A method for establishing a digital model of a road roller vibration system

Abstract

The present invention relates to the technical field of non-road mobile machinery dynamic models, and in particular to a method for establishing a digital model of a vibration system of a road roller. The method comprises the following steps: obtaining an initial data set of vibration characteristics of pavement materials; performing multi-physical field coupling analysis on the initial data set of vibration characteristics of pavement materials to obtain pavement material response characteristic data; performing dynamic system modeling on the vibration system of the road roller based on the pavement material response characteristic data to obtain an initial digital model of the vibration system; performing dynamic feature extraction on the initial digital model of the vibration system to obtain dynamic parameter mapping data of the vibration system; performing parameter calibration on the initial digital model of the vibration system based on the dynamic parameter mapping data of the vibration system to obtain a digital model of the vibration system. The intelligent vibration system model of the present invention can accurately capture the dynamic characteristics of the soil and achieve the best compaction effect.

Description

Technical Field

[0001] The invention relates to the technical field of non-road mobile machinery dynamics models, and in particular to a method for establishing a digital model of a vibration system of a road roller. Background Art

[0002] In modern road construction, the performance of road rollers, as important compaction equipment, directly affects the quality and service life of the road surface. The vibration system of the road roller is one of the key technologies to achieve effective compaction. It uses the vibration of the vibrating wheel to make the road surface material reach the required density. However, the existing vibration system of the road roller has some technical difficulties in practical application, which are particularly prominent in specific application scenarios.

[0003] First, existing vibration systems for road rollers often lack accurate dynamic simulation capabilities. In changing construction environments, such as road surfaces with different materials or under different climatic conditions, the performance of the vibration system will vary. Traditional simulation methods often rely on static parameters and cannot respond to these changes in real time, resulting in unstable compaction results. For example, when compacting soft soil, the frequency and amplitude of the vibration system need to be adjusted according to the dynamic characteristics of the soil to avoid over-compaction or under-compaction. However, existing vibration system models cannot accurately capture these dynamic characteristics, making it impossible to achieve optimal compaction results. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a method for establishing a digital model of a roller vibration system to solve at least one of the above technical problems.

[0005] To achieve the above object, a method for establishing a digital model of a roller vibration system comprises the following steps:

[0006] Step S1: obtaining an initial pavement material vibration characteristic data set; performing multi-physics field coupling analysis on the initial pavement material vibration characteristic data set to obtain pavement material response characteristic data;

[0007] Step S2: Based on the pavement material response characteristic data, dynamic system modeling is performed on the vibration system of the road roller to obtain an initial digital model of the vibration system; dynamic characteristics are extracted from the initial digital model of the vibration system to obtain dynamic parameter mapping data of the vibration system; based on the dynamic parameter mapping data of the vibration system, parameter calibration is performed on the initial digital model of the vibration system to obtain a digital model of the vibration system;

[0008] Step S3: constructing a control framework for the digital model of the vibration system to obtain a vibration system parameter control framework; controlling the compaction process of the roller vibration system based on the digital model of the vibration system and the vibration system parameter control framework to obtain a vibration system control model;

[0009] Step S4: numerically simulate the compaction process of the target road surface material based on the vibration system control model to obtain a compaction process simulation model; quantitatively evaluate the compaction effect of the target road surface based on the compaction process simulation model to obtain a compaction effect evaluation index set; and modify the compaction process simulation model according to the compaction effect evaluation index set to obtain a modified compaction process numerical model;

[0010] Step S5: construct a performance monitoring framework for the modified compaction process numerical model to obtain a vibration system performance monitoring framework; predict the state of the roller vibration system throughout its life cycle based on the vibration system performance monitoring framework to obtain a system operation status warning diagram; adjust the vibration system digital model according to the system operation status warning diagram to obtain an intelligent roller vibration system digital model.

[0011] The present invention can accurately capture the dynamic response characteristics of the pavement material by acquiring the vibration characteristic data set of the initial pavement material and performing multi-physical field coupling analysis. This enables the vibration system of the roller to be optimized and adjusted according to the characteristics of the actual pavement material, thereby achieving a more accurate and stable compaction effect in a variable construction environment. By modeling the dynamic system and extracting the dynamic characteristics of the vibration system, and calibrating the parameters based on the dynamic parameter mapping data, an accurate digital model of the vibration system can be constructed. This helps to improve the performance of the vibration system and make it better adaptable to different construction conditions and pavement materials. By constructing a vibration system parameter control framework and a control model, it is possible to achieve accurate control of the vibration system of the roller during the compaction process. This improvement in control capability helps to improve compaction efficiency and reduce energy consumption. By numerically simulating the compaction process of the target pavement material and quantitatively evaluating the compaction effect based on the simulation model, an accurate set of compaction effect evaluation indicators can be provided. This helps the construction team to better understand the compaction process and adjust the construction strategy in time to achieve the best compaction effect. By correcting the compaction process simulation model according to the compaction effect evaluation indicator set, the compaction process can be continuously optimized to ensure the consistency and reliability of the compaction quality. This continuous optimization capability helps reduce maintenance costs and increase the service life of roads. By building a vibration system performance monitoring framework, the vibration system of the roller can be predicted and monitored throughout its life cycle. This helps to detect and prevent potential equipment failures in a timely manner, reduce construction risks, and improve construction safety. By adjusting and optimizing the digital model of the vibration system, the construction of an intelligent digital model of the vibration system of the roller can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings:

[0013] Figure 1A schematic flow chart of steps of a method for establishing a digital model of a vibration system of a road roller according to an embodiment is shown.

[0014] Figure 2 A detailed flowchart diagram of step S14 of an embodiment is shown. DETAILED DESCRIPTION

[0015] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0017] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0018] To achieve this, please refer to Figures 1 to 2 The present invention provides a method for establishing a digital model of a roller vibration system, comprising the following steps:

[0019] Step S1: obtaining an initial pavement material vibration characteristic data set; performing multi-physics field coupling analysis on the initial pavement material vibration characteristic data set to obtain pavement material response characteristic data;

[0020] Step S2: Based on the pavement material response characteristic data, dynamic system modeling is performed on the vibration system of the road roller to obtain an initial digital model of the vibration system; dynamic characteristics are extracted from the initial digital model of the vibration system to obtain dynamic parameter mapping data of the vibration system; based on the dynamic parameter mapping data of the vibration system, parameter calibration is performed on the initial digital model of the vibration system to obtain a digital model of the vibration system;

[0021] Step S3: constructing a control framework for the digital model of the vibration system to obtain a vibration system parameter control framework; controlling the compaction process of the roller vibration system based on the digital model of the vibration system and the vibration system parameter control framework to obtain a vibration system control model;

[0022] Step S4: numerically simulate the compaction process of the target road surface material based on the vibration system control model to obtain a compaction process simulation model; quantitatively evaluate the compaction effect of the target road surface based on the compaction process simulation model to obtain a compaction effect evaluation index set; and modify the compaction process simulation model according to the compaction effect evaluation index set to obtain a modified compaction process numerical model;

[0023] Step S5: construct a performance monitoring framework for the modified compaction process numerical model to obtain a vibration system performance monitoring framework; predict the state of the roller vibration system throughout its life cycle based on the vibration system performance monitoring framework to obtain a system operation status warning diagram; adjust the vibration system digital model according to the system operation status warning diagram to obtain an intelligent roller vibration system digital model.

[0024] In this embodiment, a sensor and a data acquisition system (such as a National Instruments data acquisition card) are used to obtain a vibration characteristic data set of the initial pavement material. These data include the response of the pavement material under different vibration conditions, such as acceleration, displacement and stress. The collected data are analyzed using multi-physics coupling analysis software (such as COMSOL Multiphysics) to obtain the response characteristic data of the pavement material. Based on the response characteristic data, the vibration system of the roller is modeled by a dynamic system using a system modeling software (such as MATLAB / Simulink) to create an initial digital model of the vibration system. The model includes detailed parameters of the vibration wheel, the transmission system and the control system. The initial digital model is subjected to dynamic feature extraction, and the dynamic parameter mapping data of the vibration system is obtained using signal processing technology (such as fast Fourier transform FFT). The initial digital model of the vibration system is parameter calibrated using the dynamic parameter mapping data. The calibrated model is used to construct a control framework, and the control theory (such as PID control) and simulation tools (such as Simulink) are used to obtain the vibration system parameter control framework. Based on the digital model of the vibration system and the parameter control framework, compaction process control is performed to obtain a vibration system control model. The compaction process of the target pavement material is numerically simulated using numerical simulation software (such as ABAQUS) based on the vibration system control model to obtain a compaction process simulation model. This model is used to predict the compaction effect and quantitatively evaluate the compaction effect to obtain a set of compaction effect evaluation indicators. According to the compaction effect evaluation indicator set, the compaction process simulation model is modified to obtain a modified compaction process numerical model. A performance monitoring framework is constructed, and the performance of the modified compaction process numerical model is monitored using data acquisition and analysis tools (such as Python data analysis library) to obtain a vibration system performance monitoring framework. Based on the vibration system performance monitoring framework, the vibration system of the roller is predicted throughout its life cycle to obtain a system operation status warning diagram. Finally, according to the system operation status warning diagram, the vibration system digital model is adjusted to obtain an intelligent roller vibration system digital model.

[0025] Preferably, step S1 comprises the following steps:

[0026] Step S11: collecting vibration response signals of target pavement materials to obtain a pavement material vibration response signal set;

[0027] Specifically, a suitable sensor can be selected, such as an accelerometer (model: B&K 4393), which is suitable for capturing the dynamic response of pavement materials under vibration. The sensor is installed on the surface of the pavement material. Next, the sensor is connected using a data acquisition card (model: National Instruments NI USB-4431), which can provide 24-bit analog-to-digital conversion and support a sampling rate of up to 102.4 kS / s. During the acquisition process, the acquisition parameters such as sampling frequency and acquisition time are set through the control software (e.g., LabVIEW). For example, for asphalt pavement, the sampling frequency is set to 1000 Hz and the acquisition time is 10 seconds, while for concrete pavement, a higher sampling frequency and a longer acquisition time are required. After the acquisition is completed, a set of vibration response signals of the pavement material is obtained.

[0028] Step S12: performing spectrum denoising on the pavement material vibration response signal set to obtain a purified pavement material vibration response signal set;

[0029] Specifically, the signal processing software (e.g., MATLAB) can be used to import the pavement material vibration response signal set. The short-time Fourier transform (STFT) combined with the wavelet threshold denoising method is selected. In MATLAB, the signal is first subjected to STFT to convert the time domain signal into a frequency domain signal. Next, the appropriate wavelet basis function (e.g., Daubechies wavelet) and the number of decomposition layers (e.g., 3 layers) are selected to perform wavelet transform on the signal. Based on the wavelet transform, a threshold is set (e.g., using the Donoho-Johnstone's universal threshold determination method) to identify and suppress noise. The threshold is set to 3 times the standard deviation of the signal to remove those coefficients below the threshold, which are considered to be noise. After denoising, the signal is subjected to an inverse wavelet transform to restore the time domain form of the signal. Finally, the denoised signal is checked through a visualization tool to obtain a purified pavement material vibration response signal set.

