Intelligent highway pavement disease monitoring system based on digital twinning

Abstract

The invention relates to the technical field of digital twinning, and discloses an intelligent highway pavement disease monitoring system based on digital twinning. Comprising a multi-dimensional data sensing module, a pavement disease feature extraction module, a disease recognition model establishment module, a virtual mapping modeling module, a digital twinborn disease prediction module and a real-time interaction feedback module, and is used for acquiring multi-dimensional data of different road sections of a highway pavement, and extracting crack contour information of a pavement image by adopting an edge detection algorithm; void areas between pavements of different road sections and a base layer are detected according to sound wave detection signals, a virtual three-dimensional model synchronously updated with an actual pavement state is constructed based on a finite element analysis method through a digital twin technology, and the risk of highway pavement diseases under the change trend of traffic flow pressure distribution is predicted. And intelligent monitoring and risk prediction of highway pavement diseases are realized.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and more specifically to an intelligent highway pavement defect monitoring system based on digital twin. Background Art

[0002] With the rapid development of expressway networks and the continued growth of traffic volume, the demand for intelligent pavement maintenance is becoming increasingly prominent, and intelligent pavement defect monitoring technology has gradually become a research hotspot. However, existing pavement defect monitoring technologies still have significant deficiencies in real-time performance, accuracy, and system integration, making it difficult to fully meet the demand for efficient monitoring under complex expressway conditions. They rely on manual inspections or regular inspections using vehicle-mounted LiDAR, which are costly, have low coverage, and are difficult to detect early hidden defects. If pavement defects such as cracks, rutting, and potholes are not addressed promptly, the cost of repair will increase exponentially.

[0003] However, existing pavement defect monitoring technologies still have significant deficiencies in real-time performance, accuracy, and system integration, making it difficult to fully meet the needs for efficient monitoring under complex highway conditions. For example, the patent with publication number CN114740010B proposes a pavement defect monitoring method based on laser ranging and image analysis, which generates distance-displacement curves and combines image processing technology to identify cracks, protrusions, and subsidence. However, this technical solution mainly relies on a single laser ranging and image analysis method, lacks the ability to fuse and process multi-source data, and is difficult to achieve all-round, high-precision monitoring of pavement defects. In addition, this method does not introduce digital twin technology and cannot build dynamic interaction between the physical world and the virtual model, thereby limiting its adaptability and predictive capabilities under complex road conditions.

[0004] Existing technologies still have significant shortcomings in multi-source data fusion, digital twin modeling, and comprehensive analysis of environmental factors, making them unable to meet the needs of modern intelligent highway maintenance. Therefore, there is an urgent need for an intelligent monitoring system that can achieve real-time interaction between the physical world and virtual models through digital twin technology, and combine multi-source data fusion analysis with dynamic modeling of environmental factors. This system can improve the accuracy, real-time nature, and predictive capabilities of pavement disease monitoring, providing technical support for efficient highway maintenance. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent monitoring system for highway pavement defects based on digital twins to solve the problems existing in the above-mentioned background technology.

[0006] The present invention provides the following technical solution: an intelligent highway pavement defect monitoring system based on digital twins, comprising: a multidimensional data perception module, a pavement defect feature extraction module, a defect identification model establishment module, a virtual mapping modeling module, a digital twin defect prediction module, and a real-time interactive feedback module;

[0007] The multi-dimensional data sensing module is used to obtain multi-dimensional data of different sections of the highway pavement, including a pavement image acquisition unit, an acoustic wave detection signal acquisition unit, and a traffic pressure distribution data acquisition unit;

[0008] The pavement disease feature extraction module uses an edge detection algorithm to extract crack contour information from the pavement image, analyzes the first risk index of highway pavement diseases, and analyzes the second risk index of highway pavement diseases based on the acoustic wave detection signal;

[0009] The disease identification model establishment module establishes a comprehensive early warning model based on the first risk index of highway pavement diseases and the second risk index of highway pavement diseases to perform intelligent monitoring of highway pavement diseases;

[0010] The virtual mapping modeling module constructs a virtual three-dimensional model based on the finite element analysis method that is updated synchronously with the actual road surface state, with traffic pressure distribution data as the X-axis, the first risk index of highway pavement disease as the Y-axis, and the second risk index of highway pavement disease as the Z-axis;

[0011] The digital twin disease prediction module predicts the risk of highway pavement diseases under the changing trend of traffic pressure distribution based on the virtual three-dimensional model;

[0012] The real-time interactive feedback module sends key data in the virtual model to the remote monitoring terminal through a high-speed communication network, and displays the location and size change trend of the abnormal area through a graphical interface.

