parameters of pavement materials; For the Layer and Fresnel reflection coefficient of layer interface; For the The frequency of the layer is Nonlinear medium characteristic parameters of pavement materials.

[0022] Furthermore, the signal analysis part calculates the moisture content and density of the road surface using the multi-physics field coupling model through the following formula:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] in, is the moisture content of the road surface; is the nonlinear medium characteristic parameter of the pavement material; is the nonlinear medium characteristic parameter of solid particles; is the nonlinear medium characteristic parameter of water; is the porosity; is the geometric structure parameter; is the density of the road surface; is the density of solid particles; is the density of water; is the air density; is the bulk modulus of the mixed medium; , and They are the bulk modulus of solid, water and air respectively.

[0028] Furthermore, the pavement quality evaluation and optimization part calculates the pavement quality evaluation index according to the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the water content and density of the pavement. The formula is:

[0029] ;

[0030] in, The moisture content of the preset standard road surface; The density of the preset standard road surface; is the number of pavement layers; For the preset Standard nonlinear medium characteristic parameters of the pavement layer; The preset frequency is , the temperature is and pressure Nonlinear medium characteristic parameters of pavement materials at ; is the preset first weight; is the preset second weight; is the preset third weight; is the preset fourth weight; For the preset The weight of the standard nonlinear medium characteristic parameter of the pavement layer; when the quality evaluation index of the pavement is calculated If the quality exceeds the set threshold, it is judged that there is a problem with the road quality.

[0031] The real-time evaluation and optimization system of road quality of the present invention has the following beneficial effects: the present invention has made a breakthrough in detection accuracy. Traditional road detection technology can often only provide a single physical parameter or surface information, while the present invention realizes a comprehensive evaluation of the internal structure and material properties of the road surface by comprehensively analyzing the nonlinear medium characteristic parameters, layered structure thickness, water content and density of the road surface material and other key physical parameters. In particular, through the nonlinear inversion algorithm, the present invention can accurately estimate the medium characteristics of the road surface material under different frequency, temperature and pressure conditions, which enables the system to capture the microstructural changes and deep physical state of the material. This multi-dimensional and multi-level analysis capability significantly improves the accuracy of road quality detection, especially in the case of complex multi-layer structures and heterogeneous materials, the detection depth and resolution that are difficult to achieve with traditional technologies are effectively solved in the present invention. Secondly, the present invention greatly improves the real-time performance of road quality assessment. Existing road surface detection methods usually require offline data processing and analysis, and it is difficult to achieve real-time monitoring and rapid response. The present invention can process complex reflection signals collected from ground penetration radar systems in real time through efficient signal processing technology, especially high-order frequency domain deconvolution and nonlinear adaptive enhancement algorithms. This real-time processing capability enables the system to analyze data and generate quality assessment reports in real time during the road surface inspection process, greatly shortening the time interval from data collection to decision output. This rapid response capability is particularly important for road managers, as it helps them take effective maintenance measures when problems first occur to prevent them from further deteriorating, thereby extending the service life of the road and improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 A schematic diagram of the system structure of a road quality real-time assessment and optimization system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Example 1: Reference Figure 1 , a real-time road quality evaluation and optimization system, the system includes: a road penetration radar signal transmission and collection part, a signal processing part, a signal analysis part and a road quality evaluation and optimization part; the road penetration radar signal transmission and collection part is used to use a multi-spectrum ground penetration radar system to transmit a penetration radar signal to a target road surface, and then collect the reflected signal of the penetration radar signal; the signal processing part is used to perform high-order frequency domain deconvolution on the reflected signal to improve the resolution, and then apply a nonlinear adaptive signal enhancement algorithm to enhance it, and finally extract signal features; the signal analysis part is used to estimate the road material based on the nonlinear inversion algorithm according to the extracted signal features The nonlinear medium characteristic parameters of the material are used; the pavement layered structure is estimated by using the multiple scattering theory and the nonlinear least square method to obtain the thickness of each layer in the pavement layered structure; the moisture content and density of the pavement are calculated by using the multi-physical field coupling model; the pavement quality evaluation and optimization part is used to calculate the quality evaluation index of the pavement according to the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement; the absolute values ​​of the differences between the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement and their corresponding reference values ​​are calculated respectively, and the item corresponding to the maximum absolute value is used as the quality optimization item.

[0036] Specifically, the multi-spectrum ground penetrating radar system has multiple operating frequency bands, which means that it can emit radar signals of different frequencies, and each frequency signal has different penetration depth and reflection characteristics in the pavement material. Lower frequency signals can penetrate deeper layers but have lower resolution, while higher frequency signals can provide higher resolution but shallower penetration depth. Through the multi-spectrum system, the system can make comprehensive use of these signals of different frequencies to obtain richer internal information of the pavement, thereby providing basic data for subsequent signal processing and analysis. When the radar signal is emitted to the pavement, the signal will be reflected, refracted and scattered according to parameters such as the dielectric constant, conductivity and humidity of the pavement material. Since the pavement material is usually not a single homogeneous material, but is composed of materials of different layers and properties, the radar signal will be reflected at the interface of these different layers. The characteristics of the reflected signal, such as intensity, phase and time delay, are closely related to the physical properties of these materials. Therefore, through the received reflected signal, the system can infer a variety of information about the internal structure of the pavement, such as the thickness of each layer of material, changes in dielectric constant, and possible defects or anomalies. The acquisition part captures these complex reflection signals through high-precision receiving equipment and performs preliminary processing to reduce noise and interference to ensure the authenticity and effectiveness of the signal. The received signal contains not only the reflection information from the surface of the road surface, but also the reflection information from different layers and even the bottom layer inside the road surface. Therefore, these signals contain a lot of details about the internal structure of the road surface. This part converts these time domain reflection signals into frequency domain information that can be further analyzed through the conversion of time domain and frequency domain, so that the system can more effectively extract the internal characteristics of the road surface in the subsequent signal processing link. It is worth mentioning that the design of this part takes into account the complexity of actual road conditions. The diversity of road materials, humidity changes, temperature changes, vehicle loads, etc. may affect the transmission and reflection of signals. Therefore, the system adopts a multi-spectrum transmission method, which can not only capture more comprehensive reflection information at different frequencies, but also offset or correct errors and deviations caused by changes in the external environment through comprehensive analysis of these different frequency signals, thereby ensuring the accuracy and reliability of signal acquisition. This multi-spectrum signal acquisition method enables the system to adapt to a variety of complex road conditions, thereby providing reliable real-time data support. In practical applications, the performance of the road penetration radar signal transmission and collection part is directly related to the overall function of the system. It not only needs to transmit and receive radar signals efficiently, but also needs to be able to provide consistent and high-precision data output under a variety of complex road conditions. This part successfully overcomes the shortcomings of the traditional single-frequency radar system in complex road environments through innovative multi-spectrum radar technology and efficient signal reception and processing mechanisms, and significantly improves the real-time and accuracy of road quality assessment.This design enables the system to provide timely and accurate road quality information at construction sites or during highway maintenance, thereby helping relevant personnel make scientific decisions and improving the efficiency and effectiveness of road maintenance and optimization.