[0030] Step S13: extracting microscopic structural features from the vibration response signal set of the purified pavement material to obtain a microscopic feature set of the pavement material vibration response;

[0031] Specifically, Fourier transform (FFT) can be used to convert the time domain signal of the vibration response signal set of the purified pavement material into a frequency domain signal. In MATLAB, the fft function is applied to each signal set, and appropriate parameters such as sampling frequency and signal length are set. Next, the peak analysis method is used to identify local maxima and minima in the signal, which are related to the microstructural characteristics of the pavement material. A threshold, such as 3 times the standard deviation of the signal mean, is set to identify significant peaks. The peaks in the signal are found by the findpeaks function, and their positions and amplitudes are extracted. Wavelet transform is also used to further analyze the local characteristics of the signal. The signal is wavelet decomposed by selecting an appropriate wavelet basis (e.g., 'db4', i.e., the fourth order of Daubechies wavelet) and the number of decomposition layers (e.g., 5 layers). By analyzing the wavelet coefficients of different layers, the vibration characteristics of the pavement material at different scales are captured. Finally, the features extracted from FFT and wavelet transform are integrated to form a microscopic feature set of the vibration response of the pavement material. These features include the position and amplitude of the peak, and the statistical characteristics of the wavelet coefficients (such as mean, standard deviation, energy distribution, etc.).

[0032] Step S14: performing frequency-strain correlation analysis on the material vibration response micro-feature set to obtain pavement material vibration sensitivity mapping data;

[0033] Specifically, please refer to the sub-steps of step S14 for the detailed implementation process of this embodiment.

[0034] Step S15: constructing a pavement material vibration characteristic classification standard based on the pavement material vibration sensitivity mapping data to obtain a pavement material vibration characteristic classification standard;

[0035] Specifically, statistical analysis software (such as R language or Python with pandas and scikit-learn libraries) can be used to process material vibration sensitivity mapping data. These data include simulated and experimental strain values ​​at different frequencies, such as elastic modulus, Poisson's ratio, etc. For example, it is found that at some frequency points, the difference between the simulated and experimental data exceeds a preset threshold (such as 10%), and these data points are marked as outliers and removed. Select the K-means clustering algorithm to classify the data, which can divide the data into K categories based on the characteristics of the data. Determine the optimal number of categories K, which can be achieved by a variety of methods, such as the Elbow Method or the Silhouette Coefficient. For example, through the elbow rule analysis, it is found that when K=3, the sum of squared errors of the clustering begins to stabilize, so K=3 is selected as the optimal number of categories. Use the K-means algorithm to cluster the data and obtain the center point and boundary of each category. For example, three categories are obtained, each corresponding to a different level of vibration sensitivity. Category 1 contains data points with low strain response, category 2 contains data points with medium strain response, and category 3 contains data points with high strain response. Finally, the classification standard of pavement material vibration characteristics is constructed based on the clustering results. For example, category 1 is defined as "low vibration sensitivity", category 2 is defined as "medium vibration sensitivity", and category 3 is defined as "high vibration sensitivity". These classification standards can be used to guide actual pavement design and construction. For example, for pavement materials with "high vibration sensitivity", additional compaction measures are taken to ensure the stability and durability of the pavement. In this way, the classification standard of pavement material vibration characteristics can be obtained.

[0036] Step S16: classify the pavement material vibration response micro-feature set according to the pavement material vibration characteristic classification standard to obtain a classified pavement material vibration characteristic data set, and perform multi-physical field coupling analysis on the classified pavement material vibration characteristic data set to obtain pavement material response characteristic data.

[0037] Specifically, please refer to the sub-steps of step S16 for the detailed implementation process of this embodiment.

[0038] The present invention improves the accuracy and reliability of the data by collecting the vibration response signal of the target pavement material and performing spectrum denoising. By extracting the microstructural features of the purified signal set, the microscopic characteristics of the pavement material are deeply analyzed, providing detailed information for understanding the behavior of the material under different vibration conditions. The ability to analyze the vibration sensitivity of pavement materials is enhanced through frequency-strain correlation analysis. The pavement material vibration characteristic classification standard constructed based on vibration sensitivity mapping data realizes the systematic classification of material characteristics and provides customized control strategies for the compaction of different material types. The adaptability and generalization ability of the model are improved through multi-physical field coupling analysis, so that the digital model of the vibration system can better cope with the diversity and complexity in actual construction.

[0039] Preferably, step S14 comprises the following steps:

[0040] Step S141: vectorizing the material vibration response microscopic feature set to obtain a pavement material vibration feature vector set;

[0041] Specifically, data analysis software (such as Python with NumPy and Pandas libraries) can be used to process the microscopic feature set of material vibration response. Assume that the microscopic feature set of material vibration response includes features such as peak frequency, peak amplitude, and energy distribution after wavelet transformation. Create a feature vector for each pavement material, which contains the numerical values ​​of these features. For example, for a specific asphalt pavement sample, the following eigenvalues ​​are obtained: the peak frequencies are 5Hz, 10Hz, and 15Hz, and the corresponding peak amplitudes are 0.8, 1.2, and 0.9, respectively, and the energy distribution after wavelet transformation is {0.1, 0.3, 0.2, 0.1, 0.3}. Combine these eigenvalues ​​into a vector: [5Hz, 0.8, 10Hz, 1.2, 15Hz, 0.9, 0.1, 0.3, 0.2, 0.1,0.3]. This vector is the vibration feature vector of the asphalt pavement sample. For a batch of pavement material samples, the above process is repeated to construct a vibration feature vector for each sample, and these vectors are combined to form a pavement material vibration feature vector set.

[0042] Step S142: Performing Fourier-wavelet composite transformation on the vibration characteristic vector set of the pavement material to obtain the material microscopic vibration frequency distribution data;

[0043] Specifically, a signal processing software (such as MATLAB) can be used to perform a Fourier-wavelet composite transform. First, for each vibration feature vector obtained in step S141, it is regarded as a time series signal. Perform a Fourier transform (FFT) on this signal to analyze its frequency components. In MATLAB, use the fft function and set appropriate parameters such as sampling frequency and signal length. Next, perform a wavelet transform on the same signal to capture the time-frequency characteristics of the signal. Select a suitable wavelet basis (for example: 'sym8', which is the eighth order of the symmetric wavelet) and the number of decomposition layers (for example: 3 layers). In MATLAB, use the wavedec function for wavelet decomposition and the waverec function for reconstruction to obtain wavelet coefficients at different scales. By combining the results of FFT and wavelet transform, a composite frequency distribution data is obtained, which contains the frequency information provided by the Fourier transform and the time-frequency details provided by the wavelet transform. For example, it is found that there is a significant frequency peak near 5Hz, and the wavelet transform shows that this frequency component is particularly significant in a certain time period of the signal. Finally, the results of these composite transformations are organized into material microscopic vibration frequency distribution data.

[0044] Step S143: performing strain-frequency correlation identification on the material vibration response microscopic feature set based on the material microscopic vibration frequency distribution data to obtain pavement material vibration correlation mapping data;

[0045] Specifically, data analysis software (such as Python with SciPy and NumPy libraries) can be used to process the material micro-vibration frequency distribution data, and the frequency distribution data of each pavement material sample can be analyzed to identify the frequency points associated with the strain response. The data is visualized by drawing a frequency distribution graph, and a peak detection algorithm (such as the find_peaks function in the SciPy library) is used to identify significant frequency peaks. The parameters of peak detection are set, such as a minimum height of 0.5 and a minimum distance of 10 data points, to ensure that the frequency points that truly represent the material properties are identified. For example, for a specific asphalt pavement sample, it is found that there are obvious frequency peaks at 10Hz and 20Hz, which are closely related to the strain response of the material. These frequency points are associated with the corresponding strain values ​​to create an association map that contains the relationship between frequency and strain. For example, it is found that the strain is 0.01 at 10Hz and 0.02 at 20Hz. These data are recorded as {(10Hz, 0.01), (20Hz, 0.02)}, forming part of the pavement material vibration association map data. For a batch of pavement material samples, the above process is repeated to construct a vibration association map for each sample, and the samples are aggregated to form a pavement material vibration association map dataset.

[0046] Step S144: constructing a material vibration sensitivity index system for the pavement material vibration correlation mapping data to obtain a pavement material vibration sensitivity evaluation index set;

[0047] Specifically, statistical analysis software (such as R language) can be used to process the vibration correlation mapping data of pavement materials. First, the correlation mapping data of each pavement material sample is statistically analyzed to calculate the strength of the relationship between frequency and strain. Use correlation analysis (such as the Pearson correlation coefficient) to quantify the linear relationship between frequency and strain, and use the correlation coefficient as an indicator of vibration sensitivity. For example, for a specific asphalt pavement sample, the correlation coefficients between the strain response and frequency at 10Hz and 20Hz are calculated to be 0.8 and 0.9, respectively, indicating that these two frequency points have a strong correlation with the strain response. Use these correlation coefficients as vibration sensitivity indicators and combine them with other statistical indicators (such as the variance and mean of the frequency response) to construct a vibration sensitivity index system. For example, define a vibration sensitivity index that combines the correlation coefficient, variance and mean, and the formula is: For a batch of pavement material samples, the above process is repeated to construct a vibration sensitivity evaluation index set for each sample, and the sets are combined to form a pavement material vibration sensitivity evaluation index database.

[0048] Step S145: quantifying the strain response of the pavement material at different frequencies on the pavement material vibration correlation mapping data according to the pavement material vibration sensitivity evaluation index set, obtaining the pavement material spectrum-strain response curve, and identifying the extreme points of the pavement material spectrum-strain response curve to obtain the pavement material vibration characteristic critical point set;

[0049] Specifically, a data analysis and graphics processing software (such as MATLAB) can be used to process the vibration sensitivity evaluation index set, and the vibration correlation mapping data of each pavement material sample can be analyzed to determine the strain response at different frequencies. The spectrum-strain response curve is plotted to visualize the data, and the plot function is used to generate the graph. For example, for a specific asphalt pavement sample, a series of data points are obtained, such as {(5Hz, 0.01), (10Hz, 0.03), (15Hz,0.05), ..., (50Hz, 0.02)}, which represent the strain response at different frequencies. A curve fitting tool (such as the fit function of MATLAB) is used to fit the data points to obtain a smooth spectrum-strain response curve. Select a suitable fitting model, such as polynomial fitting or exponential fitting, and set the fitting parameters, such as the order of the polynomial is 3. Through fitting, a mathematical model describing the variation of strain response with frequency is obtained. The extreme point identification is performed on the fitted curve to determine the critical point set of the vibration characteristics of the pavement material. Use a derivative extreme value search algorithm (such as MATLAB's fminbnd or fminsearch function) to find the maximum and minimum points of the curve. Set the initial guess value and search interval for the search, such as searching between the frequency range of 5Hz and 50Hz. Use the algorithm to find the extreme points of the curve, such as the maximum point at 15Hz (15Hz, 0.05) and the minimum point at 30Hz (30Hz, 0.01), which are recorded as the critical point set of the vibration characteristics of the pavement material. For a batch of pavement material samples, repeat the above process, construct a spectrum-strain response curve for each sample, and identify the critical point set of its vibration characteristics, and finally obtain a comprehensive set of pavement material vibration characteristic data.