[0013] Preferably, the multi-dimensional data perception module is used to obtain road condition information from multiple sources, including a road image acquisition unit, an acoustic wave detection signal acquisition unit, and a traffic pressure distribution data acquisition unit, and the total number of different road sections is n, i = 1, 2, 3, ..., n, where i represents the number of the different road sections;

[0014] The high-definition image acquisition unit is used to acquire road surface images of different sections of the road condition through a camera array installed along the highway;

[0015] The acoustic wave detection signal acquisition unit transmits high-frequency acoustic waves through an acoustic wave detection device and receives reflected signals to acquire acoustic wave detection signals from different sections of the highway and analyze whether there are cracks or cavities in the internal structure of the road surface;

[0016] The traffic pressure distribution data acquisition unit uses piezoelectric sensors buried under the highway pavement to collect traffic pressure distribution data of different road sections.

[0017] Preferably, the specific contents of the pavement disease feature extraction module are as follows:

[0018] Analyzing the first risk index of highway pavement diseases includes: a highway pavement disease identification unit, a pavement disease feature extraction unit, and a highway pavement disease first risk index analysis unit;

[0019] The analysis of the second risk index of highway pavement diseases includes: an acoustic wave detection signal extraction unit and a highway pavement disease second risk index analysis unit.

[0020] Preferably, the highway pavement damage identification unit: extracts pavement texture changes through image processing technology, identifies damage areas in different road sections, wherein the damage areas include crack areas, rutting areas, and subsidence areas, and classifies and marks the damage areas through machine learning algorithms;

[0021] The pavement disease feature extraction unit is configured to separate the diseased areas of different road sections from the background using edge detection, and transmit the separated diseased areas to the highway pavement disease first risk index analysis unit for risk analysis;

[0022] The highway pavement disease first risk index analysis unit calculates the highway pavement disease first risk index using a quantitative evaluation method based on the area, length, width and depth information of the diseased areas in different road sections.

[0023] Preferably, the acoustic wave detection signal extraction unit: uses a piezoelectric crystal transducer to emit acoustic waves of a specific frequency, the acoustic waves propagate and reflect in the road surface structure, the receiver captures the reflected signals, generates elastic wave signals through the reflected acoustic waves, and extracts acoustic wave detection signals of different road sections, the signals including: acoustic wave frequency, acoustic wave amplitude, and acoustic wave velocity;

[0024] Highway pavement disease second risk index analysis unit: Integrate the acoustic wave detection signals of different sections to build a risk assessment index system, detect the gaps between the pavement and the base layer in different sections, and calculate the highway pavement disease second risk index.

[0025] Preferably, the disease identification model establishment module analyzes the surface diseases of the highway and the gap between the highway pavement and the base layer according to the first risk index of highway pavement diseases and the second risk index of highway pavement diseases, and establishes a comprehensive early warning model. When the output result of the comprehensive early warning model is greater than the maximum value of the preset risk judgment interval, the judgment result is high risk; when the output result of the comprehensive early warning model is within the preset risk judgment interval, the judgment result is medium risk; when the output result of the comprehensive early warning model is less than the minimum value of the preset risk judgment interval, the judgment result is low risk, and the judgment result is transmitted to the virtual mapping modeling module and the real-time interactive feedback module.

[0026] Preferably, the virtual mapping modeling module includes a judgment result presenting unit and a virtual three-dimensional model building unit;

[0027] The judgment result presentation unit performs color coding on the risky road sections according to the risk level, wherein the red area indicates the road section with a high risk judgment result, the yellow area indicates the road section with a medium risk judgment result, and the green area indicates the road section with a low risk judgment result;

[0028] Virtual 3D model establishment unit: Based on the finite element analysis method, a virtual 3D model is constructed that is updated synchronously with the actual road surface status. The traffic pressure distribution data is used as the X-axis, the first risk index of highway pavement diseases is used as the Y-axis, and the second risk index of highway pavement diseases is used as the Z-axis to demonstrate the impact of traffic pressure distribution in different sections on the risk of highway pavement diseases.