[0037] The radar reflection signals received by the system are often affected by noise. At the same time, due to the multi-layer structure and complex physical properties of the pavement material, the signals may overlap and be distorted. In order to restore the real situation inside the pavement, the system first performs high-order frequency domain deconvolution on the signal. The purpose of deconvolution is to reduce or eliminate the convolution effect through inverse operations so that overlapping signals can be effectively separated. This process is carried out in the frequency domain because frequency domain deconvolution can process the frequency components of the signal more accurately, especially for those subtle changes that are difficult to distinguish in the time domain, the frequency domain processing method can restore them more effectively. This processing method greatly improves the time and space resolution of the signal, allowing the system to capture more detailed pavement structure information. Then, after the deconvolution process, the system applies a nonlinear adaptive signal enhancement algorithm to enhance the signal. Due to the nonlinear characteristics of the internal materials of the pavement and the complex interactions between different layers, the reflected signal often contains rich nonlinear information. Traditional linear enhancement methods may fail when processing such signals, or even introduce more errors. Therefore, the system adopts a nonlinear adaptive enhancement algorithm, which can automatically adjust the enhancement parameters according to the characteristics of the signal, so that the important features in the signal are amplified, while the noise and other irrelevant information are effectively suppressed. The adaptive characteristics mean that the system can automatically adjust the processing strategy according to the different situations of the real-time signal, so as to ensure the best processing effect under different road conditions. This nonlinear signal enhancement not only improves the quality of the signal, but also further improves the accuracy of feature extraction. After signal enhancement, the system enters the signal feature extraction stage. The task of this stage is to extract the characteristic parameters that best represent the road surface state from the enhanced signal. The core of road quality assessment lies in how to extract the key parameters closely related to the characteristics and structural state of the road material from the complex radar reflection signal. Through the previous high-order frequency domain deconvolution and nonlinear adaptive enhancement processing, the main features of the signal have been clearly presented. On this basis, the system uses a series of feature extraction algorithms to further analyze the signal to extract important information such as the dielectric constant of each layer of the road surface, reflection intensity, phase change, etc. This information can not only reflect the physical properties of the road surface material, but also reveal the structural changes and potential defects inside the road surface.

[0038] Since the electromagnetic properties of pavement materials, such as dielectric constant and conductivity, are expressed as nonlinear and complex relationships in radar signals, direct linear methods to estimate these parameters are often not accurate enough. The application of nonlinear inversion algorithms enables the system to reversely infer these hidden material property parameters based on the extracted signal features. These parameters are crucial to understanding the actual physical state of the pavement because they directly reflect the material composition, uniformity, and possible defects or anomalies. Through this inversion process, the system is able to build a physical model that is closer to the actual pavement conditions, thereby providing a reliable data basis for subsequent evaluation and optimization. Next, the system combines multiple scattering theory and nonlinear least squares to estimate the thickness of the pavement layered structure. Pavements are usually composed of multiple layers of different materials, and the thickness and material properties of each layer affect the reflection pattern of the radar signal. Multiple scattering theory takes into account the multiple reflections and scattering processes of radar signals between different layers, which is a detail that cannot be captured by traditional single reflection models. This theory can more accurately simulate complex signal propagation paths, especially when facing pavements with significant interlayer interfaces, and can better reflect small changes in the signal. Nonlinear least squares is used to optimize these model parameters to minimize the difference between the simulated signal and the actual received signal. In this way, the system can accurately estimate the thickness information of each layer, thus fully depicting the layered structure of the road surface. In addition, the system also uses the multi-physics field coupling model to calculate the moisture content and density of the road surface. The moisture content and density of the road surface are key factors affecting its mechanical properties and durability. Changes in these parameters can lead to a decline in road performance or even structural damage. The multi-physics field coupling model can accurately estimate these parameters under different environmental conditions by comprehensively considering the interactions between multiple physical fields such as electromagnetic fields, humidity fields, and density fields. For example, when the road surface is in a humid environment, the reflection characteristics of the radar signal will change significantly due to the high dielectric constant of water. The coupling model can capture these changes and infer the actual moisture content and density of the road surface through comprehensive analysis of the signals. Through these analyses, the system can calculate a comprehensive road quality assessment index, which combines the nonlinear medium characteristic parameters of the road surface, the thickness of the layered structure, and multiple dimensions such as moisture content and density. This index can not only quickly evaluate the overall quality of the road surface, but also identify possible quality problems based on specific parameter changes. The system further analyzes the differences between these parameters and the reference values, especially those with the largest absolute values ​​of these differences as the quality items that need to be optimized first. This approach enables the system to not only perform static evaluations, but also dynamically identify potential problems in the road surface during use and provide guidance for optimization.

