A method and system for constructing a high-precision digital twin base for highways

Abstract

This invention discloses a method and system for constructing a high-precision digital twin platform for highways, belonging to the field of intelligent highway operation and maintenance management technology. The method includes the following steps: multi-source dynamic data integration; collecting multi-source dynamic data, cleaning, fusing, and mining the data to achieve interconnection and sharing of multi-source data; constructing a full-element digital twin model; constructing a full-element digital twin model of the highway based on the processed multi-source data; optimizing the digital twin model by fusing multiple influencing factors based on multi-physics coupling; constructing a digital twin platform; constructing the digital twin platform and establishing an interface between the digital twin platform and the existing highway management system to achieve seamless connection and data sharing between systems.

Description

Technical Field

[0001] This invention belongs to the field of intelligent highway operation and maintenance management technology, and in particular relates to a method and system for constructing a high-precision digital twin base for highways. Background Technology

[0002] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art.

[0003] With the rapid advancement of intelligent transportation construction, highway operation and maintenance management is gradually transforming towards digitalization and intelligence. Digital twin technology, with its ability to accurately map physical entities and virtual models, has become the core technical support for the full life cycle management of highways.

[0004] The current digital management of highways suffers from several technical shortcomings: First, multi-source data, such as pavement structure data, real-time traffic flow data, energy consumption data, and meteorological environmental data, belong to different management systems. Data standards are inconsistent, interfaces are incompatible, and interconnection and efficient sharing are impossible, resulting in prominent data silos. Second, existing digital highway models can only achieve simple visualization of single physical entities, failing to integrate the coupled influence of multiple factors such as traffic flow, energy consumption, and weather. This leads to low model accuracy, poor dynamic response capabilities, and an inability to accurately reproduce the actual operating status of highways. Third, digital twin platforms lack effective integration with existing highway toll collection, monitoring, and maintenance management systems. Data and models cannot provide reliable support for intelligent decision-making and AI simulation, making it difficult to meet the needs of refined and intelligent highway operation and maintenance. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a high-precision digital twin foundation construction method and system for highways, solving the technical problems of existing highways' inability to interoperate multi-source data, insufficient accuracy of digital twin models, and poor platform compatibility. This enables unified management of highway data across the entire region, accurate mapping of all elements, and collaborative operation of the entire system, providing reliable digital twin foundation support for the construction of smart highways.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0007] In a first aspect, the present invention provides a method for constructing a high-precision digital twin base for highways, comprising the following steps:

[0008] S1: Multi-source dynamic data integration;

[0009] Collect dynamic data from multiple sources, clean, merge, and mine the data to achieve interconnection and sharing of multi-source data;

[0010] S2: Construction of a full-element digital twin model;

[0011] Based on the processed multi-source data, a full-element digital twin model of the highway is constructed; based on multi-physics coupling, multiple influencing factors are integrated to optimize the digital twin model;

[0012] S3: Construction of a digital twin foundation platform;

[0013] Construct a digital twin platform and establish an interface between the digital twin platform and the existing highway management system to achieve seamless integration and data sharing between the systems.

[0014] As a further technical solution, in step S1, the multi-source dynamic data includes highway pavement structure, traffic flow, energy consumption, and meteorological environment data.

[0015] As a further technical solution, in step S1, the data cleaning process specifically includes: removing duplicate data, correcting abnormal data, filling in missing data, and using a normalization algorithm to unify the data dimensions and units.

[0016] As a further technical solution, the data fusion processing adopts a multi-source data spatiotemporal alignment algorithm to achieve synchronization of the time and spatial dimensions of data from different sources; the data mining processing adopts big data correlation analysis to obtain the coupling relationship between data and extract the core features and operating rules of the data.

[0017] As a further technical solution, in step S2, the full-element digital twin model of the highway includes road surface, bridges, tunnels, traffic safety facilities, and energy facilities, and a digital mapping between these physical entities and the virtual model is established.

[0018] As a further technical solution, based on the multiphysics coupling algorithm, the digital twin model is optimized by integrating the coupled influencing factors of traffic flow, energy consumption, and meteorological environment, as well as the mutual influence of these factors.

[0019] As a further technical solution, in step S2, a three-dimensional laser scanning device is used to acquire high-precision point cloud data of highway pavement, bridges, tunnels, traffic safety facilities, and energy facilities, and then combined with BIM technology to construct a full-element digital model.

[0020] As a further technical solution, based on the finite element multiphysics coupling algorithm, a correlation model is established between traffic flow load, meteorological environment changes and the stress and energy consumption of highway infrastructure structures, so as to simulate the highway operation status under different traffic and meteorological conditions in real time and realize dynamic simulation.

[0021] As a further technical solution, in step S3, a digital twin base platform is built using a microservice architecture, integrating data acquisition, distributed storage, intelligent analysis, and 3D visualization functions; a standardized API interface is developed to achieve seamless connection between the digital twin base platform and the existing maintenance management system, traffic monitoring system, and energy management system of the road section, enabling real-time bidirectional data transmission; and the optimized digital twin model is pushed to the AI ​​simulation engine to provide model services for traffic accident prediction, maintenance decision-making, and energy consumption optimization.