[0029] Preferably, the specific contents of the digital twin disease prediction module are as follows:

[0030] Based on the time nodes of the disease evolution prediction, the historical traffic pressure data of different road sections are used to analyze the average daily traffic pressure values of different road sections. The time nodes of the disease evolution prediction include daily prediction, weekly prediction and monthly prediction;

[0031] When the time node for disease evolution prediction is daily prediction, the average daily traffic pressure values of different road sections are input into the virtual 3D model as X-axis data. The virtual 3D model outputs the first risk index and the second risk index of highway pavement disease, and returns the output results to the disease identification model establishment module for prediction risk judgment;

[0032] When the time node for disease evolution prediction is weekly, the product of the average daily traffic pressure value of different road sections and the number of days in a week is input into the virtual 3D model as the X-axis data. The virtual 3D model outputs the first risk index of highway pavement disease and the second risk index of highway pavement disease, and returns the output results to the disease identification model establishment module for prediction risk judgment;

[0033] When the time node for disease evolution prediction is monthly, the product of the average daily traffic pressure value of different road sections and the number of days in the month is input into the virtual three-dimensional model as the X-axis data. The virtual three-dimensional model outputs the first risk index of highway pavement diseases and the second risk index of highway pavement diseases, and returns the output results to the disease identification model establishment module for predictive risk judgment.

[0034] Preferably, the real-time interactive feedback module is used to receive the risk judgment results of the disease identification model establishment module, and issue risk warnings for highway pavement diseases based on the risk judgment results; send key data in the virtual model to the remote monitoring terminal through a high-speed communication network, and display the position and size change trends of abnormal areas through a graphical interface.

[0035] Technical effects and advantages of the present invention:

[0036] The present invention is equipped with a multidimensional data perception module, a pavement disease feature extraction module, a disease identification model establishment module, a virtual mapping modeling module, a digital twin disease prediction module and a real-time interactive feedback module to obtain multidimensional data of different sections of the highway pavement, use an edge detection algorithm to extract crack contour information from the pavement image, and detect the void areas between the pavement and the base layer in different sections based on the acoustic wave detection signal. Through digital twin technology and the finite element analysis method, a virtual three-dimensional model that is synchronized with the actual pavement status is constructed to predict the risk of highway pavement diseases under the changing trend of traffic pressure distribution, thereby realizing intelligent monitoring and risk prediction of highway pavement diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a structural diagram of an intelligent monitoring system for highway pavement defects based on digital twins. DETAILED DESCRIPTION

[0038] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The intelligent monitoring system for highway pavement defects based on digital twins involved in the present invention is not limited to the various structures described in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] like Figure 1 As shown, the present invention provides an intelligent highway pavement disease monitoring system based on digital twins, including: a multidimensional data perception module, a pavement disease feature extraction module, a disease identification model establishment module, a virtual mapping modeling module, a digital twin disease prediction module, and a real-time interactive feedback module;

[0040] The multi-dimensional data sensing module is used to obtain multi-dimensional data of different sections of the highway pavement, including a pavement image acquisition unit, an acoustic wave detection signal acquisition unit, and a traffic pressure distribution data acquisition unit;

[0041] The pavement disease feature extraction module uses an edge detection algorithm to extract crack contour information from the pavement image, analyzes the first risk index of highway pavement diseases, and analyzes the second risk index of highway pavement diseases based on the acoustic wave detection signal;

[0042] The disease identification model establishment module establishes a comprehensive early warning model based on the first risk index of highway pavement diseases and the second risk index of highway pavement diseases to perform intelligent monitoring of highway pavement diseases;

[0043] The virtual mapping modeling module constructs a virtual three-dimensional model based on the finite element analysis method that is updated synchronously with the actual road surface state, with traffic pressure distribution data as the X-axis, the first risk index of highway pavement disease as the Y-axis, and the second risk index of highway pavement disease as the Z-axis;

[0044] The digital twin disease prediction module predicts the risk of highway pavement diseases under the changing trend of traffic pressure distribution based on the virtual three-dimensional model;

[0045] The real-time interactive feedback module sends key data in the virtual model to the remote monitoring terminal through a high-speed communication network, and displays the location and size change trend of the abnormal area through a graphical interface.

[0046] In this embodiment, it should be specifically explained that the multi-dimensional data perception module is used to obtain road condition information from multiple sources, including a road image acquisition unit, an acoustic wave detection signal acquisition unit, and a vehicle flow pressure distribution data acquisition unit. The total number of different road sections is n, i = 1, 2, 3, ..., n, where i represents the number of the different road sections;

[0047] The high-definition image acquisition unit acquires road surface images of different sections of the road condition through a camera array installed along the highway. The camera adopts a high-resolution industrial camera and can capture detailed road surface information with millimeter-level accuracy.

