Asphalt pavement compactness dynamic detection method, system, equipment and medium

Abstract

The invention relates to the technical field of road engineering, in particular to an asphalt pavement compactness dynamic detection method, system and equipment and a medium, and the method comprises the following steps: acquiring radar amplitude data containing coordinate information at a set acquisition frequency; different frequency components in the radar amplitude data are separated through a frequency domain analysis method, and water noise reduction processing and vibration noise reduction processing are carried out; calculating the dielectric constant, the bulk density and the compaction degree through the radar amplitude data after noise reduction, performing stake mark conversion in combination with coordinate information, and calculating the rolling times of the road roller; and generating a rolling pass cloud picture, a compaction degree cloud picture and a trend chart of the compaction degree changing along with the rolling pass. According to the invention, signal enhancement or distortion caused by water mist spraying can be effectively reduced. And the influence of construction vibration on a detection result is reduced. Radar amplitude data is used for detecting the compaction process, the compaction state of the asphalt pavement is reflected in real time, then a rolling pass cloud picture, a compaction degree cloud picture and a trend chart are generated, and constructors can conveniently master the pavement quality in time.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and in particular to a method, system, device and medium for dynamically detecting the compactness of asphalt pavement. Background Art

[0002] Ground Penetrating Radar (GPR) is an effective means for detecting underground targets. It is a non-destructive detection technology. Compared with other conventional underground detection methods, it has the advantages of fast detection speed, continuous detection process, high resolution, convenient and flexible operation, low detection cost, etc., and is increasingly widely used in the field of engineering survey.

[0003] Currently, pavement detection methods based on GPR technology are widely used in engineering quality monitoring. However, in the actual construction environment, due to the influence of pavement conditions and construction equipment, the existing methods still have limitations in terms of data accuracy and real-time performance. For example, during the compactness detection of asphalt pavement by a roller, the water mist sprayed by the roller and the vibration noise will significantly interfere with the GPR signal, resulting in the deviation of the compactness measurement result from the actual value.

[0004] The information disclosed in this background art section is only intended to deepen the understanding of the overall background art of the present invention, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, system, device and medium for dynamically detecting the compactness of asphalt pavement, so as to effectively solve the problems in the background art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a method for dynamically detecting the compactness of asphalt pavement, including the following steps:

[0007] Obtain radar amplitude data containing coordinate information at a set acquisition frequency;

[0008] Through frequency domain analysis method, separate different frequency components in the radar amplitude data, and perform water noise reduction processing and vibration noise reduction processing;

[0009] Calculate the dielectric constant, bulk density and compactness through the noise-reduced radar amplitude data, perform station number conversion in combination with the coordinate information, and calculate the number of roller passes;

[0010] Generate a cloud map of the number of roller passes, a cloud map of compactness, and a trend map of the change of compactness with the number of roller passes.

[0011] Further, in the water noise reduction processing, the following steps are included:

[0012] Extract the surface reflection wave from the radar amplitude data;

[0013] Analyze the spectral characteristics of the surface reflected wave to determine the amplitude of the cut-off frequency wave;

[0014] Calculate a correction coefficient based on the amplitude of the cut-off frequency wave, and correct the radar amplitude data.

[0015] Furthermore, the step of analyzing the spectral characteristics of the surface reflected wave to determine the amplitude of the cut-off frequency wave includes:

[0016] Perform Fourier transform on the surface reflected wave to convert it from the time domain to the frequency domain;

[0017] Determine the wave amplitude value corresponding to the optimal cut-off frequency through cut-off frequency analysis.

[0018] Furthermore, the optimal cut-off frequency is 0.25 - 0.35 GHz.

[0019] Furthermore, the step of calculating the correction coefficient based on the amplitude of the cut-off frequency wave includes: dividing the wave amplitude value corresponding to the optimal cut-off frequency by the wave amplitude value of the set reference signal to obtain the correction coefficient.

[0020] Furthermore, the vibration noise reduction treatment includes the following steps:

[0021] Perform frequency domain analysis on the radar amplitude data after water noise reduction treatment to identify different frequency components therein;

[0022] Determine the noise frequency caused by the vibration of the roller according to the vibration characteristics of the roller;

[0023] Select the corresponding filter and apply it to the density distribution curve of the radar amplitude data for filtering.

