Digital twin simulation modeling system and method for asphalt pavement repair

Abstract

This invention discloses a digital twin simulation system and method for asphalt pavement repair, relating to the field of pavement repair technology. It includes: a data acquisition module for collecting multi-source data on the asphalt pavement and traffic data; a digital twin modeling module for constructing a digital twin model of the asphalt pavement using 3D modeling technology; a repair scheme design module for outputting asphalt pavement repair schemes based on a trained repair scheme recommendation model; a simulation module for simulating the designed repair schemes on the digital twin model and generating simulation result data; and a scheme evaluation module for evaluating the results data from the simulation module based on a trained evaluation model and providing an evaluation score for the scheme. This invention optimizes asphalt pavement repair schemes through real-time data acquisition and digital twin modeling, analyzes the impact of mechanics, material aging, and traffic, improves repair accuracy, enhances construction safety, and increases efficiency.

Description

Technical Field

[0001] This invention relates to the field of pavement repair technology, specifically to a digital twin simulation system and method for asphalt pavement repair. Background Technology

[0002] With the acceleration of urbanization, asphalt pavement, as one of the main infrastructures of transportation, bears an increasing traffic load. Asphalt pavement suffers from a variety of defects, including cracks, potholes, and ruts. The occurrence of these defects is closely related to factors such as traffic flow, climate change, and service life. Traditional pavement repair methods often rely on manual inspection and repair, which is time-consuming, inefficient, costly, and makes it difficult to accurately predict the repair results. Therefore, how to improve the efficiency, accuracy, and intelligence level of asphalt pavement repair has become an urgent problem to be solved in the field of pavement engineering.

[0003] Digital twin technology, as an emerging virtual reality technology, has been widely used in various engineering fields. It can monitor, analyze and optimize the behavior of physical objects in real time by creating virtual models that correspond to actual physical objects. For asphalt pavement repair, combining digital twin technology can effectively realize real-time monitoring of pavement conditions, intelligent design and optimization of repair plans, and greatly improve the accuracy and efficiency of the repair process. Summary of the Invention

[0004] To address the aforementioned technical problems, a digital twin simulation system and method for asphalt pavement repair are provided. This technical solution solves the problems mentioned above.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A digital twin simulation system for asphalt pavement repair includes:

[0007] Data acquisition module: used to collect multi-source data on asphalt pavement and traffic data;

[0008] Digital twin modeling module: Electrically connected to the data acquisition module, it receives the acquired data, uses 3D modeling technology to construct a digital twin model of the asphalt pavement, presents the actual state of the pavement in digital form, and updates the model in real time based on the acquired data.

[0009] Repair scheme design module: Based on the digital twin model, combined with the type and severity of asphalt pavement defects and traffic demand, and based on the trained repair scheme recommendation model, outputs asphalt pavement repair schemes;

[0010] Simulation module: Connected to the digital twin modeling module and the repair scheme design module, it simulates the designed repair scheme on the digital twin model, simulates the changes in the mechanical properties of the road surface, the material aging process, and the impact of traffic during the repair process, and generates simulation result data;

[0011] Solution Evaluation Module: Receives the result data from the simulation module, evaluates the result data based on the trained evaluation model, and provides an evaluation score for the solution. Based on the evaluation score, it determines whether the solution meets the expected performance standard. If it does, it outputs the asphalt pavement repair solution for construction personnel to refer to and implement. If it does not meet the standard, it adjusts the asphalt pavement repair solution.

[0012] Preferably, the digital twin modeling module specifically includes:

[0013] Data receiving and preprocessing unit: Receives multi-source data of asphalt pavement from the data acquisition module, including pavement geometry, cracks, potholes, temperature and humidity, and traffic flow data. Cleans, verifies, and formats the received data to remove noise.

[0014] 3D modeling technology application unit: Based on the data of road surface geometry, use CAD modeling tools to generate a basic geometric model of the road surface, add crack and pothole damage data to the basic model, generate a digital twin model, and show the distribution and severity of road surface damage;

[0015] Materials and Environment Modeling Unit: Combines the material parameters of asphalt pavement with environmental factors to form a virtual model that is consistent with the actual pavement behavior, combining 3D modeling technology with virtual reality technology;

[0016] Real-time data updates: Through real-time connection with the data acquisition module, the digital twin model updates in real time based on newly acquired data, and dynamically corrects the road surface condition in the model according to changes in the external environment and the actual road surface condition.