[0050] Step S146: Perform multi-scenario simulation verification on the critical point set of pavement material vibration characteristics to obtain material vibration sensitivity mapping data.

[0051] Specifically, finite element analysis software (such as ANSYS or ABAQUS) can be used to perform multi-scenario simulation verification. According to the critical point set of the pavement material vibration characteristics, select several key critical points, which represent the maximum or minimum strain response of the pavement material at a specific frequency. For example, the maximum point at 15Hz (15Hz, 0.05) and the minimum point at 30Hz (30Hz, 0.01) are selected as the key points of the simulation. Construct a finite element model of the pavement material. Select appropriate material property parameters, such as elastic modulus, Poisson's ratio, and density, which are based on laboratory tests or literature data. For asphalt pavement, set the elastic modulus to 2000 MPa and the Poisson's ratio to 0.35. Use the meshing tool to divide the model into sufficiently small units, such as the size of each unit is 1cm 1cm. Dynamic loading is applied to the finite element model to simulate different working scenarios. For example, the vibration effect of a vehicle passing through the road surface at different speeds is simulated. Different loading frequencies are set, including the critical point frequency of interest, and different loading durations, such as 1 second and 2 seconds. During the simulation, strain response data of key points are collected and compared with experimental data. The post-processing tools of the software are used to extract these data and calculate the error between the simulated strain and the experimental strain. For example, it is found that the simulated strain at 15Hz is 0.055, which is very close to the experimental value of 0.05, with an error within 5%, indicating that the model is effective. Finally, based on the comparison of simulation results and experimental data, the vibration sensitivity of the pavement material is mapped. The simulated strain response is combined with the experimental data to construct a material vibration sensitivity mapping data set. For example, a mapping data set is obtained, which contains simulated and experimental strain values ​​at different frequencies, such as {(15Hz, simulated strain 0.055, experimental strain 0.05), (30Hz, simulated strain 0.012, experimental strain 0.01)}.

[0052] The present invention provides a standardized data basis and enhances feature recognition capabilities by quantifying the microscopic feature set of the vibration response of the pavement material. By using the Fourier-wavelet composite transform to deeply analyze the vibration frequency distribution, the vibration behavior of the material at different frequencies is revealed. Through strain-frequency correlation identification, the strain response of the pavement material can be accurately identified. The constructed vibration sensitivity index system enables the sensitivity of the material to vibration to be systematically evaluated, providing a quantitative evaluation tool. The critical point set of the vibration characteristics of the pavement material is determined by drawing the spectrum-strain response curve and identifying the extreme points, providing a key reference for compaction control. The adaptability and accuracy of the model are improved through multi-scenario simulation verification, ensuring high prediction performance under different working conditions.

[0053] Preferably, step S16 comprises the following steps:

[0054] Step S161: classifying the pavement material vibration response micro-feature set according to the pavement material vibration characteristic classification standard to obtain a classified pavement material vibration characteristic data set;

[0055] Specifically, database management software (such as SQL Server or MongoDB) and data analysis tools (such as Python with pandas library) can be used to process and classify the micro-feature set of vibration response of pavement materials. The micro-feature data of vibration response of all pavement materials are exported from the database, which includes various feature values ​​extracted from steps S13 to S145, such as peak frequency, peak amplitude, energy distribution after wavelet transformation, etc. According to the classification criteria established in step S15, these feature values ​​are assigned to different categories. For example, three vibration sensitivity categories are defined: "low", "medium" and "high". A decision tree classification algorithm (DecisionTreeClassifier in scikit-learn library can be used) is used to automatically perform the classification task. Train this classifier, input feature values ​​including peak frequency, peak amplitude, etc., and the output is a classification label ("low", "medium" or "high"). During the training process, parameters with a maximum depth of 5 and a minimum sample split of 10 are selected to avoid overfitting. For example, there is an asphalt sample whose characteristic values ​​are: peak frequencies of 10Hz and 20Hz, corresponding peak amplitudes of 0.8 and 1.2 respectively, and the energy distribution after wavelet transform is {0.1, 0.3, 0.2, 0.1, 0.3}. Through the decision tree classifier, this sample is classified as a "high" vibration sensitivity category. All samples are classified in this way and the classification results are stored in a new database table to form a classified pavement material vibration characteristic dataset.

[0056] Step S162: performing a vibration response comparison experiment on different material types of the target road surface according to the classified road surface material vibration characteristic data set to obtain a classified road surface material dynamic response data set;

[0057] Specifically, laboratory testing equipment (e.g., dynamic signal analyzer and vibration table) can be used to conduct vibration response comparison experiments on different categories of pavement material samples. Three different types of pavement materials are selected, such as asphalt, concrete, and crushed stone, each of which has a different vibration sensitivity category. The vibration parameters of the vibration table are set, such as a frequency range of 0-50Hz, an amplitude of 0.1mm, and a duration of 60 seconds. Samples of each material type are tested and vibration response data such as acceleration, displacement, and strain are recorded. A dynamic signal analyzer (model: Lansmont Signal 30) is used to collect these data, and the sampling frequency is set to 1000Hz. For example, for an asphalt sample, the strain responses recorded under 10Hz and 20Hz vibrations are 0.03 and 0.05, respectively. For a concrete sample, the strain responses recorded under the same vibration conditions are 0.02 and 0.04, respectively. These data are organized into a classified pavement material dynamic response dataset, and each data point includes material type, vibration frequency, amplitude, and corresponding strain response.

[0058] Step S163: Calculate the pavement material response data difference index for the classified pavement material dynamic response data set to obtain a pavement material vibration characteristic difference index set, and construct a database based on the classified pavement material dynamic response data set and the pavement material vibration characteristic difference index set to obtain a pavement material vibration characteristic difference database;

[0059] Specifically, data analysis software (such as Python with pandas and NumPy libraries) can be used to process the classified pavement material dynamic response dataset. The dynamic response data of different material types are exported from the database, including the strain response under different vibration conditions. The difference indicators of the response data of each material at different frequencies are calculated, such as the standard deviation of the strain response, the coefficient of variation (the ratio of the standard deviation to the mean), the peak strain response, etc. For example, for two materials, asphalt and concrete, the standard deviation of the strain response under 10Hz vibration is calculated, and the standard deviation of the asphalt material is 0.01, and the standard deviation of the concrete material is 0.008. Use these difference indicators to quantify the differences in vibration characteristics of different materials. Use database management software (such as MySQL) to build a database of differences in pavement material vibration characteristics. Create a data table with the calculated difference indicators as fields and the response data of each material as records. For example, create a table named "MaterialVibrationCharacteristics" with fields including "MaterialType" (material type), "Frequency" (frequency), "StrainResponseStd" (strain response standard deviation), "CoefficientOfVariation" (coefficient of variation), etc. All calculated difference indices and corresponding material response data are stored in this database, forming a complete database of pavement material vibration characteristics differences.

[0060] Step S164: performing cross-scale correlation on the pavement material vibration characteristic difference database to obtain a pavement material vibration characteristic correlation diagram;

[0061] Specifically, a data visualization and analysis software (such as Python with NetworkX and Matplotlib libraries) can be used to process the vibration characteristic difference database of pavement materials. The vibration characteristic difference data of all pavement materials are exported from the database, including the standard deviation of strain response at different frequencies, coefficient of variation, etc. A graph database is constructed, with each material type as a node and the vibration characteristic difference indicators between materials as edges. For example, asphalt and concrete are taken as two nodes, and the difference in the standard deviation of strain response between them is used as the weight of the edge connecting the two nodes. Use the NetworkX library to create this graph, and use the Matplotlib library to visualize this graph. Set the size and color of the node to represent different material types, and set the width of the edge to represent the size of the vibration characteristic difference. For example, the edge between asphalt and concrete is thicker, indicating that they have a large difference in vibration characteristics. Through the pavement material vibration characteristic association graph, the difference in vibration characteristics between different pavement materials and the relationship between them can be intuitively seen. For example, if the association edge between asphalt and gravel is thin, it means that they have a small difference in vibration characteristics and can be used interchangeably under certain construction conditions.

[0062] Step S165: quantitatively describing the vibration characteristics of the target pavement material according to the pavement material vibration characteristic correlation diagram to obtain a pavement material vibration characteristic index set;

[0063] Specifically, data science software (such as Python with NetworkX and NumPy libraries) can be used to process the vibration characteristic association graph of pavement materials. The association graph is exported from the graph database, which contains nodes of different pavement materials and the edges between them. The weights of the edges represent the differences in vibration characteristics between materials. A set of vibration characteristic indicators are calculated for each material node, including vibration sensitivity, vibration transmission efficiency, vibration absorption capacity, etc. For example, vibration sensitivity is defined as the standard deviation of the strain response of the material at a specific frequency, vibration transmission efficiency is the mean of the strain response of the material at different frequencies, and vibration absorption capacity is the attenuation rate of the strain response of the material under high-frequency vibration. For a specific asphalt pavement sample, the following indicator set is calculated: vibration sensitivity is 0.05, vibration transmission efficiency is 0.2, and vibration absorption capacity is 0.03. These indicators are obtained by analyzing the strain response data of asphalt materials at different frequencies. These indicators are quantified and assigned specific values ​​to form a vibration characteristic indicator set of asphalt materials. The vibration characteristic indicator set of all materials is organized into structured data, such as a CSV file or a database table.

[0064] Step S166: performing dynamic response clustering on the target pavement material according to the pavement material vibration characteristic index set to obtain pavement material vibration characteristic clustering data;

[0065] Specifically, machine learning software (such as Python with the scikit-learn library) can be used to process the vibration characteristic index set of pavement materials. Import the vibration characteristic indexes of all pavement materials from structured data, including vibration sensitivity, vibration transmission efficiency, vibration absorption capacity, etc. Select the K-means clustering algorithm to perform cluster analysis on these indexes. For example, choose to divide the materials into three clusters: high vibration response group, medium vibration response group, and low vibration response group. In the scikit-learn library, use the KMeans class to implement this clustering process, and set the number of clusters n_clusters to 3. Before clustering, normalize the index data. Use the StandardScaler class to implement normalization and convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. For example, for a batch of pavement material samples containing asphalt, concrete, and gravel, through K-means clustering analysis, asphalt and gravel are classified as high vibration response groups, and concrete is classified as low vibration response groups. Mark the clustering results on each sample, and generate a clustering dataset containing the cluster labels of each sample. In this way, clustering data of pavement material vibration characteristics can be obtained.