[0024] Furthermore, the step of acquiring radar amplitude data containing coordinate information at a set acquisition frequency includes the following steps:

[0025] Set the acquisition frequency, acquire radar amplitude data, and perform preprocessing, and save the valid information to a CSV file in real time;

[0026] Adopt the serial port to USB method to receive GPS positioning data in real time and synchronously save it to the same CSV file as the radar amplitude data, so that each row of the CSV file contains the valid information of the radar and its corresponding GPS coordinates.

[0027] Furthermore, in the trend graph of the compaction degree changing with the number of rolling passes, trend prediction is performed through a density prediction model, and the density prediction model is:

[0028]

[0029] Where ε Ac is the dielectric constant of asphalt mixture, G se is the effective density of aggregate, G mm is the maximum theoretical density, P Am is the content of asphalt mortar, ε Am is the dielectric constant of asphalt mortar, ε S is the dielectric constant of aggregate, G mb is the bulk density of asphalt mixture.

[0030] The present invention also includes a dynamic detection system for the compactness of asphalt pavement, which uses the method as described above. The system includes:

[0031] An acquisition unit for acquiring radar amplitude data containing coordinate information at a set acquisition frequency;

[0032] A noise reduction unit for separating different frequency components in the radar amplitude data by frequency domain analysis method, and performing water noise reduction processing and vibration noise reduction processing;

[0033] A calculation unit for calculating the dielectric constant, bulk density and compactness through the noise-reduced radar amplitude data, performing station number conversion in combination with the coordinate information, and calculating the number of rolling passes of the roller;

[0034] A visual unit for generating a cloud map of the number of rolling passes, a cloud map of compactness and a trend map of the change of compactness with the number of rolling passes.

[0035] The present invention also includes a computer device, including a memory, a processor and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method as described above is implemented.

[0036] The present invention also includes a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method as described above is implemented.

[0037] The beneficial effects of the present invention are as follows: By setting the acquisition frequency to collect radar amplitude data with GPS coordinate information in real time, the accurate matching of road surface detection data and actual position is realized, providing a basis for subsequent data processing and station number conversion. By the frequency domain analysis method, different frequency components in the radar amplitude data are separated, and water noise reduction processing and vibration noise reduction processing are carried out, which can effectively reduce the signal enhancement or distortion caused by spraying water mist. And reduce the influence of construction vibration on the detection results. Using radar amplitude data to detect the compaction process, the compaction state of asphalt pavement is reflected in real time, including calculating key indexes such as dielectric constant, bulk density and compactness, and then generating a cloud map of the number of rolling passes, a cloud map of compactness and a trend map, which is convenient for construction personnel to timely master the road surface quality. Description of the Drawings

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is the flowchart of the method in Embodiment 1;

[0040] Figure 2 It is the structural schematic diagram of the system in Embodiment 1;

[0041] Figure 3 It is the flowchart of noise reduction in Embodiment 2

[0042] Figure 4 It is the diagram of the change in dielectric constant before and after noise reduction in Embodiment 2;

[0043] Figure 5 It is the flowchart of CSV file generation in Embodiment 2;

[0044] Figure 6 It is the flowchart of data upload and visualization in Embodiment 2;

[0045] Figure 7 It is the structural schematic diagram of the computer device of the present invention. Detailed implementation manners

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0047] Embodiment 1:

[0048] As Figure 1 shown: A dynamic detection method for the compactness of asphalt pavement includes the following steps:

[0049] Obtain radar amplitude data containing coordinate information at a set acquisition frequency;

[0050] Through frequency-domain analysis methods, separate different frequency components in the radar amplitude data, and perform water noise reduction processing and vibration noise reduction processing;

[0051] Calculate the dielectric constant, bulk density, and compactness through the noise-reduced radar amplitude data, perform station number conversion in combination with the coordinate information, and calculate the number of rolling passes of the roller;

[0052] Generate a cloud map of the number of rolling passes, a cloud map of compactness, and a trend map of the change in compactness with the number of rolling passes.