[0039] The pavement quality assessment part builds a complex model based on multi-dimensional physical parameters to calculate the pavement quality assessment index. This index reflects the overall health of the pavement by weighting each parameter. Nonlinear dielectric properties of pavement materials, such as dielectric constant and conductivity, can reveal the microscopic characteristics of pavement materials, including their composition, uniformity, and possible signs of aging or damage; the thickness of the layered structure provides key information about the structural integrity of the pavement, especially when evaluating the bearing capacity and durability of the pavement, these data are crucial; moisture content and density reflect the physical state of the pavement material, which have a direct impact on the mechanical properties and long-term stability of the pavement. By comprehensively analyzing these parameters, the system can generate a comprehensive quality assessment index to quantify the overall quality of the pavement. This index not only helps users quickly understand the current state of the pavement, but also allows customized assessments for specific needs by adjusting the weights of different parameters. After completing the calculation of the quality assessment index, the system further performs difference analysis. The difference analysis here refers to comparing the actual measured pavement parameters with the preset reference standards to determine which parameters deviate from the ideal state. Specifically, the system calculates the absolute difference between each parameter and its reference value, which reflects the distance between the actual road surface and the ideal state. The larger the difference, the less ideal the condition of the parameter, which also means that there are greater potential problems in this part of the road surface. In this way, the system can effectively identify the main sources of road quality problems and provide direction for subsequent optimization. It is particularly worth mentioning that the system identifies the ones with the largest absolute values ​​of these differences as the key items for quality optimization. The logic behind this approach is that although all parameters will affect the overall quality of the road surface, the abnormality of certain key parameters may have a more serious impact on the performance of the road surface. For example, if the water content of the road surface is significantly higher than the standard value, it may cause the mechanical properties of the road surface material to deteriorate, causing cracks or potholes; while too thick or too thin layered structures may affect the bearing capacity and durability of the road surface. Therefore, by focusing on optimizing these parameters that have the greatest impact on the quality of the road surface, the system can achieve the most effective maintenance and repair with limited resources, and improve the service life and safety of the overall road surface. In addition, the road surface quality assessment and optimization part also has the functions of dynamic adjustment and adaptive optimization. As time goes by and the conditions of road use change, the state of the road surface is also changing. The system is able to monitor these changes in real time and adapt to different environments and usage conditions by dynamically adjusting the weights and reference standards in the assessment model. For example, in the rainy season, the system may pay more attention to the moisture content and density changes of the pavement, while in the dry season, it may pay more attention to the aging and structural integrity of the material. This adaptive ability enables the system to not only provide accurate assessments under static conditions, but also maintain the effectiveness and reliability of the assessment under dynamic conditions.

[0040] Embodiment 2: The multi-spectrum ground penetration radar system comprises at least five antennas with different center frequencies, and its time resolution is less than or equal to 0.1 nanoseconds, and its spatial resolution is less than or equal to 1 centimeter; a standard reflector is used to perform amplitude calibration on the multi-spectrum ground penetration radar system; a uniform medium of known thickness is used to perform time calibration on the multi-spectrum ground penetration radar system; the scanning interval of the multi-spectrum ground penetration radar system is 5 centimeters, the time window is 100 nanoseconds, and the number of stacks is 32 times / channel; the multi-spectrum ground penetration radar system is grid-scanned along a predetermined route of the target road surface, and each grid point is collected using all available frequencies to obtain a reflected signal.

[0041] Specifically, the main advantage of using a multi-spectrum antenna is that it can simultaneously obtain reflected signals at different frequencies, and electromagnetic waves of different frequencies have different penetration depths and resolution characteristics. Antennas with lower frequencies can penetrate deeply into the road surface and detect deeper structural information, while antennas with higher frequencies can provide higher spatial resolution and capture fine structures in the shallow surface layer. This multi-spectrum configuration allows the system to comprehensively utilize the advantages of each frequency signal to obtain comprehensive internal information of the road surface. The setting of a time resolution of less than or equal to 0.1 nanoseconds and a spatial resolution of less than or equal to 1 cm ensures that the system can accurately capture tiny time delays and spatial changes, thereby improving the perception of road surface conditions. In order to ensure the accuracy of the radar system, the embodiment describes the process of amplitude calibration using a standard reflector. Amplitude calibration is to ensure that each frequency antenna can provide a consistent amplitude response under the same conditions, so that the intensity of the reflected signal can directly reflect the electromagnetic properties of the road surface material. The standard reflector usually has a known electromagnetic reflection characteristic. By placing the reflector in the field of view of the radar system and recording its reflected signal, the amplitude response of each antenna can be calibrated to eliminate the amplitude error caused by differences in antenna characteristics or changes in the external environment. Equally important is the time calibration process. The radar system is time-calibrated using a homogeneous medium of known thickness to ensure that the radar system's time delay measurement is accurate. The key to time calibration is to accurately measure the time interval from the emission to the reflection of the radar signal, which directly affects the calculation of the thickness of each layer of the road surface. The homogeneous medium has a known electromagnetic wave propagation speed and thickness. Through calibration, the system's time base can be accurately determined, thereby improving the accuracy of the road structure measurement. In actual operation, the multi-spectrum ground penetrating radar system is set to a scanning interval of 5 cm, a time window of 100 nanoseconds, and a stacking number of 32 times / channel. These parameters are set to optimize to ensure high resolution and high signal-to-noise ratio of the data. The scanning interval of 5 cm means that the system collects data every 5 cm as it moves along the road surface, which ensures that no details are missed in a large road surface area. The time window of 100 nanoseconds limits the system's maximum time range for the reflected signal, which is related to the maximum detection depth of the road surface. The stacking number of 32 times / channel is to reduce random noise and improve signal clarity and accuracy through the superposition of multiple signal acquisitions. During data collection, the system performs grid scanning along the predetermined route of the target road surface, and each grid point uses all available frequencies for signal collection. Grid scanning is an accurate measurement method that divides the road surface into multiple small areas, each of which performs independent signal collection and analysis. This method can ensure the comprehensiveness and precision of road surface information. Through multi-frequency data collection at each grid point, the system can obtain road surface reflection signals of different depths and resolutions, thereby more comprehensively depicting a multi-dimensional image of the internal structure of the road surface.

[0042] Embodiment 3: The reflected signal is expressed using the following formula:

[0043] ;

[0044] in, For the frequency ,depth and time The reflected signal; For the The amplitude of a frequency component; For the The wave number of the frequency component; For the The attenuation coefficient of the frequency component; is the total number of frequency components; for order Bessel function; For the The parameters of the Bessel function; is the total order of Bessel function; For the The intensity of the relaxation process; For the The characteristic time of a relaxation process; For the The distribution parameters of the relaxation process; is the total number of relaxation processes; and All are integer subscript indices.