[0066] Step S167: Perform principal component dimensionality reduction on the pavement material vibration characteristic clustering data to obtain a pavement material vibration characteristic mapping matrix, and perform eigenvalue reconstruction on the pavement material vibration characteristic mapping matrix to obtain pavement material response characteristic data.

[0067] Specifically, statistical analysis software (such as R language or Python with scikit-learn library) can be used to process the vibration characteristics clustering data of pavement materials. The clustered data are exported from the database, which includes a set of vibration characteristics indicators of different pavement materials, such as vibration sensitivity, vibration transmission efficiency, vibration absorption capacity, etc. The principal component analysis (PCA) method is selected for dimensionality reduction. In Python, the PCA class in the scikit-learn library is used to implement this process. The number of components (n_components) of PCA is set to 2 or 3. Before performing PCA, the data is standardized, and the mean of each feature is normalized to 0 and the standard deviation is normalized to 1 using the StandardScaler class. For example, for a pavement material data set containing asphalt, concrete, and gravel, after applying PCA, the original multidimensional feature space is reduced to a two-dimensional space, and a new feature space is obtained, in which each dimension is a linear combination of the original features, called a principal component. These principal components are sorted from large to small according to the variance contribution rate. The first principal component has the largest variance, the second principal component is orthogonal to the first and has the second largest variance, and so on. After dimensionality reduction, a pavement material vibration characteristic mapping matrix is ​​obtained, in which each row represents a sample and each column represents a principal component. The eigenvalues ​​of each principal component (i.e., the standard deviation of the principal component) are calculated, and these eigenvalues ​​represent the importance or variance contribution rate of each principal component. The mapping matrix is ​​reconstructed by eigenvalues ​​to obtain the pavement material response characteristic data. A certain proportion of variance (e.g., 95%) is selected to be retained, and the number of principal components to be retained is determined based on the eigenvalues. For example, if it is found that the first two principal components have explained 95% of the variance, only these two principal components are retained and the pavement material response characteristic data are reconstructed. Finally, the pavement material response characteristic data is obtained.

[0068] The present invention provides customized processing strategies for compaction of different material types by classifying the microscopic feature set of vibration response of pavement materials, enhancing the experimental comparative analysis capability, deeply understanding the behavioral differences of different materials under vibration, and constructing a comprehensive data difference indicator set, laying the foundation for establishing a database of differences in vibration characteristics of pavement materials. This database provides data resources for the vibration system, guides material selection and compaction strategies in actual construction, and reveals the relationship between material properties at different scales through cross-scale correlation analysis, enhancing the overall understanding of material behavior. The present invention can also quantitatively describe the vibration characteristics of pavement materials, provide key parameters for precise control of vibration systems, identify material groups with similar vibration response characteristics through dynamic response cluster analysis, and provide a new method for material classification management and compaction. Through principal component dimensionality reduction and eigenvalue reconstruction, key information can be extracted from complex data, the model can be simplified, and data availability can be improved.

[0069] Preferably, step S2 comprises the following steps:

[0070] Step S21: performing a vibration loading test on the vibration system of the road roller to obtain a vibration loading characteristic data set;

[0071] Specifically, a vibration test device (such as Lansmont Vibration Table) can be used to test the vibration system of the roller. The vibration system of the roller is fixed on the vibration table, and the parameters of the vibration table are set, such as a frequency range of 0-50Hz and an amplitude of 0.1mm to 2mm, to simulate different vibration conditions encountered in actual construction. A data acquisition system (such as a data acquisition card from National Instruments) is used to record parameters such as pressure, acceleration, and displacement during the vibration process. The data acquisition card is connected to the vibration table through a sensor and can record data in real time at a sampling frequency of at least 1000Hz. Set up data acquisition software (such as LabVIEW) to control the test process, including the duration of the test, the sampling frequency of the data, and the vibration mode of the vibration table. During the test, the vibration response data at different amplitudes and frequencies are recorded, including the acceleration, displacement, and force of the vibration system. For example, it is recorded that at a frequency of 10Hz and an amplitude of 1mm, the maximum acceleration of the vibration system of the roller is 5m / s² and the maximum displacement is 0.5mm. These data are transmitted to the computer in real time and stored as a vibration loading characteristic data set.

[0072] Step S22: extracting the pavement material frequency domain characteristics from the pavement material response characteristic data to obtain a pavement material frequency spectrum characteristic data set;

[0073] Specifically, signal processing software (such as MATLAB) can be used to process the pavement material response data. First, the time domain data is converted into frequency domain data to analyze the response characteristics of the pavement material at different frequencies. The fast Fourier transform (FFT) algorithm is used to achieve this conversion, and the parameters of the FFT, such as the sampling frequency and data length, are set. For example, for a specific asphalt pavement sample, time series signals are recorded during the vibration loading test. These time domain signals are converted into frequency domain signals using the fft function in MATLAB, and a frequency distribution diagram is obtained. By analyzing this distribution diagram, the main frequency response characteristics of the asphalt pavement sample, such as the resonant frequency, damping ratio, and frequency response amplitude, are extracted. The frequency domain characteristic parameters, such as the power spectral density (PSD) and frequency response function (FRF), are further calculated. For example, the PSD value of the asphalt pavement sample at 10Hz is calculated to be 0.02, and the FRF amplitude is 1.5. These frequency domain characteristic parameters are organized into a pavement material spectrum characteristic data set.

[0074] Step S23: performing feature alignment on the vibration loading characteristic data set and the pavement material spectrum characteristic data set to obtain an aligned vibration loading characteristic data set and an aligned pavement material spectrum characteristic data set, and performing feature fusion on the aligned vibration loading characteristic data set and the aligned pavement material spectrum characteristic data set to obtain a pavement material-roller coupling dynamic characteristic data set;

[0075] Specifically, the vibration loading characteristic dataset and the pavement material spectrum characteristic dataset can be exported from the database to ensure that they contain corresponding features that can be compared, such as the same frequency range. Perform feature alignment, which means that it is necessary to ensure that the features in the two datasets are compared at the same frequency points. For example, it is found that the vibration loading characteristic dataset has data at frequency points such as 5Hz, 10Hz, and 15Hz, and the pavement material spectrum characteristic dataset also has data at these frequency points. Select these common frequency points for alignment. Next, use a feature fusion technique, such as weighted fusion, to combine the information of the two datasets. Assign weights to the features in each dataset based on their contribution to the pavement compaction effect. For example, it is believed that the vibration acceleration of the roller has a more important effect on the compaction effect than the resonant frequency of the pavement material, so it is given a higher weight. Use the following formula for weighted fusion: .in, and are weight coefficients, and their sum is 1. For example, setting , Assume that at a frequency of 10 Hz, the vibration acceleration of the roller is 3 m / s², and the amplitude corresponding to the resonance frequency of the pavement material is 1.5. The coupling characteristics are calculated according to the above weight fusion formula: This coupling eigenvalue represents the comprehensive dynamic response of the pavement material and roller vibration system at a frequency of 10 Hz. The coupling eigenvalues ​​of all frequency points are sorted into a new data set, namely the pavement material-roller coupling dynamic characteristic data set.

[0076] Step S24: performing vibration system parameter identification based on the pavement material-roller coupling dynamic characteristic data set to obtain a roller-pavement coupling dynamic parameter set;

[0077] Specifically, parameter identification software (such as MATLAB's System Identification Toolbox) can be used to process the pavement material-roller coupled dynamic characteristic data set. Export the coupled dynamic characteristic data set from the database, which includes vibration responses at different frequencies, such as acceleration, displacement, and strain. Select the least squares method or the maximum likelihood method to estimate the parameters of the vibration system. For example, select the least squares method to fit the data and estimate the stiffness and damping parameters of the system. Set up the lsqcurvefit function in MATLAB to implement this process and define the objective function, which describes the difference between the theoretical response and the actual response of the vibration system. For example, there is a theoretical model that describes the dynamic behavior of the roller vibration system in the form of: .in, is the amplitude, is the damping ratio, is the natural frequency, is the damping frequency, is the time. Use the experimental data to estimate these parameters by minimizing the sum of squared errors between the experimental response and the theoretical response. Run the parameter identification algorithm and get a set of optimal parameters, e.g. , , , These parameters constitute the roller-pavement coupling dynamic parameter set, which describe the dynamic characteristics of the roller vibration system under different conditions.

[0078] Step S25: performing nonlinear dynamic modeling on the roller vibration system based on the roller-pavement coupling dynamic parameter set to obtain an initial digital model of the vibration system;

[0079] Specifically, a nonlinear dynamic modeling software (such as Simulink of MATLAB or PyDyn library of Python) can be used to construct a nonlinear dynamic model of the vibration system of the road roller. According to the dynamic parameter set obtained in step S24, set the parameters of the model, such as stiffness, damping and mass. Construct a nonlinear dynamic model including springs, dampers and nonlinear elements (such as friction or clearance). For example, in Simulink, use basic elements such as "Mass", "Damper" and "Spring" to build a model, and add a "Nonlinear Spring" element to simulate nonlinear characteristics. Set the parameters of these elements according to the parameter set. For example, set the stiffness of the spring to 1000 N / m, the damping coefficient of the damper to 0.1 Ns / m, and the parameters of the nonlinear spring to a specific nonlinear curve, such as the Hertz contact model. Configure the input of the model, such as simulating different vibration inputs, such as sine waves or random vibrations, and set the time and initial conditions of the simulation. Run the simulation and observe the output of the model, such as acceleration, velocity and displacement response. After the simulation is completed, analyze the simulation results to verify the accuracy of the model. It is found that the response of the model is in good agreement with the experimental data, indicating that the model can accurately simulate the behavior of the roller vibration system. This nonlinear dynamic model is the initial digital model of the vibration system.

[0080] Step S26: extracting dynamic features from the initial digital model of the vibration system to obtain dynamic parameter mapping data of the vibration system, and calibrating parameters of the initial digital model of the vibration system based on the dynamic parameter mapping data of the vibration system to obtain the digital model of the vibration system.

[0081] Specifically, please refer to the sub-steps of step S26 for the detailed implementation process of this embodiment.

[0082] The present invention obtains a comprehensive vibration loading characteristic data set through vibration loading test, thereby improving the integrity and accuracy of the data set. By extracting the frequency domain characteristics of the pavement material, an in-depth understanding of the response characteristics of the material at different frequencies is obtained. The vibration loading characteristic data set and the pavement material spectrum characteristic data set are effectively integrated through feature alignment and feature fusion technology to form a coupled dynamic characteristic data set. Parameter identification based on this data set can accurately obtain the coupling dynamic parameters between the roller and the pavement. By using the coupling dynamic parameter set for nonlinear dynamic modeling, an initial digital model of the vibration system that reflects the actual physical phenomena is constructed. By performing dynamic feature extraction and parameter calibration on the initial digital model of the vibration system, an accurate digital model of the vibration system is obtained, thereby improving the prediction accuracy and practicality of the model.