[0017] Preferably, the repair scheme design module specifically includes:

[0018] Disease identification and classification unit: Based on the data in the digital twin model, the types of diseases of asphalt pavement, as well as the distribution and severity of diseases, are obtained, different types of diseases are classified, and their locations and impact ranges are marked in the model;

[0019] Repair Demand Analysis Unit: Combining traffic flow data, it obtains the road's traffic load and demand. Based on the road surface usage, the severity of damage, and traffic demand, it determines whether the road surface needs comprehensive repair, partial repair, or reinforcement only.

[0020] Repair process selection unit: Based on the type and severity of pavement distress, and using a pre-set repair process library, select the corresponding repair process and plan the construction process according to the selected repair process.

[0021] Preferably, the repair requirement analysis unit specifically includes:

[0022] A comprehensive repair demand analysis model is established, which combines traffic flow, road surface damage severity, and road surface usage to determine whether comprehensive repair, partial repair, and reinforcement measures are needed. The weights of different factors are obtained based on the analytic hierarchy process, and a factor judgment matrix is ​​constructed. The weight of each factor is obtained by calculating the matrix. The weight of each factor is used as input, and the repair demand score is used as output.

[0023] The formula for the repair requirements analysis model is as follows:

[0024] S = V T ·T+H J ·J+K L ·L

[0025] In the formula, V T It is the weight of traffic flow, where T is the traffic flow data, and H is the weight of traffic flow. J It is the weight of the severity of the disease, J is the severity of the disease, K L It represents the weight of usage, where L is the usage.

[0026] By analyzing historical scoring data under the same conditions for different types of repairs, regression analysis is used to obtain the threshold for repair demand scores. The repair demand scores are then compared with the thresholds to determine whether the road surface requires comprehensive repair, partial repair, or reinforcement measures.

[0027] Preferably, the simulation module specifically includes:

[0028] Mechanical performance simulation unit: Based on the existing geometry and material properties of the road surface, a finite element model is constructed, traffic load data is input, the impact of traffic flow on the road surface is simulated, the stress of the road surface under different repair schemes is simulated, and the improvement of the repair effect on the mechanical performance of the road surface is evaluated.

[0029] Material Aging and Damage Simulation Unit: Based on temperature and humidity data and the service life of the road surface, the aging process of asphalt materials is simulated. Combined with the type and severity of the damage, the impact of the repair process on the material performance is analyzed, and the aging and performance changes of the materials before and after repair are compared.

[0030] Traffic Impact Simulation Unit: Simulates traffic conditions when different repair schemes are implemented. Based on real-time climate data, environmental factors are introduced into the simulation model to simulate the impact of environmental factors on pavement mechanical properties, material aging, and repair effects.

[0031] Multi-scenario simulation unit: Based on different traffic flow and temperature and humidity conditions, it simulates different repair schemes, compares the effects of each repair scheme through simulation of multiple scenarios, and selects the optimal scheme.

[0032] Preferably, the scheme evaluation module specifically includes:

[0033] Simulation Result Evaluation Unit: Receives simulation result data of the repair scheme from the simulation module, analyzes and evaluates the simulation results using a pre-trained evaluation model, quantifies the evaluation results, and obtains an evaluation score;

[0034] Expected effect judgment unit: By comparing the evaluation score with the preset standard, it determines whether the repair plan meets the expected effect standard. If the plan meets the standard, it continues to be implemented. If it does not meet the standard, the repair plan is adjusted and the repair plan is redesigned and simulated until the plan meets the standard.

[0035] Preferably, the step of receiving simulation result data of the repair scheme from the simulation module, analyzing and evaluating the simulation results using a pre-trained evaluation model, quantifying the evaluation results, and obtaining an evaluation score specifically includes:

[0036] The evaluation model formula is as follows:

[0037]

[0038] In the formula, G is the total evaluation score, and f i w is the value of the i-th evaluation factor. i Here, is the weight of the i-th factor, n is the number of evaluation factors considered, D is the judgment result, and G is the weight of the ith factor. K This is the preset standard score.