[0048] The acoustic wave detection signal acquisition unit transmits high-frequency acoustic waves through an acoustic wave detection device and receives reflected signals to acquire acoustic wave detection signals from different sections of the highway and analyze whether there are cracks or cavities in the internal structure of the road surface;

[0049] The traffic pressure distribution data acquisition unit uses piezoelectric sensors buried under the highway pavement to collect traffic pressure distribution data of different road sections.

[0050] In this embodiment, it should be specifically explained that the specific contents of the pavement disease feature extraction module are as follows:

[0051] Analyzing the first risk index of highway pavement diseases includes: a highway pavement disease identification unit, a pavement disease feature extraction unit, and a highway pavement disease first risk index analysis unit;

[0052] The analysis of the second risk index of highway pavement diseases includes: an acoustic wave detection signal extraction unit and a highway pavement disease second risk index analysis unit.

[0053] In this embodiment, it should be specifically explained that the highway pavement defect identification unit: uses image processing technology to extract pavement texture changes and identify defect areas in different road sections. The defect areas include crack areas, rutting areas, and subsidence areas, and classifies and labels the defect areas through a machine learning algorithm. Transverse cracks: cracks perpendicular to the driving direction of the road, usually caused by temperature changes or foundation settlement. Longitudinal cracks: cracks parallel to the driving direction of the road, mostly caused by uneven settlement of the roadbed or improper construction joint treatment. Reticulated cracks: small cracks that intersect with each other to form a reticulated pattern, usually related to pavement aging and insufficient base strength. Rutting areas: permanent deformation of the pavement caused by repeated vehicle rolling, usually appearing in the wheel track area. Subsidence areas: sinking of local areas of the pavement, which may be caused by insufficient roadbed compaction or underground cavities.

[0054] The pavement disease feature extraction unit is configured to separate the diseased areas of different road sections from the background using edge detection, and transmit the separated diseased areas to the highway pavement disease first risk index analysis unit for risk analysis;

[0055] The specific algorithm of edge detection is: based on the grayscale gradient of each pixel in the horizontal and vertical directions in the road surface images of different road sections, the gradient amplitude of each pixel is calculated. The calculation formula is: Among them G i Represents the gradient amplitude of each pixel in the road surface image of different road sections, G ix Represents the grayscale gradient of each pixel in the horizontal direction in the road surface images of different road sections, G iy Represents the vertical grayscale gradient of each pixel in the road surface images of different road sections;

[0056] The gradient direction of each pixel is calculated based on the grayscale gradient of each pixel in the horizontal and vertical directions in the road surface images of different road sections. The calculation formula is: where θ i Represents the gradient direction of each pixel in the road surface images of different road sections;

[0057] The local maximum value is retained in the gradient direction, non-edge points are suppressed, high and low thresholds are used to segment edges, weak edges are connected through hysteresis threshold processing, and the diseased areas of different road sections are separated from the background.

[0058] The highway pavement disease first risk index analysis unit calculates the highway pavement disease first risk index using a quantitative assessment method based on the area, length, width, and depth information of the diseased areas in different road sections. The calculation formula is:

[0059] Among them, M i represents the first risk index of highway pavement disease, k represents the correction coefficient, s i Indicates the area of diseased areas in different road sections, S i Indicates the total area of different road sections, l i Indicates the length of the disease in different road sections, l imax Indicates the threshold of disease length of different road sections, w i Indicates the width of the disease in different road sections, w imax Indicates the width threshold of different road sections, d i Indicates the depth of disease in different road sections, d i0 represents the critical depth of damage in different road sections, ω1, ω2, ω3 and ω4 represent weight coefficients respectively, and their sum is 1.