[0083] Preferably, step S26 comprises the following steps:

[0084] Step S261: Analyze the geometric constraints of the vibration system of the road roller to obtain a geometric parameter constraint set of the vibration system;

[0085] Specifically, please refer to the sub-steps of step S261 for the detailed implementation process of this embodiment.

[0086] Step S262: constructing dynamic boundary conditions for the roller vibration system according to the vibration system geometric parameter constraint set to obtain a system dynamic constraint framework;

[0087] Step S263: Discrete modeling of the vibration system of the road roller based on the initial digital model of the vibration system and the system dynamics constraint framework to obtain a discretized model of the vibration system;

[0088] Step S264: extracting eigenvalues ​​from the vibration system discretization model to obtain a vibration system eigenvalue set, and mapping the vibration system eigenvalue set with the roller vibration system to obtain vibration system dynamic parameter mapping data;

[0089] Step S265: collecting real-time data of the vibration system of the roller using sensors to obtain a real-time working parameter set of the roller, and calculating correction parameters of the dynamic parameter mapping data of the vibration system according to the real-time working parameter set of the roller to obtain correction data of the vibration system parameters;

[0090] Step S266: calibrate the parameters of the initial digital model of the vibration system according to the vibration system parameter correction data to obtain the digital model of the vibration system.

[0091] The present invention realizes accurate geometric and dynamic modeling of the roller vibration system through geometric constraint analysis and dynamic boundary condition construction, thereby obtaining the system's dynamic constraint framework, which not only improves the physical consistency of the model, but also enhances the reliability of the model. By extracting eigenvalues ​​from the discretized model of the vibration system, the feature analysis capability of the model is enhanced, and the key dynamic characteristics of the system are identified. By mapping the vibration system eigenvalue set with the actual roller vibration system, accurate mapping of dynamic parameters is achieved, so that the model can more accurately reflect the system behavior under actual working conditions. Real-time data acquisition by sensors further enhances the real-time adaptability of the model, so that the model can adapt to changes in the construction process in real time. By calibrating the parameters of the initial digital model of the vibration system according to the parameter correction data, the parameter calibration process is optimized, and the accuracy of model prediction and the accuracy of construction control are improved.

[0092] Preferably, step S261 includes the following steps:

[0093] Step S2611: performing three-dimensional scanning on the vibration system of the road roller to obtain a three-dimensional model of the vibration system, and performing geometric feature extraction on the three-dimensional model of the vibration system to obtain a geometric feature set of the vibration system;

[0094] Specifically, a three-dimensional scanning device (such as FARO Focus3D X 130 laser scanner) can be used to scan the vibration system of the road roller. The vibration system of the road roller is placed on a stable platform, and the parameters of the scanner are set, such as a resolution of 0.5 mm and a scanning speed of 10 frames per second. During the scanning process, a full-range scan is performed around the vibration system to capture all its geometric details. After the scanning is completed, the scanned data is processed using a three-dimensional data processing software (such as Geomagic Control X) to generate a three-dimensional model of the vibration system. Geometric features are extracted from the three-dimensional model using a feature extraction tool (such as CATIA or SolidWorks). Key geometric features such as the diameter, width, thickness of the vibration wheel, and the size and shape of the connecting parts are identified and measured. For example, the diameter of the vibration wheel is measured to be 1.5m, the width is 0.7m, and the thickness is 0.2m. These geometric features are recorded as a vibration system geometric feature set.

[0095] Step S2612: reconstructing the vibration system geometric feature set by topological constraints to obtain the vibration system geometric constraint relationship data;

[0096] Specifically, computer-aided design software (such as Siemens NX) can be used to process the geometric feature set of the vibration system. The geometric feature set is imported into the NX software, and a detailed geometric model of the vibration system is constructed. The model is reconstructed with topological constraints. The geometric relationships and constraints between the various components in the model are defined, such as fixed connections, rotational connections, and sliding connections. Use the constraint management tools in the NX software to set these constraints. For example, the connection between the vibration wheel and the roller body is defined as a fixed connection to ensure that their relative positions remain unchanged during movement. The influence of these constraints on the overall geometric structure of the vibration system is further analyzed to obtain the geometric constraint relationship data of the vibration system. For example, it is found that the position and angle of the vibration wheel are restricted by the surrounding structure, and these constraints are recorded as geometric constraint relationship data. In this way, key obtains the geometric constraint relationship data of the vibration system.

[0097] Step S2613: extracting constraint conditions of the vibration system of the road roller according to the geometric constraint relationship data of the vibration system to obtain a set of geometric constraint indicators of the vibration system;

[0098] Specifically, engineering analysis software (such as ANSYS SpaceClaim) can be used to process the geometric constraint relationship data of the vibration system. Import the detailed geometric model and constraint relationship data of the vibration system into the software. Check the connection and contact between each component to determine their influence on the behavior of the vibration system. Use the constraint analysis tool in the software to extract geometric constraints. For example, it is found that there is a fixed connection between the vibrating wheel and the roller body, which means that the vibrating wheel cannot move relative to the body. This constraint condition is extracted and quantified into geometric constraint indicators, such as constraint type (fixed, hinged, sliding, etc.), constraint position (coordinate value), and constraint direction (angle or rotation axis). For example, the following set of geometric constraint indicators is extracted: the fixed connection between the vibrating wheel and the body is at coordinates (0, 0, 0), the contact point between the vibrating wheel and the ground is at coordinates (1.5, 0, -0.2), and the vibrating wheel can rotate ±5 degrees in the vertical direction. These indicators are recorded and organized into a set.

[0099] Step S2614: performing kinematic constraint mapping on the roller vibration system according to the vibration system geometric constraint index set to obtain system motion freedom mapping data;

[0100] Specifically, multi-body dynamics simulation software (such as RecurDyn) can be used for kinematic constraint mapping. The geometric model and geometric constraint index set of the vibration system are imported into the simulation software, and the multi-body dynamics model of the system is constructed. Kinematic constraints are set according to the geometric constraint index set. For example, a fixed joint is set according to the fixed connection between the vibration wheel and the fuselage, and a sliding joint is set according to the contact point between the vibration wheel and the ground, and its rotation angle is limited. Use the kinematic analysis tool of the software to define these constraints and map them to each degree of freedom of the system. Analyze the degrees of freedom of motion of each component and record their range of motion and restrictions. For example, it is found that the degrees of freedom of the vibration wheel in the vertical direction are ±5 degrees of rotation, the degrees of freedom in the horizontal direction are 0 (fixed), and the degrees of freedom along the forward direction of the roller are sliding (unrestricted). These data are organized into system motion degree of freedom mapping data, including the type, range and constraint conditions of each degree of freedom. In this way, the system motion degree of freedom mapping data can be obtained.

[0101] Step S2615: performing constraint space topology modeling on the roller vibration system based on the system motion freedom mapping data to obtain a geometric constraint boundary framework of the vibration system;

[0102] Specifically, CAD software (such as SolidWorks) and topology optimization software (such as Altair OptiStruct) can be used to process the system motion freedom mapping data. The detailed geometric model of the roller vibration system and the motion freedom mapping data are imported into the CAD software, and a detailed geometric model of the system is constructed. The vibration system is topologically modeled in constrained space using topology optimization software. Define the boundary conditions of the model, such as the connection points of the fixed vibration wheel and the fuselage, and the sliding boundary where the vibration wheel contacts the ground. Set material properties and load conditions, such as the density, elastic modulus, and force of the vibration wheel during compaction. In Altair OptiStruct, run the topology optimization algorithm, such as using the sensitivity method or the gradient projection method, to determine the optimal distribution of materials to maximize the performance of the system while satisfying geometric constraints. Set the objective function of the optimization, such as minimizing the weight of the system or maximizing its stiffness, and define the constraints of the design variables, such as the shape and size of the vibration wheel. After the optimization is completed, the vibration system geometric constraint boundary framework is obtained, which is a model that describes the optimal material distribution and structural layout of the vibration system under all geometric constraints.

[0103] Step S2616: simulate the extreme working condition of the vibration system geometric constraint boundary framework to obtain the extreme value constraint condition set of the vibration system, and fine-tune the parameters of the initial digital model of the vibration system according to the extreme value constraint condition set of the vibration system to obtain the geometric parameter constraint set of the vibration system.

[0104] Specifically, simulation software (such as ANSYS Mechanical) can be used to simulate extreme conditions. The vibration system geometric constraint boundary framework is imported into the simulation software, and the parameters of the extreme conditions are set, such as the maximum vibration frequency, maximum amplitude, and maximum working pressure. These extreme condition loads are applied to the model, and the simulation is run to simulate the behavior of the roller under extreme conditions. Key parameters such as the maximum stress, maximum displacement, and stability of the vibration wheel are monitored. For example, it is found that the maximum stress of the vibration wheel is 250 MPa and the maximum displacement is 5 mm at the maximum amplitude. After the simulation is completed, the results are analyzed and the extreme value constraint condition set is extracted, including the extreme values ​​of parameters such as maximum stress, maximum displacement, and vibration frequency. These extreme values ​​provide the performance boundary of the vibration system under extreme conditions. Based on these extreme value constraint condition sets, we fine-tune the parameters of the initial digital model of the vibration system. For example, increase the material strength of the vibration wheel or adjust its geometry to ensure the performance and safety of the system under extreme conditions. These adjustments are made in the CAD software using parametric modeling tools, and the digital model is updated. Finally, the geometric parameter constraint set of the vibration system is obtained, which is a set that includes all geometric parameters and performance boundaries under extreme working conditions.

[0105] The present invention realizes accurate geometric modeling of the vibration system of the road roller through three-dimensional scanning and geometric feature extraction. The geometric constraint relationship of the vibration system is revealed through the reconstruction of topological constraints, which enhances the understanding of the interdependence of the internal structure of the system. The acquisition of the geometric constraint index set provides accurate parameters for kinematic constraint mapping and system modeling, while the mapping of motion degrees of freedom helps to predict the behavior of the system under actual working conditions. The geometric constraint boundary framework of the vibration system is constructed through constraint space topological modeling. The behavior of the system under extreme conditions is predicted through extreme working condition simulation, ensuring the safety and reliability of the system. The accuracy of the geometric parameter constraint set of the vibration system is further improved through parameter fine-tuning and optimization, and the system performance is optimized.