[0039] Digital twin simulation methods for asphalt pavement repair include:

[0040] S1: Data acquisition, obtaining asphalt pavement and traffic data, including pavement geometry, damage types, traffic flow, and temperature and humidity information;

[0041] S2: Digital twin modeling, which uses 3D modeling and virtual reality technologies to generate a digital twin model of asphalt pavement and updates it in real time;

[0042] S3: Repair scheme design, based on digital twin model for disease identification and classification, combined with traffic load and demand analysis, to select repair technology;

[0043] S4: Simulation simulation, which simulates the repair plan on a digital twin model, simulates mechanical properties, material aging and traffic impact, and generates simulation results;

[0044] S5: Scheme evaluation. Based on the simulation results, the evaluation model is used to evaluate the scheme, give the scheme a score, and determine whether it meets the expected effect. If it does not meet the expected effect, the scheme is adjusted and the simulation is repeated until the standard is met.

[0045] Preferably, S2 specifically includes:

[0046] S201: Based on road surface geometry data, a basic three-dimensional road surface model is generated using CAD modeling tools;

[0047] S202: Integrate pavement distress type and severity data with the basic model to generate a digital twin model;

[0048] S203: Combine road surface material properties with environmental factors to create a virtual model that conforms to the actual situation and simulates the actual behavior of the road surface;

[0049] S204: Based on the real-time data update mechanism, correct and update the digital twin model in real time.

[0050] Preferably, S3 specifically includes:

[0051] S301: Based on data from the digital twin model, automatically identify the types of defects in asphalt pavement, classify the defects, and mark the location and extent of their impact.

[0052] S302: Combining traffic flow data and road surface usage, analyze the severity of road surface defects, assess whether comprehensive repair, partial repair, or reinforcement is required, determine the optimal repair requirements, and output a repair requirement score.

[0053] S303: Select the repair process based on the type and severity of pavement defects, refer to the pre-set repair process library, formulate a construction plan, and plan the construction process.

[0054] S304: Optimize the selection of repair processes based on the results of the repair needs analysis.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] This invention proposes to ensure the accuracy and timeliness of asphalt pavement repair schemes through real-time data acquisition and digital twin modeling. It optimizes repair schemes through multi-scenario simulation, analyzes mechanical properties, material aging, and traffic impacts to improve repair accuracy. The scheme evaluation module reduces costs and resource waste, identifies problems in advance to improve construction safety, and integrates automated management to simplify traditional operations, improve efficiency, and update pavement conditions in real time to adapt to external changes, ensuring the flexibility and timeliness of repair schemes. Attached Figure Description

[0057] Figure 1 This is a system framework diagram of the present invention;

[0058] Figure 2 This is a flowchart illustrating the steps of the present invention. Detailed Implementation

[0059] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0060] Reference Figure 1 As shown, a digital twin simulation system for asphalt pavement repair includes:

[0061] Data acquisition module: used to collect multi-source data on asphalt pavement and traffic data;

[0062] Digital twin modeling module: Electrically connected to the data acquisition module, it receives the acquired data, uses 3D modeling technology to construct a digital twin model of the asphalt pavement, presents the actual state of the pavement in digital form, and updates the model in real time based on the acquired data.

[0063] Repair scheme design module: Based on the digital twin model, combined with the type and severity of asphalt pavement defects and traffic demand, and based on the trained repair scheme recommendation model, outputs asphalt pavement repair schemes;

[0064] Simulation module: Connected to the digital twin modeling module and the repair scheme design module, it simulates the designed repair scheme on the digital twin model, simulates the changes in the mechanical properties of the road surface, the material aging process, and the impact of traffic during the repair process, and generates simulation result data;

[0065] Solution Evaluation Module: Receives the result data from the simulation module, evaluates the result data based on the trained evaluation model, and provides an evaluation score for the solution. Based on the evaluation score, it determines whether the solution meets the expected performance standard. If it does, it outputs the asphalt pavement repair solution for construction personnel to refer to and implement. If it does not meet the standard, it adjusts the asphalt pavement repair solution.

[0066] The digital twin modeling module specifically includes:

[0067] Data receiving and preprocessing unit: Receives multi-source data of asphalt pavement from the data acquisition module, including pavement geometry, cracks, potholes, temperature and humidity, and traffic flow data. Cleans, verifies, and formats the received data to remove noise.