[0045] Specifically, in this formula, the amplitude of the reflected signal Represents the reflection intensity of electromagnetic waves when passing through different materials, which is directly related to the electromagnetic properties of the material, such as dielectric constant and conductivity. Different materials have different reflection and absorption characteristics for electromagnetic waves, resulting in different amplitudes of reflected signals of different frequencies. By analyzing these amplitude changes, the system can infer the electromagnetic properties of materials at different levels, thus providing a basis for the identification and quality assessment of pavement materials. The wave number in the formula It describes the propagation speed of electromagnetic waves in different media. The wave number is related to the wavelength of the signal, so it can reflect the change in the speed of electromagnetic waves when they propagate in the pavement material. Since the wave number is closely related to the physical properties of the medium, especially at different depths, changes in pavement materials will lead to changes in the wave number. Therefore, analyzing these wave numbers can reveal the layered structure inside the pavement. With these data, the system can construct a three-dimensional model of the pavement, which provides a reference for optimization and maintenance. Attenuation coefficient It plays a role in describing the signal energy loss in the formula. When electromagnetic waves propagate in pavement materials, they will gradually attenuate due to absorption and scattering effects. The size of the attenuation coefficient reflects the material's ability to absorb electromagnetic waves. High attenuation means that the signal loses more energy in the material, which is usually related to the material's physical properties such as density and humidity. Therefore, by analyzing the attenuation coefficient, the system can evaluate the physical state of the material, such as whether the humidity exceeds the standard or whether there are abnormal changes in the material, which is crucial for monitoring and maintaining pavement quality. In addition, the formula contains the Bessel function , which is used to describe the multiple scattering phenomenon of electromagnetic waves in the road surface. Bessel functions play an important role in wave analysis, especially when describing the propagation of waves in complex media. They can effectively capture the scattering, reflection and diffraction effects of electromagnetic waves. Bessel functions of different orders reflect the behavior of waves at different scales. Through these functions, the system can analyze the interaction between different levels in the road surface. This detailed analysis helps to identify small defects or unevenness in the road surface and provide higher accuracy for quality assessment. Finally, the description of the relaxation process is also introduced in the formula. This term is used to express it. The relaxation process is the manifestation of the delay effect of internal molecules or atoms when the material responds to an external electromagnetic field. After being stimulated by electromagnetic waves, different materials need a certain amount of time for their internal structure to reach a stable state. This time delay affects the phase and amplitude of the signal. By analyzing the relaxation process, the system can deeply understand the microscopic dynamic characteristics of the material, which is particularly important when evaluating the degree of material aging or damage.

[0046] Embodiment 4: The signal processing part performs high-order frequency domain deconvolution on the reflected signal to improve the resolution by the following formula, and then applies a nonlinear adaptive signal enhancement algorithm to enhance it:

[0047] ;

[0048] in, is the enhanced signal; for Fourier transform of is the system transfer function; for The conjugate complex function of ; is the noise-to-signal ratio; is the regularization parameter; is the phase correction function; is the nonlinear enhancement factor; For the Harmonic enhancement coefficients; For the The center frequency of each harmonic; For the The shape parameters of the harmonics; is the total number of harmonics; the enhanced signal is analyzed using a convolutional neural network Perform feature extraction to obtain signal features.

[0049] Specifically, the basis of the signal processing part is to perform Fourier transform on the original reflection signal and convert the time domain signal into the frequency domain signal. In this process, the original time signal is converted into frequency domain representation This conversion allows the system to perform more complex operations on the signal in frequency space. The advantage of Fourier transform is that it can decompose complex time domain waveforms into superpositions of different frequency components, thus providing a more efficient way for deconvolution and enhancement processing. In the high-order frequency domain deconvolution process, the main task of the system is to eliminate the radar system transfer function. The transfer function describes the response characteristics of the radar system to the input signal, which usually includes the frequency response of the device itself, noise and other non-ideal factors. Through the deconvolution operation, that is, through This expression, the system attempts to reverse these effects and restore the original reflection signal characteristics. In this formula, is the conjugate complex form of the transfer function, which is used to compensate the signal, and reflects the power response of the system. In order to avoid amplifying noise during the reverse operation, a regularization parameter is introduced into the formula , which combines the noise-to-signal ratio , which is used to suppress the abnormal amplification effect caused by noise. In this way, the system can maintain the stability and clarity of the signal while eliminating system distortion. After deconvolution, the signal needs to be further enhanced to improve its resolution and feature clarity. The nonlinear adaptive signal enhancement algorithm plays a key role here. The algorithm adjusts the phase and amplitude of the signal to highlight the important features in the signal. Specifically, the phase correction function in the formula is used to correct the phase error of the frequency component, while the nonlinear enhancement factor Then through The nonlinear enhancement of the signal is realized by the expression of . The nonlinear enhancement here is controlled by the square term of the signal amplitude, that is, when the signal amplitude is large, the enhancement effect is relatively weakened, and when the signal is weak, the enhancement effect is more obvious. This adaptive enhancement mechanism enables the subtle features in the signal to be effectively amplified without introducing too much distortion to the strong signal part. Furthermore, the system also applies a harmonic enhancement mechanism to improve the frequency characteristics of the signal. Harmonic enhancement is achieved by weighting different harmonic components in the signal. This item reflects the enhancement of each harmonic component. Here, is the harmonic enhancement coefficient, which determines the enhancement amplitude of each harmonic, and is the center frequency of the harmonic, which controls which frequency range of the signal is enhanced. In this way, the system can optimize the signal in a specific frequency range so that the key frequency components can be better represented. This processing is particularly important in multi-spectrum ground penetration radar systems, because electromagnetic waves of different frequencies have different propagation and reflection characteristics in the road surface. By optimizing these frequency components, the system can obtain more comprehensive information about the internal structure of the road surface. Finally, the enhanced signal Feature extraction is performed using a convolutional neural network (CNN). This step serves as a bridge in the entire processing flow, converting the enhanced frequency domain signal into a feature vector for further analysis. Convolutional neural networks have significant advantages in processing images and signal feature extraction, and can automatically learn and extract key patterns and features in signals. By applying convolutional neural networks to the enhanced signal, the system can further denoise and extract core features related to road quality. These features will be used for subsequent road condition analysis and quality assessment, helping the system make more accurate judgments.