[0106] Preferably, step S3 comprises the following steps:

[0107] Step S31: constructing a dynamic boundary of the roller vibration system based on the vibration system geometric parameter constraint set to obtain a vibration system control boundary;

[0108] Specifically, system analysis software (such as MATLAB's Simulink) can be used to process the geometric parameter constraint set of the vibration system. The geometric parameter constraint set is imported into the software. These parameters include the maximum stress, maximum displacement and other extreme working condition parameters of the vibration wheel. In Simulink, a dynamic model is built to simulate the behavior of the roller vibration system. The dynamic boundaries of the model are set according to the geometric parameter constraint set. For example, the maximum stress boundary of the vibration wheel is set to 250 MPa and the maximum displacement boundary is set to 5 mm. These boundaries define the limits of safe operation of the system. Use the "Saturation" module in Simulink to simulate these boundaries. The "Saturation" module can limit the output value of the signal to not exceed the set upper and lower limits. For example, set the upper limit of the stress signal to 250 MPa and the upper limit of the displacement signal to 5 mm. In this way, when the actual stress or displacement of the system exceeds these boundaries, the output signal will be limited to the boundary value, simulating the actual physical limitations of the system. In this way, the vibration system regulation boundaries are obtained, and these boundaries are integrated into the dynamic model.

[0109] Step S32: constructing a dynamic threshold adjustment mechanism for the vibration system digital model based on the vibration system control boundary to obtain a vibration system parameter threshold adjustment mechanism;

[0110] Specifically, the vibration system control boundary can be integrated into the digital model, and the dynamic threshold adjustment parameters can be set. The "Gain" module and "Switch" module in Simulink are used to construct a dynamic threshold adjustment mechanism. For example, the vibration frequency and amplitude are dynamically adjusted according to the actual stress and displacement values ​​of the vibration wheel. When the actual stress is close to the upper limit, the gain of the vibration frequency is reduced through the "Gain" module to reduce the stress; when the displacement exceeds the upper limit, it is switched to a lower amplitude setting through the "Switch" module. Threshold adjustment parameters are set, such as a stress threshold of 240 MPa (lower than the upper limit of 250 MPa) and a displacement threshold of 4.5 mm (lower than the upper limit of 5 mm). When the actual stress exceeds 240 MPa, the system automatically reduces the vibration frequency; when the actual displacement exceeds 4.5 mm, the system automatically reduces the amplitude. In this way, a vibration system parameter threshold adjustment mechanism is obtained, which can dynamically adjust the vibration parameters according to the actual working conditions to keep the system within a safe and effective operating range.

[0111] Step S33: performing multi-scenario simulation verification on the vibration system parameter threshold adjustment mechanism to obtain system parameter threshold adaptability evaluation data;

[0112] Specifically, simulation software (such as ANSYS Mechanical or MATLAB Simulink) can be used to perform multi-scenario simulation verification of the vibration system parameter threshold adjustment mechanism. According to the different working conditions encountered in actual construction, a series of simulation scenarios are defined, including different soil types, roller speeds, vibration frequencies, and amplitudes. For each simulation scenario, set the corresponding parameter values ​​and run the simulation. For example, in the first scenario, the simulated roller is traveling at a speed of 4 km / h on soft soil, the vibration frequency is set to 30 Hz, and the amplitude is set to 1.5 mm. In the second scenario, the simulated roller is traveling at a speed of 6 km / h on hard soil, the vibration frequency is set to 35 Hz, and the amplitude is set to 2.0 mm. Monitor key parameters during the simulation process, such as the stress, displacement, and soil compaction of the vibration wheel. Record the performance of these parameters under different working conditions, especially the system's response when the parameters approach or exceed the preset threshold. For example, it was found that on soft soil, when the stress exceeded 240 MPa, the system automatically reduced the vibration frequency to keep the stress within a safe range. After the simulation is completed, analyze the results of each scenario to evaluate the adaptability of the parameter threshold adjustment mechanism. Obtain system parameter threshold adaptability evaluation data.

[0113] Step S34: reconstructing the parameter space of the vibration system digital model according to the system parameter threshold adaptability evaluation data to obtain a vibration system parameter control framework;

[0114] Specifically, the parameter threshold adaptability evaluation data can be imported into MATLAB and the performance of the system under different parameter settings can be analyzed. Use a genetic algorithm or a particle swarm optimization algorithm to find the optimal parameter settings. For example, use a genetic algorithm to optimize the combination of vibration frequency and amplitude to maximize soil compaction while keeping stress within a safe range. Define the optimization objective function, such as maximizing compaction, while setting constraints such as stress not exceeding 250 MPa and displacement not exceeding 5 mm. Run the optimization algorithm and monitor the performance changes of each iteration. For example, the algorithm will find a new parameter combination with a vibration frequency of 32 Hz and an amplitude of 1.8 mm, which improves soil compaction while keeping stress below 240 MPa, and finally obtain the parameter regulation framework of the vibration system.

[0115] Step S35: performing control strategy iteration on the vibration system digital model according to the vibration system parameter control framework to obtain a vibration system control model.

[0116] Specifically, the optimal parameter settings and performance impact data obtained in the parameter control framework can be imported into the Simulink model. In Simulink, a digital model of the vibration system is constructed, and the initial control parameters are set based on the data in the parameter control framework. A control strategy, such as a proportional-integral-derivative (PID) controller, is designed to dynamically adjust the parameters of the vibration system, such as vibration frequency and amplitude, in response to different soil conditions and compaction requirements. The parameters of the PID controller are set, such as the proportional gain (Kp), integral gain (Ki), and derivative gain (Kd). For example, Kp=0.5, Ki=0.1, and Kd=0.05 are selected based on the analysis of the system response in the parameter control framework. The controller is configured to adjust the vibration parameters based on the feedback signal of the soil compaction to keep the compaction within the target range. Next, the control strategy is run in Simulink and tested for multiple iterations. In each iteration, the parameters of the PID controller are adjusted based on the simulation results. For example, it is found that the current control strategy performs well on hard soils, but causes insufficient compaction on soft soils. Therefore, the values ​​of Ki and Kd are adjusted to improve the compaction effect on soft soil. Stateflow is used to manage different states and modes of the control strategy, such as switching between different soil types and compaction stages. State transition conditions are defined, such as increasing the amplitude when the soil compaction is lower than the target value, or reducing the frequency when the vibration wheel stress exceeds the threshold. After multiple iterations and adjustments, a vibration system control model that can adapt to different working conditions and optimize the compaction effect can be obtained. This control model integrates the optimal parameters and control strategies in the parameter regulation framework, and can automatically adjust the vibration parameters in actual construction.

[0117] The present invention optimizes the vibration system control and accurately defines the operating range by constructing the vibration system control boundary based on the geometric parameter constraint set. It introduces a dynamic threshold adjustment mechanism, which enables the system to adjust the parameter threshold according to different working conditions, enhancing adaptability and flexibility. The adaptability of the adjustment mechanism is ensured through multi-scenario simulation verification. The accuracy and practicality of the model are further improved through the reconstruction of the parameter space, and the iterative optimization of the control strategy continuously improves the vibration system control model, improving the compaction effect and construction efficiency.

[0118] Preferably, step S4 comprises the following steps:

[0119] Step S41: numerically simulating the compaction process of the target pavement material based on the vibration system control model to obtain an initial pavement compaction simulation model;

[0120] Specifically, numerical simulation software (such as ABAQUS) can be used to perform numerical simulation of the compaction process. According to the parameter settings and control strategies in the vibration system control model, the boundary conditions and loading conditions of the simulation are defined. For example, the vibration frequency of the vibration wheel is set to 35 Hz and the amplitude is 2.0 mm, and these parameters are adjusted according to the control model to adapt to different soil conditions. In ABAQUS, a geometric model of the target pavement material is constructed and meshed for finite element analysis. Appropriate material properties, such as elastic modulus and Poisson's ratio, are selected, which are based on laboratory test data. For example, for asphalt concrete, the elastic modulus is set to 2 GPa and the Poisson's ratio is set to 0.35. Boundary conditions for the contact between the vibration wheel and the pavement are applied, and the time step and total time of the compaction process are set. For example, a time step of 0.01 seconds and a total compaction time of 10 seconds are selected. Then, the simulation is run to observe the deformation and stress distribution of the pavement material under vibration. After the simulation is completed, the initial compaction simulation model of the pavement is obtained, which describes the stress, strain and density distribution of the pavement material during the compaction process.

[0121] Step S42: discretizing the pavement material microstructure on the pavement initial compaction simulation model to obtain a pavement material microgrid dynamic model;

[0122] Specifically, the initial compaction simulation model of the pavement can be processed using microstructure modeling software (such as MesoScale or custom scripts combined with computational particle technology such as DEM) and the microstructure discretization can be performed. The macroscopic properties of the pavement material, such as density and elastic modulus, are extracted from the initial compaction simulation model and used in the microstructure model. The microstructure model of the pavement material is constructed, the material is discretized into particles or units, and the contact model between the particles is defined. For example, asphalt concrete is discretized into particles with diameters between 0.1 mm and 10 mm, and the Hertz-Mindlin contact model is used to describe the contact forces between the particles. The initial random arrangement of the particles is set, and the properties of the particles, such as particle size distribution, particle shape, and friction coefficient between particles are defined. For example, the friction coefficient of the particles is set to 0.6 to simulate the friction effect between the particles. The microstructure model is run to simulate the dynamic behavior of the particles under vibration. The movement of the particles, the distribution of contact and force, and the relative displacement between the particles are monitored. For example, it is found that the contact force between the particles increases during vibration, resulting in an increase in the density of the material and a decrease in porosity. In this way, a micro-grid dynamic model of the pavement material can be obtained, which describes the changes in the microstructure of the pavement material during the compaction process.

[0123] Step S43: constructing dynamic constraint conditions for the compaction process of the target pavement material according to the pavement material micro-grid dynamic model to obtain a pavement compaction process boundary condition set;

[0124] Specifically, computational particle technology software (such as EDEM) can be used to process the micro-grid dynamic model of pavement materials and construct dynamic constraints. Extract key dynamic parameters from the micro-grid dynamic model, such as the contact stiffness and damping characteristics of the particles. Define boundary conditions during compaction, such as the contact force model between particles, the friction conditions between particles and the mold, and the frequency and amplitude of the vibration input. For example, set the frequency of the vibration input to 40 Hz and the amplitude to 0.5 mm, and adjust these parameters based on experimental data. In EDEM, set the physical properties of the particles, such as density and particle size distribution, and define the contact model between particles. Use the Mohr-Coulomb model to describe the friction and adhesion between particles, set the internal friction angle to 30 degrees, and the adhesion to 1000 Pa. Construct a two-dimensional or three-dimensional compaction model to simulate the dynamic behavior of particles under vibration. Define the geometric boundaries of the model and set boundary conditions, such as the particles cannot pass through the side boundaries of the mold during compaction, and finally obtain the boundary condition set for the pavement compaction process.