[0053] By setting the acquisition frequency, the radar amplitude data with GPS coordinate information is collected in real time, realizing the accurate matching of road surface detection data with the actual position, and providing a basis for subsequent data processing and stake number conversion. The radar amplitude data is used to detect the compaction process, and the compaction state of the asphalt pavement is reflected in real time, including calculating key indicators such as dielectric constant, bulk density, and compaction degree, and then generating cloud maps of rolling passes, compaction degree, and trend charts, which is convenient for construction personnel to timely grasp the road surface quality.

[0054] In this embodiment, in the water noise reduction treatment, the following steps are included:

[0055] Extract the surface reflection wave from the radar amplitude data;

[0056] Analyze the spectral characteristics of the surface reflection wave to determine the wave amplitude at the cut-off frequency;

[0057] Calculate the correction coefficient through the wave amplitude at the cut-off frequency and correct the radar amplitude data.

[0058] Among them, analyzing the spectral characteristics of the surface reflection wave to determine the wave amplitude at the cut-off frequency includes:

[0059] Perform Fourier transform on the surface reflection wave to convert it from the time domain to the frequency domain;

[0060] Through cut-off frequency analysis, determine the wave amplitude corresponding to the optimal cut-off frequency.

[0061] As an optimization of the above embodiment, the optimal cut-off frequency is 0.25 - 0.35 GHz.

[0062] By extracting the surface reflection wave, Fourier transform, and cut-off frequency analysis, determine the wave amplitude at the optimal cut-off frequency (0.25 - 0.35 GHz), calculate the correction coefficient, and thus perform water interference correction on the radar amplitude data. This step can effectively reduce the signal enhancement or distortion caused by spraying water mist.

[0063] Among them, calculating the correction coefficient through the wave amplitude at the cut-off frequency includes: dividing the wave amplitude corresponding to the optimal cut-off frequency by the wave amplitude of the set reference signal to obtain the correction coefficient.

[0064] As an optimization of the above embodiment, in the vibration noise reduction treatment, the following steps are included:

[0065] Perform frequency domain analysis on the radar amplitude data after water noise reduction treatment to identify different frequency components therein;

[0066] According to the vibration characteristics of the roller, determine the noise frequency caused by the vibration of the roller;

[0067] Select the corresponding filter, apply it to the density distribution curve of the radar amplitude data, and perform filtering processing on it.

[0068] Based on the vibration characteristics of the roller, identify and separate the noise caused by mechanical vibration in the frequency domain, and select the corresponding filter to process the density distribution curve of the radar amplitude data, so as to reduce the influence of construction vibration on the detection results.

[0069] In this embodiment, the radar amplitude data containing coordinate information is acquired at a set acquisition frequency, including the following steps:

[0070] Set the acquisition frequency, acquire the radar amplitude data, and perform preprocessing, and save the effective information to the CSV file in real time;

[0071] Adopt the serial port to USB method to receive GPS positioning data in real time, and synchronously save it to the same CSV file as the radar amplitude data, so that each row of the CSV file contains the effective information of the radar and its corresponding GPS coordinates.

[0072] Further calculate the data after noise reduction processing, extract indicators such as dielectric constant, bulk density, and compaction degree, and use the GPS coordinate information to complete the calculation of the stake number conversion and the number of rolling passes, so as to realize the comprehensive and accurate evaluation of the compaction state.

[0073] As the preference of the above embodiment, in the trend chart of the compaction degree changing with the number of rolling passes, perform trend prediction through the density prediction model, and the density prediction model is:

[0074]

[0075] In the formula, ε AC is the dielectric constant of the asphalt mixture, G se is the effective density of the aggregate, G mm is the maximum theoretical density, P Am is the asphalt mortar content, ε Am is the dielectric constant of the asphalt mortar, ε S is the dielectric constant of the aggregate, G mb is the bulk density of the asphalt mixture.

[0076] By real-time collecting the radar amplitude data with GPS coordinate information and using frequency domain analysis to perform noise reduction processing on water and vibration noise, the compaction degree of the asphalt pavement can be accurately calculated, and intuitive graphic data can be generated. Its main function is to monitor the construction quality in real time and provide scientific and accurate data support for the construction process. The advantages are reflected in aspects such as real-time, high precision, automation, data visualization, and adaptability to complex construction environments, which can significantly improve the efficiency and quality of compaction degree detection and provide reliable guarantee for engineering construction.