[0068] 3D modeling technology application unit: Based on the data of road surface geometry, use CAD modeling tools to generate a basic geometric model of the road surface, add crack and pothole damage data to the basic model, generate a digital twin model, and show the distribution and severity of road surface damage;

[0069] Materials and Environment Modeling Unit: Combines the material parameters of asphalt pavement with environmental factors to form a virtual model that is consistent with the actual pavement behavior, combining 3D modeling technology with virtual reality technology;

[0070] Real-time data updates: Through real-time connection with the data acquisition module, the digital twin model updates in real time based on newly acquired data, and dynamically corrects the road surface condition in the model according to changes in the external environment and the actual road surface condition.

[0071] By employing CAD modeling and virtual reality technologies, the pavement modeling process not only displays the types, distribution, and severity of pavement defects, but also dynamically updates the digital model based on changes in the external environment and pavement conditions. In particular, in the material and environment modeling unit, by combining actual material properties and environmental factors, a highly realistic virtual simulation of pavement behavior is provided.

[0072] The repair solution design module specifically includes:

[0073] Disease identification and classification unit: Based on the data in the digital twin model, the types of diseases of asphalt pavement, as well as the distribution and severity of diseases, are obtained, different types of diseases are classified, and their locations and impact ranges are marked in the model;

[0074] Repair Demand Analysis Unit: Combining traffic flow data, it obtains the road's traffic load and demand. Based on the road surface usage, the severity of damage, and traffic demand, it determines whether the road surface needs comprehensive repair, partial repair, or reinforcement only.

[0075] Repair process selection unit: Based on the type and severity of pavement distress, and using a pre-set repair process library, select the corresponding repair process and plan the construction process according to the selected repair process.

[0076] The repair requirements analysis unit specifically includes:

[0077] A comprehensive repair demand analysis model is established, which combines traffic flow, road surface damage severity, and road surface usage to determine whether comprehensive repair, partial repair, and reinforcement measures are needed. The weights of different factors are obtained based on the analytic hierarchy process, and a factor judgment matrix is ​​constructed. The weight of each factor is obtained by calculating the matrix. The weight of each factor is used as input, and the repair demand score is used as output.

[0078] By analyzing historical scoring data under the same conditions for different types of repairs, regression analysis is used to obtain the threshold for repair demand scores. The repair demand scores are then compared with the thresholds to determine whether the road surface needs comprehensive repair, partial repair, or reinforcement measures.

[0079] By introducing the analytic hierarchy process (AHP), which comprehensively considers traffic flow, severity of damage, and road surface condition, and uses a scientific weighting system, the necessity of different repair measures is assessed. A threshold model is established through regression analysis to effectively determine whether the road surface needs comprehensive repair, partial repair, or reinforcement, greatly improving the decision-making accuracy of repair schemes.

[0080] The simulation module specifically includes:

[0081] Mechanical performance simulation unit: Based on the existing geometry and material properties of the road surface, a finite element model is constructed, traffic load data is input, the impact of traffic flow on the road surface is simulated, the stress of the road surface under different repair schemes is simulated, and the improvement of the repair effect on the mechanical performance of the road surface is evaluated.

[0082] Material Aging and Damage Simulation Unit: Based on temperature and humidity data and the service life of the road surface, the aging process of asphalt materials is simulated. Combined with the type and severity of the damage, the impact of the repair process on the material performance is analyzed, and the aging and performance changes of the materials before and after repair are compared.

[0083] Traffic Impact Simulation Unit: Simulates traffic conditions when different repair schemes are implemented. Based on real-time climate data, environmental factors are introduced into the simulation model to simulate the impact of environmental factors on pavement mechanical properties, material aging, and repair effects.

[0084] Multi-scenario simulation unit: Based on different traffic flow and temperature and humidity conditions, it simulates different repair schemes, compares the effects of each repair scheme through simulation of multiple scenarios, and selects the optimal scheme.

[0085] In the multi-scenario simulation unit, the mechanical properties, material aging process and traffic conditions under different repair schemes are simulated. Environmental factors are introduced into the simulation model to improve the accuracy and practicality of the simulation. This module can perform multi-scenario simulations under different traffic conditions and climate changes, providing multi-dimensional support for formulating the optimal repair scheme.