[0050] Embodiment 5: The signal analysis part estimates the nonlinear medium characteristic parameters of the pavement material based on the nonlinear inversion algorithm according to the extracted signal characteristics through the following formula:

[0051] ;

[0052] ;

[0053] in, The frequency is , the temperature is and pressure Nonlinear medium characteristic parameters of pavement materials at ; is the high frequency limiting dielectric constant; For the Temperature-dependent dielectric strength of each relaxation process; For the The pressure-dependent characteristic time of each relaxation process; For the The temperature-dependent broadening parameter of the relaxation process; and are the third-order nonlinear polarizability and the fifth-order nonlinear polarizability; is the electric field strength; is the characteristic time under standard conditions; is the activation energy; is the activation volume; is the Boltzmann constant; Representation calculation The signal amplitude of Representation calculation signal frequency.

[0054] Specifically, in Embodiment 5 of the present invention, the formula proposed is used to estimate the nonlinear medium characteristic parameter of the pavement material , which is a complex model designed to describe in detail how a material behaves at different frequencies. ,temperature and pressure The electromagnetic response behavior under certain conditions. This formula covers not only the linear response of the material, but also the dynamic characteristics of the nonlinear polarization effect and relaxation process. The accurate estimation of these parameters is crucial for the real-time evaluation and optimization of pavement quality, especially when the complex physical properties of pavement materials need to be finely identified and analyzed.

[0055] First, the first part of the formula The linear response of the pavement material and the influence of the relaxation process are described. Here, It represents the dielectric constant at the high frequency limit, that is, when the frequency tends to infinity, the dielectric response of the material reaches the limit value. This parameter reflects the intrinsic characteristics of the material under high frequency conditions, which is mainly contributed by the electronic polarization and atomic polarization inside the material. The response speed of electrons and atoms is much higher than the molecular polarization and relaxation process. Therefore, at high frequencies, the dielectric response of the material tends to be stable. This value is the characterization of this limit response. The relaxation process is the time delay phenomenon exhibited by the material when responding to an external electromagnetic field. In actual pavement materials, this relaxation process is caused by molecular polarization or lattice vibration, that is, the molecules or lattices in the material cannot reach equilibrium instantly after being affected by an external electric field, but need to gradually recover to a stable state after a certain period of time. It is used to describe the effect of these relaxation processes on the dielectric properties of the material. Each relaxation process corresponds to a specific time constant. , this time constant is related to the pressure The effect of temperature on the relaxation process is shown in Figure 2. and To describe, is the temperature-dependent dielectric strength of the relaxation process, and is the broadening parameter, which describes the spectrum width of the relaxation process. In order to more accurately describe the kinetic behavior of these relaxation processes, the formula introduces and activation volume The activation energy and activation volume are the energy barriers and volume changes that the material needs to overcome during the relaxation process, which determine the response speed of the material at different temperatures and pressures. Specifically, the time constant of the relaxation process Depend on Given, where is the characteristic time under standard conditions, is the Boltzmann constant, The two terms in the function describe the effects of temperature and pressure on the relaxation process. The higher the temperature, the faster the relaxation process, and the greater the pressure, the shorter the relaxation process. Through these models, the system can dynamically estimate the response behavior of the material under actual road conditions, thereby providing reliable data support for the accurate assessment of road quality. However, the electromagnetic response of the material is not limited to linear effects. Especially when the road surface is subjected to a strong electric field, nonlinear effects also become very important. The subsequent part of the formula introduces the description of nonlinear polarization, through To express, and They represent the third-order and fifth-order nonlinear polarizability of the material, respectively. The nonlinear polarizability reflects the response characteristics of the material under a strong electric field. This nonlinear response may not be significant when the electric field strength is low, but as the electric field strength increases, the polarization response of the material will be significantly enhanced, showing superlinear characteristics. This is particularly important in high-power radar systems because these nonlinear effects can cause distortion of signal propagation and reflection, thus affecting the actual electromagnetic property measurement of the pavement material. The term represents the third-order nonlinear response of the material, which mainly describes the nonlinear polarization phenomenon of the material under a medium-intensity electric field. The terms are further extended to higher-order nonlinear responses, which are usually only apparent under very strong electric fields. By introducing these higher-order nonlinear polarization terms, the formula is able to capture the complex electromagnetic behavior of materials under extreme conditions. This nonlinear effect has a significant impact on the electromagnetic response of pavement materials in practical applications, such as under the irradiation of strong electromagnetic waves from radar systems, and must be considered in the model. The overall design of the formula reflects the combined effect of multiple physical effects of the material. By combining linear and nonlinear responses, the system is able to comprehensively analyze the electromagnetic properties of pavement materials under different environmental conditions. In particular, in complex environments, changes in temperature and pressure can significantly affect the electromagnetic response of materials. By dynamically adjusting these factors, the system can more accurately estimate the dielectric properties of materials. This flexibility enables the system to adapt to various pavement conditions, thereby improving the accuracy and reliability of pavement quality assessment.

[0056] Embodiment 6: The signal analysis part estimates the pavement layered structure using the multiple scattering theory and the nonlinear least squares method through the following formula to obtain the thickness of each layer in the pavement layered structure:

[0057] ;

[0058] ;

[0059] ;

[0060] in, For the Thickness of the layer; is the speed of light in a vacuum; For the Complex reflection coefficient at the top of the layer; For the The frequency of the layer is Nonlinear medium characteristic parameters of pavement materials; For the Layer and Fresnel reflection coefficient of layer interface; For the The frequency of the layer is Nonlinear medium characteristic parameters of pavement materials.