[0077] As Figure 2 shown, this embodiment further includes a dynamic detection system for asphalt pavement compaction degree. Using the method as described above, the system includes:

[0078] An acquisition unit for obtaining radar amplitude data containing coordinate information at a set acquisition frequency;

[0079] A noise reduction unit for separating different frequency components in the radar amplitude data through frequency domain analysis methods and performing water noise reduction processing and vibration noise reduction processing;

[0080] A calculation unit for calculating the dielectric constant, bulk density, and compaction degree through the noise-reduced radar amplitude data, performing station number conversion in combination with the coordinate information, and calculating the number of roller compaction passes;

[0081] A visual unit for generating a cloud map of the number of compaction passes, a cloud map of the compaction degree, and a trend map of the change of the compaction degree with the number of compaction passes.

[0082] Embodiment 2:

[0083] This embodiment provides a dynamic detection system and method for asphalt pavement compaction degree based on ground penetrating radar to solve the problem of reduced detection accuracy caused by complex construction environments in the prior art. The technical solution of this embodiment includes: (1) airborne detection equipment: real-time collection of pavement compaction degree data during construction through a ground penetrating radar device mounted on a roller. (2) Noise reduction algorithm: including a frequency domain analysis noise reduction method for surface water and a digital filtering noise reduction method for roller vibration. (3) Detection system: combining with a GPS positioning system to perform real-time processing of radar amplitude data and generating construction guidance information such as a cloud map of the number of compaction passes and a cloud map of the compaction degree.

[0084] Airborne detection equipment:

[0085] Due to the structural and operating condition limitations of the roller, it is impossible to directly install a ranging wheel. Therefore, this embodiment uses a GPS positioning device for data collection. By integrating a GPS module on the radar device, the airborne detection equipment is enabled to collect data in time mode. In this mode, the radar device obtains radar amplitude data containing coordinate information at a certain acquisition frequency, and the density of the data depends on the driving speed of the roller. Specifically, the antenna frequency of the radar device is 1.6G, the acquisition frequency is 32Hz, and the acquisition frequency of the GPS device is 1Hz to ensure the accuracy and efficiency of data collection. Based on the geographical coordinates recorded by the GPS and combined with the driving path of the roller, the data collected by the radar can be matched with the station number position of the actual road section, realizing the precise correspondence between the data and the actual road section.

[0086] To adapt to the construction site conditions, a dedicated mounting scheme is designed in this embodiment to ensure the stable installation of the radar device on the roller and the high efficiency of data collection. The radar device is installed at the connection of the roller body. An L-shaped triangular support frame made of lightweight and strong aluminum alloy material is used to fix the device. A transparent acrylic protection box is installed at the bottom of the support for placing the radar antenna. Two rows of screw holes are designed on both sides of the support to adjust the height of the radar device, ensuring that the distance between the radar antenna and the road surface remains at about 23 cm on different models of rollers. In addition, the GPS device and the radar antenna are installed at the same horizontal position and at different heights to avoid spatial displacement errors between the two, thereby improving the synchronization accuracy of the positioning data and the radar detection data and ensuring the accuracy of the data positioning information.

[0087] Noise reduction algorithm:

[0088] As Figure 3 shown, in this embodiment, a scheme for vibration noise reduction through digital filtering technology is proposed for the noise interference of the airborne ground penetrating radar device under the high-frequency vibration conditions of the roller, and the dielectric constant of the asphalt layer is measured by combining the surface reflection method, ensuring the accuracy and reliability of the compaction degree data.

[0089] Since the ground penetrating radar device is mounted on the roller, the high-frequency vibration generated by the device during the rolling process will cause significant interference to the radar antenna and the overall system. In particular, the operating vibration frequency and rolling vibration frequency of the roller will introduce regular high-frequency noise, resulting in fluctuations in the measured values of the dielectric constant and density in the radar signal, and even masking the true changes in the density curve. Therefore, in this embodiment, a frequency domain analysis method is used to separate different frequency components in the radar density distribution curve, identify the noise components caused by device vibration, and remove these noises through digital filtering technology.