[0086] The solution evaluation module specifically includes:

[0087] Simulation Result Evaluation Unit: Receives simulation result data of the repair scheme from the simulation module, analyzes and evaluates the simulation results using a pre-trained evaluation model, quantifies the evaluation results, and obtains an evaluation score;

[0088] Expected effect judgment unit: By comparing the evaluation score with the preset standard, it determines whether the repair plan meets the expected effect standard. If the plan meets the standard, it continues to be implemented. If it does not meet the standard, the repair plan is adjusted and the repair plan design and simulation are carried out again until the plan meets the standard.

[0089] By combining the results data from the simulation module and using the trained evaluation model, quantitative analysis and scoring are performed to effectively assess the feasibility and effectiveness of the repair plan. By comparing the evaluation score with the preset standard, it is ensured that the repair effect of the plan meets the expected requirements. If the standard is not met, the plan is automatically adjusted and re-simulated to ensure continuous optimization.

[0090] The simulation module receives simulation result data of the repair plan, analyzes and evaluates the simulation results using a pre-trained evaluation model, quantifies the evaluation results, and obtains an evaluation score. Specifically, this includes:

[0091] The evaluation model formula is as follows:

[0092]

[0093] In the formula, G is the total assessment score, and f i w is the value of the i-th evaluation factor. i Here, is the weight of the i-th factor, n is the number of evaluation factors considered, D is the judgment result, and G is the weight of the ith factor. K This is the preset standard score.

[0094] Digital twin simulation methods for asphalt pavement repair include:

[0095] S1: Data acquisition, obtaining asphalt pavement and traffic data, including pavement geometry, damage types, traffic flow, and temperature and humidity information;

[0096] S2: Digital twin modeling, which uses 3D modeling and virtual reality technologies to generate a digital twin model of asphalt pavement and updates it in real time;

[0097] S3: Repair scheme design, based on digital twin model for disease identification and classification, combined with traffic load and demand analysis, to select repair technology;

[0098] S4: Simulation simulation, which simulates the repair plan on a digital twin model, simulates mechanical properties, material aging and traffic impact, and generates simulation results;

[0099] S5: Scheme evaluation. Based on the simulation results, the evaluation model is used to evaluate the scheme, give the scheme a score, and determine whether it meets the expected effect. If it does not meet the expected effect, the scheme is adjusted and the simulation is repeated until the standard is met.

[0100] S2 specifically includes:

[0101] S201: Based on road surface geometry data, a basic three-dimensional road surface model is generated using CAD modeling tools;

[0102] S202: Integrate pavement distress type and severity data with the basic model to generate a digital twin model;

[0103] S203: Combine road surface material properties with environmental factors to create a virtual model that conforms to the actual situation and simulates the actual behavior of the road surface;

[0104] S204: Based on the real-time data update mechanism, correct and update the digital twin model in real time.

[0105] S3 specifically includes:

[0106] S301: Based on data from the digital twin model, automatically identify the types of defects in asphalt pavement, classify the defects, and mark the location and extent of their impact.

[0107] S302: Combining traffic flow data and road surface usage, analyze the severity of road surface defects, assess whether comprehensive repair, partial repair, or reinforcement is required, determine the optimal repair requirements, and output a repair requirement score.

[0108] S303: Select the repair process based on the type and severity of pavement defects, refer to the pre-set repair process library, formulate a construction plan, and plan the construction process.

[0109] S304: Optimize the selection of repair processes based on the results of the repair needs analysis.

[0110] In summary, the advantages of this invention are:

[0111] The data acquisition module acquires asphalt pavement and traffic flow data in real time, and the digital twin modeling module can generate and update the pavement digital model in an instant, ensuring the accuracy and real-time nature of the repair plan. The repair plan design module combines pavement defects, traffic demand and environmental factors to intelligently recommend the best repair plan, which greatly improves the accuracy and efficiency of the repair.

[0112] The simulation module analyzes mechanical properties, material aging processes, and traffic impacts through multi-scenario simulations, providing a scientific basis for construction plans. This helps to identify potential problems in advance and optimize them. The multi-dimensional evaluation of repair effects can help decision-makers determine the feasibility of different repair plans and the best time to implement them, ensuring the efficient and accurate execution of road repair work.

[0113] The solution evaluation module, by comparing with historical data and based on scientific repair needs analysis and repair process selection, can significantly reduce trial and error costs, avoid unnecessary repeated repairs, and reduce resource waste during construction. At the same time, through simulation, it can identify and solve potential problems before actual construction, reducing potential safety hazards during construction.