[0061] Specifically, in the formula Indicates The thickness of the layer is a key parameter that the system hopes to determine through signal analysis. The estimation of thickness depends on the interaction of electromagnetic waves between different layers, especially the reflection coefficient and propagation characteristics to infer the physical thickness of each layer. Term, describing the frequency Effect on the phase of the complex reflection coefficient. This term reveals how the phase of the reflected wave changes as it propagates between layers by differentiating the phase difference caused by the frequency change. The phase change is directly related to the layer thickness, because the length of the electromagnetic wave's propagation path in the material affects its phase, so this can be used to indirectly measure the layer thickness. In addition, a correction factor is included in the formula , which takes into account the influence of the dielectric properties of the material on the propagation of electromagnetic waves. The nonlinear dielectric property parameters of the material Describes the propagation speed and attenuation characteristics of electromagnetic waves in materials. Since these characteristics vary with frequency, it is necessary to use the frequency derivative This correction factor ensures that the effect of the material's dielectric properties on the thickness estimation at different frequencies can be correctly compensated, thereby improving the accuracy of the thickness estimation. Next, the formula It is The complex reflection coefficient of the top layer indicates the electromagnetic wave The reflection coefficient is a complex number that contains the amplitude and phase information of the reflected wave. The calculation of this coefficient takes into account the multiple reflections and interference effects of electromagnetic waves when they propagate between layers. Through the multiple scattering theory, the system can simulate the multiple reflection behaviors of electromagnetic waves between layers and integrate these reflection behaviors into a total complex reflection coefficient. This coefficient not only takes into account direct reflection, but also includes the part of the electromagnetic wave that is reflected again after propagating within the layer. The cumulative effect of these multiple reflections is crucial for accurately estimating the thickness of each layer. In the calculation of Item 1 describes in detail Layer and Reflection and transmission characteristics between layers. Here, It is Layer and The Fresnel reflection coefficient of the layer interface reflects the effect of the refractive index difference between the two layers of material on the reflection. The reflection coefficient is determined by the dielectric properties of each layer. In particular, the greater the difference in refractive index between the two layers, the greater the reflection coefficient. Therefore, by calculating the Fresnel reflection coefficient, the system can accurately estimate the interface reflection characteristics between the layers. In this expression, the exponential term Describes the electromagnetic waves The phase delay effect in the layer. The phase delay is caused by the combined effect of wavelength and frequency when the electromagnetic wave propagates in the medium. layer, due to the dielectric properties of the material , its phase will change, and this change accumulates after multiple reflections and eventually affects the total complex reflection coefficient Using this formula, the system can relate these phase changes to the thickness of the layer. The parameters are iteratively adjusted by nonlinear least squares method to minimize the difference between the theoretically calculated reflection coefficient and the actual measured value. This iterative optimization process makes the final estimated thickness value have high accuracy. Through detailed modeling of the above multiple physical processes, the present invention can accurately estimate the thickness of each layer in the pavement layered structure through complex signal processing and analysis. This is of great significance for the real-time evaluation of pavement quality, because the durability, strength and service life of the pavement are closely related to its internal structure. The thickness change of the layered structure may reflect the aging, damage or structural defects of the pavement material, so accurate measurement of this thickness information can provide key data support for pavement maintenance and optimization. Compared with the traditional single reflection or simple layer thickness estimation method, the analysis method combining multiple scattering theory and nonlinear least squares method of the present invention can capture more complex internal reflection characteristics of the pavement, especially in the case of multi-layer materials, showing higher accuracy and reliability. This high-precision analysis capability enables the system to provide more accurate and meaningful evaluation results when dealing with complex pavement environments, thereby providing a scientific basis for road maintenance and optimization and improving road safety and durability. Through these advanced signal analysis methods, the present invention has broad application potential in modern transportation infrastructure management and can significantly improve the efficiency and effectiveness of road maintenance.

[0062] Embodiment 7: The signal analysis part calculates the water content and density of the road surface using the multi-physics field coupling model through the following formula:

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] in, is the moisture content of the road surface; is the nonlinear medium characteristic parameter of the pavement material; is the nonlinear medium characteristic parameter of solid particles; is the nonlinear medium characteristic parameter of water; is the porosity; is the geometric structure parameter; is the density of the road surface; is the density of solid particles; is the density of water; is the air density; is the bulk modulus of the mixed medium; , and They are the bulk modulus of solid, water and air respectively.

[0068] Specifically, pavement materials are usually complex multiphase media composed of multiple components, including solid particles, water and air. Each component has its own unique physical properties, such as dielectric constant, density, bulk modulus, etc. These properties determine the behavior of electromagnetic waves when propagating in the material, and also affect the macroscopic physical properties of the material, such as density and water content. In this model, the water content It is a crucial parameter. Water in pavement materials not only affects the electromagnetic properties of the material, but is also directly related to the mechanical properties of the pavement. The presence of water may cause the material to soften or swell, leading to the deterioration of the pavement structure. Therefore, accurate calculation of the water content of the pavement is of great significance for evaluating the overall quality and durability of the pavement. In order to calculate the water content , the model uses nonlinear medium characteristic parameters This is because the dielectric constant of a material changes significantly with the change of its internal moisture. The dielectric constant is a parameter that reflects the ability of a material to respond to an external electric field. It depends not only on the composition of the material, but also on the geometric properties of the internal structure. The geometric structure parameters introduced in the formula It is to describe how the microstructure inside the material affects its macroscopic electromagnetic properties. Specifically, different geometric structures will change the propagation path and speed of electromagnetic waves in the material, and these effects will eventually be reflected in the overall dielectric constant of the material. The calculation formula for water content is based on the mixed medium theory. Mixed medium theory is used in electromagnetism to describe the overall electromagnetic properties of composite media composed of different dielectric materials. Through this theory, the overall dielectric constant of the material can be regarded as the weighted average of the dielectric constants of its components. This formula derives the nonlinear dielectric characteristic parameters of the material by weighting the dielectric constants of each component and combining them with the geometric structure parameters. Then, through reverse deduction, we can get Calculate the water content The physical significance of this calculation process is that when the water content of the material increases, the overall dielectric constant of the material increases, because the dielectric constant of water is usually much larger than that of solid materials and air. will increase significantly. Therefore, by measuring and calculating , the system can sensitively detect the changes in moisture inside the material. In addition, the density is another key parameter, which is closely related to the mechanical properties of the material. The density of a material depends not only on the density of its constituent components, but also on the distribution of these components in the material. The density calculation formula is based on the law of mass conservation and mixed media theory, which describes the mass distribution of solid particles, water, and air in the pavement material. By combining the density of each part with its volume fraction, the total density of the entire material can be calculated. In this model, the calculation of density also reflects the change in the state of the material under different environmental conditions. The density of pavement materials is not fixed, but will change with changes in external conditions such as temperature and pressure. For example, at high temperatures, the material may expand, resulting in a decrease in density; at high pressures, the material may be compressed, resulting in an increase in density. Therefore, the formula that combines the density of solid particles, water, and air with their respective volume fractions can dynamically reflect the actual physical state of the material. It is worth noting that the bulk modulus The introduction of further expanded the analysis of the mechanical properties of materials. Bulk modulus is a parameter that describes the material's ability to resist volume changes and is usually used to characterize the rigidity of a material when it is compressed or expanded. The calculation formula for bulk modulus is obtained by taking the geometric weighted average of the bulk moduli of solids, liquids, and gases. This weighted average takes into account the proportion of each component in the material and its contribution to the overall mechanical properties. The physical meaning of bulk modulus is that when the water content of the material increases, the bulk modulus of water increases. This will reduce the overall rigidity of the material to a certain extent, especially in materials with high porosity, where this effect will be more significant. Similarly, the bulk modulus of air is Very low, so in high-porosity materials, the presence of air will significantly reduce the bulk modulus of the material, causing the material to deform more easily when under pressure. By analyzing the bulk modulus, the system can evaluate the response of the material under different stress conditions, thereby providing a basis for predicting the long-term performance of the material.