[0090] Specifically, the processing process of the airborne ground penetrating radar amplitude data in this embodiment includes two main steps: water noise reduction processing and vibration noise reduction processing.

[0091] 1. Road surface water noise reduction processing

[0092] To eliminate the influence of the residual water mist sprayed by the steel wheel roller on the radar amplitude data, road surface water noise reduction processing is first carried out, and the specific steps are as follows:

[0093] ① Surface reflection wave extraction: Extract the surface reflection wave from the airborne ground penetrating radar amplitude data, which is the basis for subsequent water noise reduction processing. Precise positioning of the position of the surface reflection wave helps to identify the signal fluctuations caused by moisture.

[0094] ② Fourier transform: Perform Fourier transform on the extracted surface reflection wave to convert it from the time domain to the frequency domain for analyzing its spectral characteristics.

[0095] ③Determination of cut-off frequency amplitude: In frequency-domain analysis, through cut-off frequency analysis, the amplitude value corresponding to a specific cut-off frequency is determined, and the correction coefficient is calculated. The optimal cut-off frequency range is 0.25 - 0.35 GHz.

[0096] ④Calculation of correction coefficient: The amplitude measured by using the amplitude data of the airborne radar is divided by the amplitude of the reference signal to obtain the correction coefficient for road surface water noise reduction.

[0097] ⑤Signal correction: The signal measured on the dry road surface is corrected by using the correction coefficient to obtain the radar signal after water noise reduction processing.

[0098] 2. Vibration noise reduction processing

[0099] After the water noise reduction processing is completed, vibration noise reduction processing is further carried out to reduce the influence of the noise caused by the vibration of the roller on the data. The specific steps are as follows:

[0100] ①Frequency-domain analysis: Through frequency-domain analysis, different frequency components in the density distribution curve of the ground penetrating radar are identified, and the noise components caused by the vibration of the roller are analyzed.

[0101] ②Filter selection: According to the results of frequency-domain analysis, a finite impulse response filter (FIR) based on the window function is selected for vibration noise elimination. By comparing the characteristics of FIR and the infinite impulse response filter (IIR), the most suitable filter is selected.

[0102] ③Filtering processing: The selected filter is applied to the density distribution curve for filtering processing, so as to remove the high-frequency noise introduced by the vibration of the roller, and finally obtain a more stable and accurate radar signal.

[0103] Figure 4 The dielectric constant distribution curves before and after vibration noise reduction are shown. By comparing different cut-off frequencies, it is found that a lower cut-off frequency (2.5 Hz) has better noise reduction effects on a certain section of the road and the entire route, significantly reducing the high-frequency variation of the dielectric constant caused by the vibration of the roller. After processing, the average value of the dielectric constant is reduced from 9.53 to 5.67, significantly improving the stability and accuracy of the data.

[0104] Detection system:

[0105] In this embodiment, an integrated system platform is proposed for the data acquisition and real-time analysis of the airborne ground penetrating radar to achieve real-time transmission, processing, and visual monitoring of the data during the roller compaction operation. This embodiment uses the dual acquisition of GPS positioning data and radar amplitude data to provide technical solutions for compaction quality monitoring, uniformity analysis, and construction process optimization.

[0106] 1. Real-time Data Transmission and Processing

[0107] The electromagnetic wave data collected by the airborne ground penetrating radar can be transmitted to the system platform in real time through the communication protocol. The system background processes the road surface water and the vibration noise reduction of the roller according to the radar amplitude data of each measuring point, calculates the dielectric constant, the bulk density, and the compaction degree, and performs station number conversion in combination with the GPS coordinate data, and finally calculates the number of roller compaction passes. Based on these processed data, the system will generate cloud maps of the number of compaction passes, cloud maps of the compaction degree, and trend charts of the compaction degree changing with the number of compaction passes. These graphs can provide guidance for the compaction work of the roller to ensure the compaction quality and overall uniformity.