[0060] In this embodiment, it should be specifically explained that the acoustic wave detection signal extraction unit: uses a piezoelectric crystal transducer to transmit acoustic waves of a specific frequency, the acoustic waves propagate and reflect in the road surface structure, the receiver captures the reflected signals, generates elastic wave signals through the reflected acoustic waves, and extracts acoustic wave detection signals of different road sections, the signals including: acoustic wave frequency, acoustic wave amplitude, and acoustic wave velocity;

[0061] Highway pavement disease second risk index analysis unit: This unit integrates acoustic wave detection signals from different road sections to build a risk assessment index system, detects gaps between the pavement and base layer in different sections, and calculates the highway pavement disease second risk index;

[0062] The sound wave frequency anomaly index of different road sections is calculated based on the measured sound wave frequency. The calculation formula is: in Indicates the abnormal index of sound wave frequency in different road sections, f i实测 Indicates the measured sound wave frequency at different road sections, f 健康 Indicates the sound wave frequency of a preset healthy road surface;

[0063] The sound wave amplitude attenuation index of different road sections is calculated based on the measured sound wave amplitude. The calculation formula is: Among them F ai Represents the acoustic wave amplitude attenuation index of different road sections, ai入射 represents the amplitude of the incident wave at different road sections, a i入射 Indicates the amplitude of reflected waves at different road sections;

[0064] The sound wave velocity variation index of different road sections is calculated based on the measured sound wave velocity. The calculation formula is: Among them F vi Indicates the sound wave velocity variation index of different road sections, v i实测 represents the measured sound wave velocity at different road sections, v 健康 Indicates the sound wave velocity of a preset healthy road surface;

[0065] Calculate the second risk index of highway pavement damage using the following formula: Among them U i represents the second risk index of highway pavement defects, e represents a constant, α1, α2 and α3 represent weight coefficients respectively, and their sum is 1.

[0066] In this embodiment, it should be specifically explained that the disease identification model establishment module analyzes the surface diseases of the highway and the gap between the highway pavement and the base layer according to the first risk index of highway pavement diseases and the second risk index of highway pavement diseases, and establishes a comprehensive early warning model. The expression of the comprehensive early warning model is: Z i =F1(M i )+F2(U i ), where Z i Represents the output value of the comprehensive early warning model, F1(M i ) represents the preset first judgment function, F2(U i ) represents the preset second judgment function; when the output result of the comprehensive early warning model is greater than the maximum value of the preset risk judgment interval, the judgment result is high risk; when the output result of the comprehensive early warning model is within the preset risk judgment interval, the judgment result is medium risk; when the output result of the comprehensive early warning model is less than the minimum value of the preset risk judgment interval, the judgment result is low risk, and the judgment result is transmitted to the virtual mapping modeling module and the real-time interactive feedback module.

[0067] In this embodiment, it should be specifically explained that the virtual mapping modeling module includes a judgment result presentation unit and a virtual three-dimensional model establishment unit;

[0068] The judgment result presentation unit performs color coding on the risky road sections according to the risk level, wherein the red area indicates the road section with a high risk judgment result, the yellow area indicates the road section with a medium risk judgment result, and the green area indicates the road section with a low risk judgment result;

[0069] Virtual 3D model establishment unit: Based on the finite element analysis method, a virtual 3D model is constructed that is updated synchronously with the actual road surface status. The traffic pressure distribution data is used as the X-axis, the first risk index of highway pavement diseases is used as the Y-axis, and the second risk index of highway pavement diseases is used as the Z-axis to demonstrate the impact of traffic pressure distribution in different sections on the risk of highway pavement diseases.

[0070] In this embodiment, it should be specifically explained that the specific contents of the digital twin disease prediction module are as follows:

[0071] Based on the time nodes of the disease evolution prediction, the historical traffic pressure data of different road sections are used to analyze the average daily traffic pressure values of different road sections. The time nodes of the disease evolution prediction include daily prediction, weekly prediction and monthly prediction;

[0072] When the time node for disease evolution prediction is daily prediction, the average daily traffic pressure values of different road sections are input into the virtual 3D model as X-axis data. The virtual 3D model outputs the first risk index and the second risk index of highway pavement disease, and returns the output results to the disease identification model establishment module for prediction risk judgment;

[0073] When the time node for disease evolution prediction is weekly, the product of the average daily traffic pressure value of different road sections and the number of days in a week is input into the virtual 3D model as the X-axis data. The virtual 3D model outputs the first risk index of highway pavement disease and the second risk index of highway pavement disease, and returns the output results to the disease identification model establishment module for prediction risk judgment;

[0074] When the time node for disease evolution prediction is monthly, the product of the average daily traffic pressure value of different road sections and the number of days in the month is input into the virtual three-dimensional model as the X-axis data. The virtual three-dimensional model outputs the first risk index of highway pavement diseases and the second risk index of highway pavement diseases, and returns the output results to the disease identification model establishment module for predictive risk judgment.