[0069] Specifically, the calculation of the moisture content and density of the pavement not only provides a snapshot of the current pavement state, but can also be used to predict the performance of the pavement under different environmental conditions. For example, during rainy seasons, the moisture content of the pavement material may increase, resulting in a decrease in density and a decrease in bulk modulus, which may make the pavement more susceptible to mechanical damage. By identifying these changes in advance, managers can take preventive measures such as increasing drainage or performing timely repairs, thereby extending the service life of the pavement. On the other hand, the relationship between density and moisture content can also be used to identify potential structural problems. For example, if the density is significantly lower than normal and the moisture content is higher than expected, this may indicate that there are voids or cracks inside the pavement material, which may require further inspection or repair. This ability to identify problems early is an important advantage of the present invention, especially in modern transportation infrastructure management, which can significantly improve maintenance efficiency and reduce operating costs. The present invention achieves accurate calculations of pavement moisture content and density through a multi-physics field coupling model, which not only relies on the electromagnetic properties of the material, but also involves theories from multiple fields such as mechanics and thermodynamics. Through these formulas, the system can deeply analyze the internal structure and physical state of the pavement, thereby providing strong support for optimization decisions. These calculation results can not only reflect the current state of the road surface, but also predict its possible changes in the future, helping managers make more scientific and effective decisions to ensure the long-term durability and safety of the road surface. This efficient and accurate analysis tool has important application value in the maintenance and management of modern transportation infrastructure.

[0070] Embodiment 8: The pavement quality evaluation and optimization part calculates the pavement quality evaluation index according to the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement. The formula is:

[0071] ;

[0072] in, The moisture content of the preset standard road surface; The density of the preset standard road surface; is the number of pavement layers; For the preset Standard nonlinear medium characteristic parameters of the pavement layer; The preset frequency is , the temperature is and pressure Nonlinear medium characteristic parameters of pavement materials at ; is the preset first weight; is the preset second weight; is the preset third weight; is the preset fourth weight; For the preset The weight of the standard nonlinear medium characteristic parameter of the pavement layer; when the quality evaluation index of the pavement is calculated If the quality exceeds the set threshold, it is judged that there is a problem with the road quality.

[0073] Specifically, the first part of the formula involves the moisture content of the pavement and preset standard moisture content The difference between the water content and the road surface quality is important. Water content is one of the important factors that determine the performance of pavement materials. Too high a water content may cause the material to soften, reduce its strength, and even cause pavement damage; while too low a water content may make the material too brittle and cause cracks to form. Therefore, the control of water content is crucial to the quality of the pavement. In the formula, this difference is multiplied by the corresponding weight , reflecting the relative importance of moisture content in pavement quality assessment. Depending on the type of pavement and the environmental conditions in which it is located, the weight setting may be different. For example, in humid and rainy areas, the change in moisture has a particularly significant impact on the pavement, so It may be set higher to emphasize its weight in the quality assessment. Next, the second part of the formula considers the road surface density With standard density Density is a key parameter for evaluating the structural integrity and load-bearing capacity of a pavement. Density deviation may indicate uneven distribution of pavement materials, the presence of voids, cracks, or insufficient compactness of the material, all of which will affect the durability and safety of the pavement. The formula calculates the absolute difference between the actual density and the standard density and combines it with a weight , quantifying the impact of density changes on pavement quality. If the density is significantly lower than the standard value, it may mean that there are serious problems with the pavement material, such as loose internal structure or unfilled voids, which will cause the pavement to settle or crack during use, thus affecting driving safety. The third part involves the nonlinear medium characteristic parameters of each layer in the pavement layered structure With the preset standard value This parameter reflects the electromagnetic response characteristics of the pavement material at different frequencies and can reveal the microstructural changes and physical state of the material. By analyzing the nonlinear dielectric properties of each layer of the pavement, the system can identify abnormal phenomena in the material, such as aging, damage, or changes in material composition. In the formula, The bias is multiplied by the weight and , the latter is the overall weight coefficient, and the former is the detailed weight for each layer. Through this weighting method, the system can more accurately evaluate the status of each layer of material and quantify its impact on the overall pavement quality. This detailed evaluation is very effective in identifying and diagnosing deep structural problems in the pavement, especially in some cases where the pavement layer structure is complex, such as highways or airport runways, where the requirements for pavement quality are extremely strict. The last part considers the deviation of the nonlinear medium characteristic parameters of the material under specific environmental conditions (such as specific frequencies, temperatures and pressures). This part further expands the analysis of the performance of materials in dynamic environments. Environmental factors such as temperature and pressure have a significant impact on the electromagnetic properties of pavement materials. Especially under extreme climatic conditions, the electromagnetic response of the material may change significantly. These changes are often early signs of material degradation or damage. Therefore, by analyzing these parameters, the system can warn of possible quality problems in advance, so that corresponding maintenance measures can be taken before the problem becomes serious. The whole formula comprehensively generates a quality assessment index by weighting the deviation values ​​of different physical parameters. The size of this index directly reflects the gap between the current quality of the road surface and the ideal state. The design goal is to quantify the overall health of the road surface through a single value, thereby providing a simple and easy-to-understand reference indicator for road managers. When the set quality threshold is exceeded, the system will sound an alarm, prompting managers to inspect or repair the road surface. This real-time assessment and early warning function greatly improves the efficiency of road surface management, helping managers intervene before problems become serious, thereby extending the service life of the road surface, reducing maintenance costs, and ensuring the safety of road traffic.