[0108] 2. Data Preprocessing Module

[0109] The data preprocessing module extracts effective information from each electromagnetic wave signal to reduce the data volume and improve the analysis accuracy and speed. The specific steps are as follows:

[0110] Sampling point screening: By screening the sampling points, the maximum amplitudes of the direct wave and the reflected wave and their corresponding times are extracted.

[0111] Data storage: The extracted effective information will be saved to the CSV file in real time for subsequent analysis and report generation.

[0112] Before officially starting data collection, the user can set the preprocessing parameters and adjust the waveform characteristic parameters of the project information and sampling point screening. During the collection process, the radar amplitude data analysis software will automatically preprocess the data and save the extracted effective information to the CSV file in real time.

[0113] 3. Real-time GPS Data Synchronization

[0114] As Figure 5 shown, in order to ensure the accurate matching of the ground penetrating radar amplitude data and the geographical location information, the system uses the serial port to USB method to receive GPS positioning data in real time and synchronously save it to the same CSV file as the radar amplitude data. In this way, each row of the CSV file contains the effective detection data of the radar and its corresponding GPS coordinates.

[0115] After each detection task is completed, the system will automatically generate a complete CSV file for subsequent data analysis and report generation. In this way, it is ensured that each piece of effective data can be accurately uploaded and precisely corresponding to the geographical location.

[0116] 4. Real-time Data Upload and Visualization Display

[0117] To achieve real-time upload of data in the system, the system reads the CSV files generated by the computer terminal in real time through a general data acquisition platform. The platform adopts a timed polling mechanism to regularly scan the specified file directory in the laptop, automatically query and read the content of the latest generated CSV file, so as to ensure that each piece of valid data can be uploaded to the system in a timely manner.

[0118] The position trajectory of the roller is recorded in real time by a GPS device to form a trajectory data set containing information such as time, coordinates, and speed. Based on these trajectory data, the system will generate a cloud map containing compaction information, and each evaluation unit shows the cumulative number of times or coverage degree that the roller has passed. These cloud maps can help construction personnel grasp the working status of the roller in real time and verify it in combination with the number of compaction passes calculated manually on site.

[0119] 5. Compaction Degree and Uniformity Analysis Function

[0120] As Figure 6 shown, in order to further improve the compaction quality monitoring and the optimization of construction technology, in this embodiment, a density prediction model is embedded in the system, and a special compaction degree analysis and uniformity analysis function interface is designed. This interface integrates the density prediction model, supports showing the law of the change of compaction degree with the number of compaction passes, and generates cloud maps of the number of compaction passes and compaction degree. Through this function, the compaction quality and uniformity in the construction process can be accurately monitored, providing strong support for the optimization of construction technology.

[0121] The density prediction model is as follows:

[0122]

[0123] Table 1 List of Key Parameters of the Prediction Model

[0124] Key parameters of the prediction model Numerical value <![CDATA[Dielectric constant ε of asphalt mixture AC > Acquisition item <![CDATA[Effective density G of aggregate se > Parameter input item <![CDATA[Maximum theoretical density G mm > Parameter input item <![CDATA[Asphalt mortar content P Am > Parameter input item <![CDATA[Dielectric constant ε of asphalt mortar Am > Parameter input item <![CDATA[Aggregate dielectric constant ε S > Parameter input item <![CDATA[Gross volume density G of asphalt mixture mb > Output item

[0125] This embodiment provides an efficient and accurate compaction quality monitoring solution by integrating technologies such as data acquisition, noise reduction processing, density prediction, trajectory generation, and real-time analysis, which is of great significance for improving construction quality and optimizing construction technology.

[0126] Please refer to Figure 7 the structural schematic diagram of the computer device provided by the embodiment of the present application shown. A computer device 400 provided by an embodiment of the present application includes: a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, it executes the method as above.

[0127] An embodiment of the present application also provides a storage medium 430, on which a computer program is stored. When the computer program is run by a processor 410, the above method is executed.

[0128] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disk.