[0075] In this embodiment, it should be specifically explained that the real-time interactive feedback module is used to receive the risk judgment results of the disease identification model establishment module, and issue risk warnings for highway pavement diseases based on the risk judgment results; send key data in the virtual model to the remote monitoring terminal through the high-speed communication network, and display the position and size change trends of the abnormal area through a graphical interface.

[0076] In this embodiment, it should be specifically explained that the difference between this embodiment and the prior art lies in that this embodiment obtains multidimensional data of different sections of the highway pavement through a multidimensional data perception module, a pavement disease feature extraction module, a disease identification model establishment module, a virtual mapping modeling module, a digital twin disease prediction module and a real-time interactive feedback module, uses an edge detection algorithm to extract crack contour information from the pavement image, detects the void areas between the pavement and the base layer in different sections based on the acoustic wave detection signal, and constructs a virtual three-dimensional model based on the finite element analysis method through digital twin technology that is updated synchronously with the actual pavement status, predicts the risk of highway pavement diseases under the changing trend of traffic pressure distribution, and realizes intelligent monitoring and risk prediction of highway pavement diseases.

[0077] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0078] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent highway pavement defect monitoring system based on digital twins, characterized by: include: Multi-dimensional data perception module, pavement defect feature extraction module, defect identification model establishment module, virtual mapping modeling module, digital twin defect prediction module, and real-time interactive feedback module; The multi-dimensional data sensing module is used to obtain multi-dimensional data of different sections of the highway pavement, including a pavement image acquisition unit, an acoustic wave detection signal acquisition unit, and a traffic pressure distribution data acquisition unit; The pavement disease feature extraction module uses an edge detection algorithm to extract crack contour information from the pavement image, analyzes the first risk index of highway pavement diseases, and analyzes the second risk index of highway pavement diseases based on the acoustic wave detection signal; The disease identification model establishment module establishes a comprehensive early warning model based on the first risk index of highway pavement diseases and the second risk index of highway pavement diseases to perform intelligent monitoring of highway pavement diseases; The virtual mapping modeling module constructs a virtual three-dimensional model based on the finite element analysis method that is updated synchronously with the actual road surface state, with traffic pressure distribution data as the X-axis, the first risk index of highway pavement disease as the Y-axis, and the second risk index of highway pavement disease as the Z-axis; The digital twin disease prediction module predicts the risk of highway pavement diseases under the changing trend of traffic pressure distribution based on the virtual three-dimensional model; The real-time interactive feedback module sends key data in the virtual model to the remote monitoring terminal through a high-speed communication network, and displays the location and size change trend of the abnormal area through a graphical interface.

2. The digital twin-based intelligent highway pavement defect monitoring system according to claim 1 is characterized by: The multi-dimensional data perception module is used to obtain road condition information from multiple sources, including a road image acquisition unit, an acoustic wave detection signal acquisition unit, and a traffic pressure distribution data acquisition unit. The total number of different road sections is n, i = 1, 2, 3, ..., n, where i represents the number of the different road sections; The high-definition image acquisition unit is used to acquire road surface images of different sections of the road condition through a camera array installed along the highway; The acoustic wave detection signal acquisition unit transmits high-frequency acoustic waves through an acoustic wave detection device and receives reflected signals to acquire acoustic wave detection signals from different sections of the highway and analyze whether there are cracks or cavities in the internal structure of the road surface; The traffic pressure distribution data acquisition unit uses piezoelectric sensors buried under the highway pavement to collect traffic pressure distribution data of different road sections.

3. The digital twin-based intelligent highway pavement defect monitoring system according to claim 1 is characterized by: The specific contents of the pavement disease feature extraction module are as follows: Analyzing the first risk index of highway pavement diseases includes: a highway pavement disease identification unit, a pavement disease feature extraction unit, and a highway pavement disease first risk index analysis unit; The analysis of the second risk index of highway pavement diseases includes: an acoustic wave detection signal extraction unit and a highway pavement disease second risk index analysis unit.