[0125] Step S44: numerically simulating the compaction process of the target pavement material through the pavement compaction process boundary condition set to obtain a compaction process simulation model, and extracting compaction simulation data from the compaction process simulation model to obtain preliminary simulation data of the compaction process;

[0126] Specifically, numerical simulation software (such as ABAQUS) can be used to perform numerical simulation of the compaction process. Set simulation parameters according to the boundary condition set of the pavement compaction process. Construct a geometric model of the pavement material and divide the mesh for finite element analysis. Apply a boundary condition set, such as setting the contact force model between particles to the Mohr-Coulomb model, the friction angle to 30 degrees, and the bonding force to 1000 Pa. Set the frequency of the vibration input to 40 Hz and the amplitude to 0.5 mm, and apply these conditions in the simulation. Run the simulation to simulate the compaction process of particles under vibration. Monitor the movement, contact and force distribution of the particles, as well as the changes in the density and porosity of the material. For example, it is found that during the vibration compaction process, the density of the material increases from 1600 kg / m³ to 1900 kg / m³, and the porosity decreases from 20% to 10%. After the simulation is completed, extract the preliminary simulation data in the compaction process simulation model, including the displacement, stress, strain, and density distribution of the particles. Use the post-processing tools of ABAQUS to visualize and analyze these data, such as generating stress cloud maps and density distribution maps. In this way, preliminary simulation data of the compaction process can be obtained.

[0127] Step S45: quantitatively evaluating the compaction effect of the target road surface based on the preliminary simulation data of the compaction process to obtain a set of compaction effect evaluation indicators, wherein the quantitative evaluation of the compaction effect includes a compaction uniformity indicator evaluation, a compaction density indicator evaluation, and a residual stress distribution probability evaluation;

[0128] Specifically, a compaction uniformity index can be calculated, which measures the consistency of the pavement compaction. Compaction uniformity is quantified using the standard deviation or coefficient of variation (ratio of standard deviation to mean). For example, the standard deviation of the density distribution of the entire pavement sample is calculated, and a small standard deviation indicates that the compaction is relatively uniform. The density value of each unit is extracted from the simulation data, and its mean and standard deviation are calculated. If the standard deviation is 0.05 (relative to the mean), the compaction uniformity is considered high. Compaction density index is calculated, which measures how compact the pavement is compacted. Compaction density is quantified using the compaction percentage (ratio of actual density to maximum theoretical density). The density value of each unit is extracted from the simulation data and compared to the maximum theoretical density of the material. For example, if the actual average density is 2000 kg / m³ and the maximum theoretical density is 2200kg / m³, the compaction percentage is 90.9%, indicating that the compaction effect is good. The residual stress distribution probability index is calculated, which measures the distribution of stress inside the pavement after compaction. The probability density function (PDF) is used to describe the distribution of residual stress. The residual stress value of each unit is extracted from the simulation data, and the histogram function in the SciPy library is used to generate a probability density distribution diagram of the residual stress. For example, it is found that the residual stress is mainly concentrated between 0-5 MPa, accounting for 80% of the total stress distribution, indicating that the residual stress distribution is relatively concentrated. In this way, a set of compaction effect evaluation indicators can be obtained, including compaction uniformity, compaction density, and residual stress distribution probability.

[0129] Step S46: modifying the compaction process simulation model according to the compaction effect evaluation index set to obtain a modified compaction process numerical model.

[0130] Specifically, key indicators such as compaction uniformity, compaction density, and residual stress distribution probability can be extracted from the compaction effect evaluation index set. For example, it is found that the standard deviation of compaction uniformity is 0.06, slightly higher than the expected 0.05, while the compaction density is 90%, which is in line with expectations, and the residual stress distribution is mainly concentrated in 0-5 MPa, which meets the design requirements. Based on these evaluation indicators, determine which corrections need to be made to the simulation model. For the problem of insufficient compaction uniformity, it is necessary to adjust the vibration parameters of the vibration wheel or the compaction process. The vibration frequency and amplitude of the vibration wheel are selected as correction parameters. In ABAQUS, the vibration frequency of the vibration wheel is modified from 35 Hz to 36 Hz, the amplitude from 2.0 mm to 2.1 mm, and the simulation is rerun. In Python, the NumPy library is used to process the simulation data, and the optimization algorithm in the SciPy library is used to find the optimal vibration parameters. Define an objective function that calculates the standard deviation of compaction uniformity under given vibration parameters, and use an optimization algorithm (such as gradient descent) to find the parameter value that minimizes the standard deviation. After correcting the simulation model, we re-ran the simulation and collected new simulation data. We compared the compaction effect evaluation indicators before and after the correction to verify the effectiveness of the correction. For example, we found that the standard deviation of compaction uniformity was reduced to 0.04 and the compaction density was increased to 92%, both of which met the expected goals. In this way, we can obtain a corrected compaction process numerical model that more accurately reflects the actual compaction process.

[0131] The present invention improves the simulation accuracy and prediction accuracy of the compaction process through numerical simulation and quantitative evaluation. The constructed dynamic constraints and detailed simulation data extraction provide a data basis for the authenticity and subsequent evaluation of the compaction process. A comprehensive set of compaction effect evaluation indicators, including compaction uniformity, compaction density and residual stress distribution probability, provides a comprehensive perspective for evaluating compaction quality.

[0132] Preferably, step S5 comprises the following steps:

[0133] Step S51: constructing system performance index dimensions for the modified compaction process numerical model to obtain a vibration system performance evaluation dimension set;

[0134] Specifically, key performance parameters can be extracted from the modified compaction process numerical model, such as the vibration frequency, amplitude, compaction degree of the vibrating wheel, and the density and strength of the pavement material. Define the dimensions of performance indicators, for example: Vibration performance dimension: including the stability of vibration frequency and amplitude, setting parameters such as frequency deviation rate (the deviation between the actual frequency and the set frequency) and amplitude deviation rate. Compaction effect dimension: including compaction degree and density uniformity, calculating the standard deviation and coefficient of variation of compaction degree. Material performance dimension: including the elastic modulus and strength of the pavement material, using simulation data to estimate these parameters. For example, the specific indicators defined in the vibration performance dimension are: Frequency deviation rate: the set frequency is 35 Hz, the actual measured frequency is 34.9 Hz, and the deviation rate is. Amplitude deviation rate: The set amplitude is 2.0 mm, the actual measured amplitude is 1.98 mm, and the deviation rate is , and finally the vibration system performance evaluation dimension set is obtained.

[0135] Step S52: constructing a performance monitoring framework for the modified compaction process numerical model according to the vibration system performance evaluation dimension set to obtain a vibration system performance monitoring framework;

[0136] Specifically, the indicators in the performance evaluation dimension can be integrated into the monitoring framework, and the data acquisition and processing process can be set. Configure data acquisition hardware (such as National Instruments' data acquisition card) to collect real-time performance data of the vibration system, including vibration parameters of the vibration wheel and pavement compaction parameters. Set the sampling frequency to 1000 Hz, and use the graphical programming environment of LabVIEW to design the monitoring interface to display real-time data and trend charts of key performance indicators. For example, design a dashboard to display current vibration frequency, amplitude, compaction degree, material strength and other indicators, as well as their real-time changes. Set the alarm threshold, and the system will automatically issue an alarm when the performance indicator exceeds the preset range. For example, if the amplitude deviation rate exceeds 2%, the system will trigger an alarm, prompting the operator to make adjustments, and finally obtain the vibration system performance monitoring framework.

[0137] Step S53: dynamically tracking the modified compaction process numerical model using the vibration system performance monitoring framework to obtain a vibration system performance change curve;

[0138] Specifically, a data acquisition card can be installed on the roller to collect the operating data of the vibration system in real time, including parameters such as vibration frequency, amplitude, and compaction degree. Set the sampling frequency of the data acquisition card to 1000 Hz, and in LabVIEW, design a monitoring interface to display the real-time data and trend graphs of these key performance parameters. Use the chart function of LabVIEW to draw curves of the performance parameters of the vibration system over time, such as the vibration frequency curve and the amplitude curve. During the compaction process, dynamically track the changes in these performance parameters and record the performance change curve. For example, it was found that the vibration frequency was maintained at 36 Hz, the amplitude was maintained at 2.1 mm, and the compaction degree increased from 85% to 92% during the compaction process. In this way, the performance change curve of the vibration system can be obtained.

[0139] Step S54: extracting system performance change points from the vibration system performance change curve to obtain vibration system performance mapping data, and performing intelligent early warning on the roller vibration system based on the vibration system performance mapping data to obtain a system operation status early warning diagram;

[0140] Specifically, data of the performance change curve can be imported from the data acquisition system and processed using the NumPy library. Performance change points are defined as significant changes in performance parameters, such as a sudden increase in vibration frequency or a significant decrease in compaction. Signal processing tools in the SciPy library are used to detect these change points, such as using the find_peaks function to identify significant peaks in the compaction curve. Detection parameters, such as the minimum height and minimum distance of the peak, are set to identify significant changes in the compaction curve. For example, the minimum height is set to 0.5% and the minimum distance is set to 10 data points to identify a significant increase in compaction. These change points are extracted and their time and amplitude are recorded to obtain vibration system performance mapping data. Based on these performance mapping data, a machine learning algorithm (such as random forest or support vector machine) is used to train an intelligent early warning model. Use Python's scikit-learn library to implement this process and set model parameters, such as the number and depth of trees. Train the model to identify abnormal performance changes that lead to a decrease in compaction quality. Use the trained model to predict real-time performance data and generate a system operation status early warning map.

[0141] Step S55: Based on the vibration system digital model and the system operation status warning diagram, the extreme working condition simulation of the roller vibration system is performed to obtain the vibration system extreme response feature set, and the parameter topology of the vibration system digital model is reconstructed according to the vibration system extreme response feature set to obtain the intelligent roller vibration system digital model.

[0142] Specifically, the digital model of the vibration system can be imported into ANSYS, and the extreme working conditions, such as maximum load, maximum temperature, and maximum vibration frequency, can be set. Run the extreme working condition simulation and monitor the system performance parameters, such as stress, deformation, and fatigue life. For example, it is found that under the maximum load, the stress of the vibration wheel exceeds the yield strength of the material, which indicates that the system needs to be optimized. These extreme response characteristics, including stress distribution, deformation, and fatigue damage parameters, are recorded to obtain the extreme response characteristic set of the vibration system. The parameter topology reconstruction of the digital model of the vibration system is performed using MATLAB's Optimization Toolbox. Define the optimization objectives, such as minimizing stress concentration and maximizing fatigue life, and set constraints, such as restrictions on material properties and geometric dimensions. Use topology optimization algorithms, such as evolutionary structural optimization (ESO) or level set method (LSM), to find the best material distribution and geometry. For example, set the number of iterations of the ESO algorithm to 100 and the penalty factor to 3 to optimize the design of the vibration wheel. After the algorithm is run, the digital model of the vibration system of the intelligent roller is obtained.