[0074] The present invention is described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. Road quality real-time evaluation and optimization system, characterized by: The system includes: a road surface penetration radar signal transmission and collection part, a signal processing part, a signal analysis part and a road surface quality evaluation and optimization part; the road surface penetration radar signal transmission and collection part is used to use a multi-spectrum ground penetration radar system to transmit a penetration radar signal to the target road surface, and then collect the reflection signal of the penetration radar signal; the signal processing part is used to perform high-order frequency domain deconvolution on the reflection signal to improve the resolution, and then apply a nonlinear adaptive signal enhancement algorithm to enhance it, and finally extract signal features; the signal analysis part is used to estimate the nonlinear medium of the road surface material based on the nonlinear inversion algorithm according to the extracted signal features Characteristic parameters; using multiple scattering theory and nonlinear least squares method to estimate the pavement layered structure, and obtain the thickness of each layer in the pavement layered structure; using a multi-physics field coupling model to calculate the moisture content and density of the pavement; the pavement quality evaluation and optimization part is used to calculate the quality evaluation index of the pavement according to the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement; respectively calculate the absolute values ​​of the differences between the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement and their corresponding reference values, and use the item corresponding to the maximum absolute value as the quality optimization item; The reflected signal is expressed using the following formula: ； in, For the frequency ,depth and time The reflected signal; For the The amplitude of a frequency component; For the The wave number of the frequency component; For the The attenuation coefficient of the frequency component; is the total number of frequency components; for Bessel functions of order; For the The parameters of the Bessel function; is the total order of Bessel function; For the The intensity of the relaxation process; For the The characteristic time of a relaxation process; For the The distribution parameters of the relaxation process; is the total number of relaxation processes; and All are integer subscript indices.

2. The road quality real-time evaluation and optimization system according to claim 1, characterized in that: The multi-spectrum ground penetrating radar system comprises at least five antennas with different center frequencies, and has a time resolution of less than or equal to 0.1 nanoseconds and a spatial resolution of less than or equal to 1 centimeter; Amplitude calibration of multi-spectrum ground penetrating radar systems using standard reflectors; A multi-spectrum ground penetration radar system is time calibrated using a uniform medium of known thickness; the multi-spectrum ground penetration radar system has a scanning interval of 5 cm, a time window of 100 nanoseconds, and a stacking number of 32 times / channel; the multi-spectrum ground penetration radar system is grid-scanned along a predetermined route of the target road surface, and each grid point is collected using all available frequencies to obtain a reflected signal.

3. The road quality real-time evaluation and optimization system according to claim 1, characterized in that: The signal processing part performs high-order frequency domain deconvolution on the reflected signal to improve the resolution through the following formula, and then applies a nonlinear adaptive signal enhancement algorithm to enhance it: ； in, is the enhanced signal; for Fourier transform of is the system transfer function; for The conjugate complex function of ; is the noise-to-signal ratio; is the regularization parameter; is the phase correction function; is the nonlinear enhancement factor; For the Harmonic enhancement coefficients; For the The center frequency of each harmonic; For the The shape parameters of the harmonics; is the total number of harmonics; the enhanced signal is analyzed using a convolutional neural network Perform feature extraction to obtain signal features.

4. The road quality real-time evaluation and optimization system according to claim 3, characterized in that: The signal analysis part estimates the nonlinear medium characteristic parameters of the pavement material based on the nonlinear inversion algorithm according to the extracted signal characteristics through the following formula: ； ； in, The frequency is , the temperature is and pressure Nonlinear medium characteristic parameters of pavement materials at ; is the high frequency limiting dielectric constant; For the Temperature-dependent dielectric strength of each relaxation process; For the The pressure-dependent characteristic time of each relaxation process; For the The temperature-dependent broadening parameter of the relaxation process; and are the third-order nonlinear polarizability and the fifth-order nonlinear polarizability; is the electric field strength; is the characteristic time under standard conditions; is the activation energy; is the activation volume; is the Boltzmann constant; Representation calculation The signal amplitude of Representation calculation signal frequency.

5. The road quality real-time evaluation and optimization system according to claim 4, characterized in that: The signal analysis part estimates the pavement layered structure using the multiple scattering theory and the nonlinear least squares method through the following formula to obtain the thickness of each layer in the pavement layered structure: ； ； ； in, For the Thickness of the layer; is the speed of light in a vacuum; For the Complex reflection coefficient at the top of the layer; For the The frequency of the layer is Nonlinear medium characteristic parameters of pavement materials; For the Layer and Fresnel reflection coefficient of layer interface; For the The frequency of the layer is Nonlinear medium characteristic parameters of pavement materials.

6. The road quality real-time evaluation and optimization system according to claim 5, characterized in that: The signal analysis part calculates the moisture content and density of the road surface using the multi-physics field coupling model through the following formula: ； ； ； ； in, is the moisture content of the road surface; is the nonlinear medium characteristic parameter of the pavement material; is the nonlinear medium characteristic parameter of solid particles; is the nonlinear medium characteristic parameter of water; is the porosity; is the geometric structure parameter; is the density of the road surface; is the density of solid particles; is the density of water; is the air density; is the bulk modulus of the mixed medium; , and They are the bulk modulus of solid, water and air respectively.

7. The road quality real-time evaluation and optimization system according to claim 6, characterized in that: The pavement quality evaluation and optimization part calculates the pavement quality evaluation index based on the nonlinear medium characteristic parameters of the pavement material, the thickness of each layer in the pavement layered structure, and the moisture content and density of the pavement. The formula is: ； in, The moisture content of the preset standard road surface; The density of the preset standard road surface; is the number of pavement layers; For the preset Standard nonlinear medium characteristic parameters of the pavement layer; The preset frequency is , the temperature is and pressure Nonlinear medium characteristic parameters of pavement materials at ; is the preset first weight; is the preset second weight; is the preset third weight; is the preset fourth weight; For the preset The weight of the standard nonlinear medium characteristic parameter of the pavement layer; when the quality evaluation index of the pavement is calculated If the quality exceeds the set threshold, it is judged that there is a problem with the road quality.

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