[0129] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0130] In the present invention, unless otherwise clearly specified and limited, the terms "mounted", "connected", "connected to", "fixed" and other terms should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0131] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0132] Any process or method description represented in a flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a manner other than shown or discussed, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0133] The logic and / or steps represented in a flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0134] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following technologies well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0135] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0136] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A dynamic detection method for the compaction degree of asphalt pavement, characterized in that, It includes the following steps: Obtain radar amplitude data containing coordinate information at a set acquisition frequency; Through frequency-domain analysis method, separate different frequency components in the radar amplitude data, and perform water noise reduction processing and vibration noise reduction processing; Calculate the dielectric constant, bulk density and compaction degree through the noise-reduced radar amplitude data, perform station number conversion in combination with the coordinate information, and calculate the number of rolling passes of the roller; Generate a cloud map of the number of rolling passes, a cloud map of compaction degree and a trend chart of the change of compaction degree with the number of rolling passes.

2. The dynamic detection method for the compactness of asphalt pavement according to claim 1, wherein In the water noise reduction processing, it includes the following steps: Extract the surface reflection wave from the radar amplitude data; Analyze the spectral characteristics of the surface reflection wave to determine the amplitude of the cut-off frequency wave; Calculate the correction coefficient through the amplitude of the cut-off frequency wave, and correct the radar amplitude data.

3. The dynamic detection method for the compactness of asphalt pavement according to claim 2, characterized in that, The analyzing the spectral characteristics of the surface reflection wave to determine the amplitude of the cut-off frequency wave includes: Perform Fourier transform on the surface reflection wave to convert it from the time domain to the frequency domain; Through cut-off frequency analysis, determine the wave amplitude corresponding to the optimal cut-off frequency.

4. The dynamic detection method for the compactness of an asphalt pavement according to claim 3, characterized in that The optimal cut-off frequency is 0.25 - 0.35 GHz.

5. The dynamic detection method for the compactness of asphalt pavement according to claim 3, characterized in that The calculating the correction coefficient through the amplitude of the cut-off frequency wave includes: dividing the wave amplitude corresponding to the optimal cut-off frequency by the wave amplitude of the set reference signal to obtain the correction coefficient.

6. The dynamic detection method for the compactness of an asphalt pavement according to claim 1, characterized in that, In the vibration noise reduction processing, it includes the following steps: Perform frequency-domain analysis on the radar amplitude data after water noise reduction processing to identify different frequency components therein; Determine the noise frequency caused by the vibration of the roller according to the vibration characteristics of the roller; Select the corresponding filter and apply it to the density distribution curve of the radar amplitude data for filtering processing.

7. The dynamic detection method for the compactness of asphalt pavement according to claim 1, wherein The obtaining radar amplitude data containing coordinate information at a set acquisition frequency includes the following steps: Set the acquisition frequency, collect radar amplitude data, and perform preprocessing, and save the effective information to the CSV file in real time; Adopt the serial port to USB method to receive GPS positioning data in real time and synchronously save it to the same CSV file as the radar amplitude data, so that each row of the CSV file contains the effective information of the radar and its corresponding GPS coordinates.

8. The dynamic detection method for the compactness of asphalt pavement according to claim 1, characterized in that In the trend chart of the change of compaction degree with the number of rolling passes, trend prediction is performed through a density prediction model, and the density prediction model is: where ε AC is the dielectric constant of asphalt mixture, G se is the effective density of aggregates, G mm is the maximum theoretical density, P Am is the content of asphalt mortar, ε Am is the dielectric constant of asphalt mortar, ε S is the dielectric constant of aggregates, G mb is the bulk density of asphalt mixture.

9. A dynamic detection system for the compaction degree of asphalt pavement, characterized in that, Using the method according to any one of claims 1 to 8, the system includes: An acquisition unit for obtaining radar amplitude data containing coordinate information at a set acquisition frequency; A noise reduction unit for separating different frequency components in the radar amplitude data through a frequency-domain analysis method and performing water noise reduction processing and vibration noise reduction processing; A calculation unit for calculating the dielectric constant, bulk density and compaction degree through the noise-reduced radar amplitude data, performing station number conversion in combination with the coordinate information, and calculating the number of rolling passes of the roller; A visual unit for generating a cloud map of the number of rolling passes, a cloud map of compaction degree and a trend chart of the change of compaction degree with the number of rolling passes.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that When the processor executes the computer program, it implements the method according to any one of claims 1 - 8.

11. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1-8.

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