4. The digital twin-based intelligent highway pavement defect monitoring system according to claim 3 is characterized by: The highway pavement defect recognition unit extracts pavement texture changes through image processing technology, identifies defect areas in different road sections, including crack areas, rutting areas, and subsidence areas, and classifies and labels the defect areas through machine learning algorithms. The pavement disease feature extraction unit is configured to separate the diseased areas of different road sections from the background using edge detection, and transmit the separated diseased areas to the highway pavement disease first risk index analysis unit for risk analysis; The highway pavement disease first risk index analysis unit calculates the highway pavement disease first risk index using a quantitative evaluation method based on the area, length, width and depth information of the diseased areas in different road sections.

5. The digital twin-based intelligent highway pavement defect monitoring system according to claim 3 is characterized by: The acoustic wave detection signal extraction unit uses a piezoelectric crystal transducer to emit acoustic waves of a specific frequency. The acoustic waves propagate and reflect in the road surface structure. The receiver captures the reflected signals, generates elastic wave signals through the reflected acoustic waves, and extracts acoustic wave detection signals of different road sections. The signals include: acoustic wave frequency, acoustic wave amplitude, and acoustic wave velocity. Highway pavement disease second risk index analysis unit: Integrate the acoustic wave detection signals of different sections to build a risk assessment index system, detect the gaps between the pavement and the base layer in different sections, and calculate the highway pavement disease second risk index.

6. The digital twin-based intelligent highway pavement defect monitoring system according to claim 1 is characterized by: The disease identification model establishment module analyzes the surface diseases of the highway and the gap between the highway pavement and the base layer according to the first risk index of the highway pavement disease and the second risk index of the highway pavement disease, and establishes a comprehensive early warning model. When the output result of the comprehensive early warning model is greater than the maximum value of the preset risk judgment interval, the judgment result is high risk; when the output result of the comprehensive early warning model is within the preset risk judgment interval, the judgment result is medium risk; when the output result of the comprehensive early warning model is less than the minimum value of the preset risk judgment interval, the judgment result is low risk, and the judgment result is transmitted to the virtual mapping modeling module and the real-time interactive feedback module.

7. The digital twin-based intelligent highway pavement defect monitoring system according to claim 1 is characterized by: The virtual mapping modeling module includes a judgment result presentation unit and a virtual three-dimensional model establishment unit; The judgment result presentation unit performs color coding on the risky road sections according to the risk level, wherein the red area indicates the road section with a high risk judgment result, the yellow area indicates the road section with a medium risk judgment result, and the green area indicates the road section with a low risk judgment result; Virtual 3D model establishment unit: Based on the finite element analysis method, a virtual 3D model is constructed that is updated synchronously with the actual road surface status. The traffic pressure distribution data is used as the X-axis, the first risk index of highway pavement diseases is used as the Y-axis, and the second risk index of highway pavement diseases is used as the Z-axis to demonstrate the impact of traffic pressure distribution in different sections on the risk of highway pavement diseases.

8. The digital twin-based intelligent highway pavement defect monitoring system according to claim 1 is characterized by: The specific contents of the digital twin disease prediction module are as follows: Based on the time nodes of the disease evolution prediction, the historical traffic pressure data of different road sections are used to analyze the average daily traffic pressure values of different road sections. The time nodes of the disease evolution prediction include daily prediction, weekly prediction and monthly prediction; When the time node for disease evolution prediction is daily prediction, the average daily traffic pressure values of different road sections are input into the virtual 3D model as X-axis data. The virtual 3D model outputs the first risk index and the second risk index of highway pavement disease, and returns the output results to the disease identification model establishment module for prediction risk judgment; When the time node for disease evolution prediction is weekly, the product of the average daily traffic pressure value of different road sections and the number of days in a week is input into the virtual 3D model as the X-axis data. The virtual 3D model outputs the first risk index of highway pavement disease and the second risk index of highway pavement disease, and returns the output results to the disease identification model establishment module for prediction risk judgment; When the time node for disease evolution prediction is monthly, the product of the average daily traffic pressure value of different road sections and the number of days in the month is input into the virtual three-dimensional model as the X-axis data. The virtual three-dimensional model outputs the first risk index of highway pavement diseases and the second risk index of highway pavement diseases, and returns the output results to the disease identification model establishment module for predictive risk judgment.

9. The digital twin-based intelligent highway pavement defect monitoring system according to claim 1 is characterized by: The real-time interactive feedback module is used to receive the risk judgment results of the disease identification model establishment module and issue risk warnings for highway pavement diseases based on the risk judgment results; The key data in the virtual model is sent to the remote monitoring terminal through a high-speed communication network, and the location and size change trend of the abnormal area are displayed through a graphical interface.

Images