[0143] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0144] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for establishing a digital model of a roller vibration system, characterized in that: The following steps are involved: Step S1: obtaining an initial pavement material vibration characteristic data set; Perform multi-physics field coupling analysis on the initial pavement material vibration characteristic data set to obtain pavement material response characteristic data; wherein step S1 includes: Step S11: collecting vibration response signals of target pavement materials to obtain a pavement material vibration response signal set; Step S12: performing spectrum denoising on the pavement material vibration response signal set to obtain a purified pavement material vibration response signal set; Step S13: extracting microscopic structural features from the vibration response signal set of the purified pavement material to obtain a microscopic feature set of the pavement material vibration response; Step S14: performing frequency-strain correlation analysis on the material vibration response micro-feature set to obtain pavement material vibration sensitivity mapping data; wherein step S14 includes: Step S141: vectorizing the material vibration response microscopic feature set to obtain a pavement material vibration feature vector set; Step S142: Performing Fourier-wavelet composite transformation on the vibration characteristic vector set of the pavement material to obtain the material microscopic vibration frequency distribution data; Step S143: performing strain-frequency correlation identification on the material vibration response microscopic feature set based on the material microscopic vibration frequency distribution data to obtain pavement material vibration correlation mapping data; Step S144: constructing a material vibration sensitivity index system for the pavement material vibration correlation mapping data to obtain a pavement material vibration sensitivity evaluation index set; Step S145: quantifying the strain response of the pavement material at different frequencies on the pavement material vibration correlation mapping data according to the pavement material vibration sensitivity evaluation index set, obtaining the pavement material spectrum-strain response curve, and identifying the extreme points of the pavement material spectrum-strain response curve to obtain the pavement material vibration characteristic critical point set; Step S146: performing multi-scenario simulation verification on the critical point set of vibration characteristics of pavement materials to obtain material vibration sensitivity mapping data; Step S15: constructing a pavement material vibration characteristic classification standard based on the pavement material vibration sensitivity mapping data to obtain a pavement material vibration characteristic classification standard; Step S16: classifying the pavement material vibration response micro-feature set according to the pavement material vibration characteristic classification standard to obtain a classified pavement material vibration characteristic data set, and performing multi-physical field coupling analysis on the classified pavement material vibration characteristic data set to obtain pavement material response characteristic data; Step S2: Based on the pavement material response characteristic data, dynamic system modeling is performed on the vibration system of the road roller to obtain an initial digital model of the vibration system; dynamic characteristics are extracted from the initial digital model of the vibration system to obtain dynamic parameter mapping data of the vibration system; based on the dynamic parameter mapping data of the vibration system, parameter calibration is performed on the initial digital model of the vibration system to obtain a digital model of the vibration system; Step S3: constructing a control framework for the digital model of the vibration system to obtain a vibration system parameter control framework; controlling the compaction process of the roller vibration system based on the digital model of the vibration system and the vibration system parameter control framework to obtain a vibration system control model; Step S4: numerically simulate the compaction process of the target road surface material based on the vibration system control model to obtain a compaction process simulation model; quantitatively evaluate the compaction effect of the target road surface based on the compaction process simulation model to obtain a compaction effect evaluation index set; and modify the compaction process simulation model according to the compaction effect evaluation index set to obtain a modified compaction process numerical model; Step S5: construct a performance monitoring framework for the modified compaction process numerical model to obtain a vibration system performance monitoring framework; predict the state of the roller vibration system throughout its life cycle based on the vibration system performance monitoring framework to obtain a system operation status warning diagram; adjust the vibration system digital model according to the system operation status warning diagram to obtain an intelligent roller vibration system digital model.

2. The method for establishing a digital model of a road roller vibration system according to claim 1, characterized in that: Step S16 includes the following steps: Step S161: classifying the pavement material vibration response micro-feature set according to the pavement material vibration characteristic classification standard to obtain a classified pavement material vibration characteristic data set; Step S162: performing a vibration response comparison experiment on different material types of the target road surface according to the classified road surface material vibration characteristic data set to obtain a classified road surface material dynamic response data set; Step S163: Calculate the pavement material response data difference index for the classified pavement material dynamic response data set to obtain a pavement material vibration characteristic difference index set, and construct a database based on the classified pavement material dynamic response data set and the pavement material vibration characteristic difference index set to obtain a pavement material vibration characteristic difference database; Step S164: performing cross-scale correlation on the pavement material vibration characteristic difference database to obtain a pavement material vibration characteristic correlation diagram; Step S165: quantitatively describing the vibration characteristics of the target pavement material according to the pavement material vibration characteristic correlation diagram to obtain a pavement material vibration characteristic index set; Step S166: performing dynamic response clustering on the target pavement material according to the pavement material vibration characteristic index set to obtain pavement material vibration characteristic clustering data; Step S167: Perform principal component dimensionality reduction on the pavement material vibration characteristic clustering data to obtain a pavement material vibration characteristic mapping matrix, and perform eigenvalue reconstruction on the pavement material vibration characteristic mapping matrix to obtain pavement material response characteristic data.

3. The method for establishing a digital model of a road roller vibration system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing a vibration loading test on the vibration system of the road roller to obtain a vibration loading characteristic data set; Step S22: extracting the pavement material frequency domain characteristics from the pavement material response characteristic data to obtain a pavement material frequency spectrum characteristic data set; Step S23: performing feature alignment on the vibration loading characteristic data set and the pavement material spectrum characteristic data set to obtain an aligned vibration loading characteristic data set and an aligned pavement material spectrum characteristic data set, and performing feature fusion on the aligned vibration loading characteristic data set and the aligned pavement material spectrum characteristic data set to obtain a pavement material-roller coupling dynamic characteristic data set; Step S24: performing vibration system parameter identification based on the pavement material-roller coupling dynamic characteristic data set to obtain a roller-pavement coupling dynamic parameter set; Step S25: performing nonlinear dynamic modeling on the roller vibration system based on the roller-pavement coupling dynamic parameter set to obtain an initial digital model of the vibration system; Step S26: extracting dynamic features from the initial digital model of the vibration system to obtain dynamic parameter mapping data of the vibration system, and calibrating parameters of the initial digital model of the vibration system based on the dynamic parameter mapping data of the vibration system to obtain the digital model of the vibration system.

4. The method for establishing a digital model of a road roller vibration system according to claim 3, characterized in that: Step S26 includes the following steps: Step S261: Analyze the geometric constraints of the vibration system of the road roller to obtain a geometric parameter constraint set of the vibration system; Step S262: constructing dynamic boundary conditions for the roller vibration system according to the vibration system geometric parameter constraint set to obtain a system dynamic constraint framework; Step S263: Discrete modeling of the vibration system of the road roller based on the initial digital model of the vibration system and the system dynamics constraint framework to obtain a discretized model of the vibration system; Step S264: extracting eigenvalues ​​from the vibration system discretization model to obtain a vibration system eigenvalue set, and mapping the vibration system eigenvalue set with the roller vibration system to obtain vibration system dynamic parameter mapping data; Step S265: collecting real-time data of the vibration system of the roller using sensors to obtain a real-time working parameter set of the roller, and calculating correction parameters of the dynamic parameter mapping data of the vibration system according to the real-time working parameter set of the roller to obtain correction data of the vibration system parameters; Step S266: calibrate the parameters of the initial digital model of the vibration system according to the vibration system parameter correction data to obtain the digital model of the vibration system.

5. The method for establishing a digital model of a road roller vibration system according to claim 4, characterized in that: Step S261 includes the following steps: Step S2611: performing three-dimensional scanning on the vibration system of the road roller to obtain a three-dimensional model of the vibration system, and performing geometric feature extraction on the three-dimensional model of the vibration system to obtain a geometric feature set of the vibration system; Step S2612: reconstructing the vibration system geometric feature set by topological constraints to obtain the vibration system geometric constraint relationship data; Step S2613: extracting constraint conditions of the vibration system of the road roller according to the geometric constraint relationship data of the vibration system to obtain a set of geometric constraint indicators of the vibration system; Step S2614: performing kinematic constraint mapping on the roller vibration system according to the vibration system geometric constraint index set to obtain system motion freedom mapping data; Step S2615: performing constraint space topology modeling on the roller vibration system based on the system motion freedom mapping data to obtain a geometric constraint boundary framework of the vibration system; Step S2616: simulate the extreme working condition of the vibration system geometric constraint boundary framework to obtain the extreme value constraint condition set of the vibration system, and fine-tune the parameters of the initial digital model of the vibration system according to the extreme value constraint condition set of the vibration system to obtain the geometric parameter constraint set of the vibration system.

6. The method for establishing a digital model of a road roller vibration system according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: constructing a dynamic boundary of the roller vibration system based on the vibration system geometric parameter constraint set to obtain a vibration system control boundary; Step S32: constructing a dynamic threshold adjustment mechanism for the vibration system digital model based on the vibration system control boundary to obtain a vibration system parameter threshold adjustment mechanism; Step S33: performing multi-scenario simulation verification on the vibration system parameter threshold adjustment mechanism to obtain system parameter threshold adaptability evaluation data; Step S34: reconstructing the parameter space of the vibration system digital model according to the system parameter threshold adaptability evaluation data to obtain a vibration system parameter control framework; Step S35: performing control strategy iteration on the vibration system digital model according to the vibration system parameter control framework to obtain a vibration system control model.

7. The method for establishing a digital model of a road roller vibration system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: numerically simulating the compaction process of the target pavement material based on the vibration system control model to obtain an initial pavement compaction simulation model; Step S42: discretizing the pavement material microstructure on the pavement initial compaction simulation model to obtain a pavement material microgrid dynamic model; Step S43: constructing dynamic constraint conditions for the compaction process of the target pavement material according to the pavement material micro-grid dynamic model to obtain a pavement compaction process boundary condition set; Step S44: numerically simulating the compaction process of the target pavement material through the pavement compaction process boundary condition set to obtain a compaction process simulation model, and extracting compaction simulation data from the compaction process simulation model to obtain preliminary simulation data of the compaction process; Step S45: quantitatively evaluating the compaction effect of the target road surface based on the preliminary simulation data of the compaction process to obtain a set of compaction effect evaluation indicators, wherein the quantitative evaluation of the compaction effect includes a compaction uniformity indicator evaluation, a compaction density indicator evaluation, and a residual stress distribution probability evaluation; Step S46: modifying the compaction process simulation model according to the compaction effect evaluation index set to obtain a modified compaction process numerical model.

8. The method for establishing a digital model of a road roller vibration system according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: constructing system performance index dimensions for the modified compaction process numerical model to obtain a vibration system performance evaluation dimension set; Step S52: constructing a performance monitoring framework for the modified compaction process numerical model according to the vibration system performance evaluation dimension set to obtain a vibration system performance monitoring framework; Step S53: dynamically tracking the modified compaction process numerical model using the vibration system performance monitoring framework to obtain a vibration system performance change curve; Step S54: extracting system performance change points from the vibration system performance change curve to obtain vibration system performance mapping data, and performing intelligent early warning on the roller vibration system based on the vibration system performance mapping data to obtain a system operation status early warning diagram; Step S55: Based on the vibration system digital model and the system operation status warning diagram, the extreme working condition simulation of the roller vibration system is performed to obtain the vibration system extreme response feature set, and the parameter topology of the vibration system digital model is reconstructed according to the vibration system extreme response feature set to obtain the intelligent roller vibration system digital model.

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