[0114] The entire system integrates modules such as automatic data acquisition, repair scheme design, simulation, and scheme evaluation, realizing intelligent management of asphalt pavement repair. Through this integrated system, managers can view data analysis results and evaluate the effectiveness of repair schemes in real time on the platform, greatly simplifying the complexity of traditional manual operation and on-site decision-making and improving work efficiency.

[0115] Through real-time data updates and digital twin model correction, the system can dynamically adjust the road surface condition model to adapt to changes in external factors such as traffic and climate, ensuring that the repair plan is always adjusted based on the latest road conditions, further improving the real-time performance and flexibility of road surface repair.

[0116] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A digital twin simulation system for asphalt pavement repair, characterized in that, include: Data acquisition module: used to collect multi-source data on asphalt pavement and traffic data; Digital twin modeling module: Electrically connected to the data acquisition module, it receives the acquired data, uses 3D modeling technology to construct a digital twin model of the asphalt pavement, presents the actual state of the pavement in digital form, and updates the model in real time based on the acquired data. Repair scheme design module: Based on the digital twin model, combined with the type and severity of asphalt pavement defects and traffic demand, and based on the trained repair scheme recommendation model, outputs asphalt pavement repair schemes; Simulation module: Connected to the digital twin modeling module and the repair scheme design module, it simulates the designed repair scheme on the digital twin model, simulates the changes in the mechanical properties of the road surface, the material aging process, and the impact of traffic during the repair process, and generates simulation result data; Solution Evaluation Module: Receives the result data from the simulation module, evaluates the result data based on the trained evaluation model, and gives the solution an evaluation score. Based on the evaluation score, it determines whether the solution meets the expected effect standard. If it does, it outputs the asphalt pavement repair solution for construction personnel to refer to and implement. If it does not meet the standard, it adjusts the asphalt pavement repair solution. The digital twin modeling module specifically includes: Data receiving and preprocessing unit: Receives multi-source data of asphalt pavement from the data acquisition module, including pavement geometry, cracks, potholes, temperature and humidity, and traffic flow data. Cleans, verifies, and formats the received data to remove noise. 3D modeling technology application unit: Based on the data of road surface geometry, use CAD modeling tools to generate a basic geometric model of the road surface, add crack and pothole damage data to the basic model, generate a digital twin model, and show the distribution and severity of road surface damage; Materials and Environment Modeling Unit: Combines the material parameters of asphalt pavement with environmental factors to form a virtual model that is consistent with the actual pavement behavior, combining 3D modeling technology with virtual reality technology; Real-time data updates: Through real-time connection with the data acquisition module, the digital twin model updates in real time based on newly acquired data, and dynamically corrects the road surface condition in the model according to changes in the external environment and the actual road surface condition.

2. The digital twin simulation system for asphalt pavement repair according to claim 1, characterized in that, The repair scheme design module specifically includes: Disease identification and classification unit: Based on the data in the digital twin model, the types of diseases of asphalt pavement, as well as the distribution and severity of diseases, are obtained, different types of diseases are classified, and their locations and impact ranges are marked in the model; Repair Demand Analysis Unit: Combining traffic flow data, it obtains the road's traffic load and demand. Based on the road surface usage, the severity of damage, and traffic demand, it determines whether the road surface needs comprehensive repair, partial repair, or reinforcement only. Repair process selection unit: Based on the type and severity of pavement distress, and using a pre-set repair process library, select the corresponding repair process and plan the construction process according to the selected repair process.

3. The digital twin simulation system for asphalt pavement repair according to claim 2, characterized in that, The repair requirement analysis unit specifically includes: A comprehensive repair demand analysis model is established, which combines traffic flow, road surface damage severity, and road surface usage to determine whether comprehensive repair, partial repair, and reinforcement measures are needed. The weights of different factors are obtained based on the analytic hierarchy process, and a factor judgment matrix is ​​constructed. The weight of each factor is obtained by calculating the matrix. The weight of each factor is used as input, and the repair demand score is used as output. By analyzing historical scoring data under the same conditions for different types of repairs, regression analysis is used to obtain the threshold for repair demand scores. The repair demand scores are then compared with the thresholds to determine whether the road surface requires comprehensive repair, partial repair, or reinforcement measures.

4. The digital twin simulation system for asphalt pavement repair according to claim 1, characterized in that, The simulation module specifically includes: Mechanical performance simulation unit: Based on the existing geometry and material properties of the road surface, a finite element model is constructed, traffic load data is input, the impact of traffic flow on the road surface is simulated, the stress of the road surface under different repair schemes is simulated, and the improvement of the repair effect on the mechanical performance of the road surface is evaluated. Material Aging and Damage Simulation Unit: Based on temperature and humidity data and the service life of the road surface, the aging process of asphalt materials is simulated. Combined with the type and severity of the damage, the impact of the repair process on the material performance is analyzed, and the aging and performance changes of the materials before and after repair are compared. Traffic Impact Simulation Unit: Simulates traffic conditions when different repair schemes are implemented. Based on real-time climate data, environmental factors are introduced into the simulation model to simulate the impact of environmental factors on pavement mechanical properties, material aging, and repair effects. Multi-scenario simulation unit: Based on different traffic flow and temperature and humidity conditions, it simulates different repair schemes, compares the effects of each repair scheme through simulation of multiple scenarios, and selects the optimal scheme.

5. The digital twin simulation system for asphalt pavement repair according to claim 4, characterized in that, The scheme evaluation module specifically includes: Simulation Result Evaluation Unit: Receives simulation result data of the repair scheme from the simulation module, analyzes and evaluates the simulation results using a pre-trained evaluation model, quantifies the evaluation results, and obtains an evaluation score; Expected effect judgment unit: By comparing the evaluation score with the preset standard, it determines whether the repair plan meets the expected effect standard. If the plan meets the standard, it continues to be implemented. If it does not meet the standard, the repair plan is adjusted and the repair plan is redesigned and simulated until the plan meets the standard.

6. The digital twin simulation system for asphalt pavement repair according to claim 5, characterized in that, The simulation results data of the repair scheme are received from the simulation module. A pre-trained evaluation model is used to analyze and evaluate the simulation results, quantify the evaluation results, and obtain a specific evaluation score. include: The evaluation model formula is as follows: ， In the formula, It is the total assessment score. It is the first The values ​​of each evaluation factor It is the first The weights of each factor It refers to the number of evaluation factors considered. To determine the result, This is the preset standard score.

7. A digital twin simulation method for asphalt pavement repair, applicable to the digital twin simulation system for asphalt pavement repair as described in any one of claims 1-6, characterized in that, include: S1: Data acquisition, obtaining asphalt pavement and traffic data, including pavement geometry, damage types, traffic flow, and temperature and humidity information; S2: Digital twin modeling, which uses 3D modeling and virtual reality technologies to generate a digital twin model of asphalt pavement and updates it in real time; S3: Repair scheme design, based on digital twin model for disease identification and classification, combined with traffic load and demand analysis, to select repair technology; S4: Simulation simulation, which simulates the repair plan on a digital twin model, simulates mechanical properties, material aging and traffic impact, and generates simulation results; S5: Scheme evaluation. Based on the simulation results, the evaluation model is used to evaluate the scheme, give the scheme a score, and determine whether it meets the expected effect. If it does not meet the expected effect, the scheme is adjusted and the simulation is repeated until the standard is met.

8. The digital twin simulation method for asphalt pavement repair according to claim 7, characterized in that, S2 specifically includes: S201: Based on road surface geometry data, a basic three-dimensional road surface model is generated using CAD modeling tools; S202: Integrate pavement distress type and severity data with the basic model to generate a digital twin model; S203: Combine road surface material properties with environmental factors to create a virtual model that conforms to the actual situation and simulates the actual behavior of the road surface; S204: Based on the real-time data update mechanism, correct and update the digital twin model in real time.

9. The digital twin simulation method for asphalt pavement repair according to claim 8, characterized in that, S3 specifically includes: S301: Based on data from the digital twin model, automatically identify the types of defects in asphalt pavement, classify the defects, and mark the location and extent of their impact. S302: Combining traffic flow data and road surface usage, analyze the severity of road surface defects, assess whether comprehensive repair, partial repair, or reinforcement is required, determine the optimal repair requirements, and output a repair requirement score. S303: Select the repair process based on the type and severity of pavement defects, refer to the pre-set repair process library, formulate a construction plan, and plan the construction process. S304: Optimize the selection of repair processes based on the results of the repair needs analysis.

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