[0022] Secondly, the present invention also provides a construction system for a high-precision highway digital twin base, comprising:

[0023] The multi-source dynamic data integration module is used to collect multi-source dynamic data, clean, merge and mine the data, and realize the interconnection and sharing of multi-source data.

[0024] The full-element digital twin model construction module is used to build a full-element digital twin model of highways based on processed multi-source data; and to optimize the digital twin model by integrating multiple influencing factors based on multi-physics coupling.

[0025] The digital twin platform building module is used to construct the digital twin platform and establish an interface between the digital twin platform and the existing highway management system, so as to achieve seamless connection and data sharing between the systems.

[0026] The beneficial effects of the present invention are as follows:

[0027] The high-precision highway digital twin base construction method of the present invention establishes a unified data standard, collects and integrates multi-source dynamic data, and uses multi-source data cleaning, fusion and mining technology to completely break down the data barriers between various highway systems, realize the interconnection and efficient sharing of multi-dimensional data such as road surface, traffic, energy consumption, and weather, greatly improve data quality and usability, and lay a solid data foundation for the digital twin base.

[0028] The high-precision digital twin base construction method for highways of the present invention constructs a digital twin model of all elements of the highway, combines a multi-physics coupling algorithm, integrates the dynamic influence of multiple factors, realizes high-precision digital mapping of physical entities and full life cycle visualization management, significantly improves model accuracy and dynamic response capability, and can realistically restore the operating status of the highway.

[0029] The high-precision highway digital twin platform construction method of the present invention, by constructing a digital twin platform, can seamlessly connect with the existing highway management system through standardized interfaces, realize data sharing and business collaboration across the entire system, and continuously provide accurate data and model support for AI simulation engines and intelligent decision-making platforms, effectively improving the intelligence and refinement of highway operation and maintenance management. Attached Figure Description

[0030] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0031] Figure 1 This is a flowchart of a method for constructing a high-precision highway digital twin base according to one or more embodiments of the present invention. Detailed Implementation

[0032] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless otherwise expressly indicated by the invention, the singular form is intended to include the plural form as well. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0034] As described in the background section, there are shortcomings in the existing technology. In order to solve the above-mentioned technical problems, this invention proposes a method and system for constructing a high-precision digital twin base for highways.

[0035] Example 1:

[0036] In a typical embodiment of the present invention, such as Figure 1 As shown, a method for constructing a high-precision digital twin base for highways is proposed, including the following steps:

[0037] S1: Multi-source dynamic data integration;

[0038] Collect dynamic data from multiple sources, clean, merge, and mine the data to achieve interconnection and sharing of multi-source data;

[0039] S2: Construction of a full-element digital twin model;

[0040] Based on the processed multi-source data, a full-element digital twin model of highways is constructed; based on multi-physics coupling, multiple influencing factors are integrated to optimize the digital twin model and improve its accuracy and reliability.

[0041] S3: Construction of a digital twin foundation platform;

[0042] Build a digital twin foundation platform and establish an interface between the digital twin foundation platform and the existing highway management system to achieve seamless connection and data sharing between the systems, and provide data and model services for AI simulation engine and intelligent decision-making platform.

[0043] In step S1, the multi-source dynamic data includes various data such as highway pavement structure, traffic flow, energy consumption, and meteorological environment. After collecting and integrating these multi-source dynamic data, a unified data standard and specification are established to achieve data interconnection and sharing.

[0044] Cleaning, fusion, and mining of multi-source dynamic data improves data quality and usability, providing reliable data support for the digital twin foundation.

[0045] The steps for acquiring multi-source dynamic data are as follows:

[0046] By using road surface detection sensors, traffic monitoring cameras, energy metering equipment, and meteorological monitoring stations, dynamic data from multiple sources such as highway pavement smoothness, structural strength, real-time traffic flow, vehicle speed, road section energy consumption, temperature, humidity, and wind speed are collected, and a unified standard including data format, encoding rules, and transmission protocol is formulated.

[0047] Furthermore, in step S1, the data cleaning process specifically includes: removing duplicate data, correcting abnormal data, filling in missing data, and using a normalization algorithm to unify the data dimensions and units. This involves removing duplicate traffic flow data, correcting abnormal energy consumption data, using linear interpolation to fill in missing meteorological data, and unifying the data units through normalization.

[0048] The data fusion processing employs a multi-source data spatiotemporal alignment algorithm to achieve synchronization of the temporal and spatial dimensions of data from different sources.

[0049] Data mining processing employs big data correlation analysis to obtain the coupling relationships between data and extract the core features and operational patterns of the data.

[0050] In step S2, the digital twin model of the entire highway includes road surface, bridges, tunnels, traffic safety facilities, energy facilities, etc., and establishes a digital mapping between these physical entities and the virtual model to realize the visualization and management of the entire life cycle of the highway.

[0051] Based on a multiphysics coupling algorithm, this algorithm integrates influencing factors such as traffic flow, energy consumption, and meteorological environment, as well as the mutual influence of these factors, to optimize the digital twin model and improve its accuracy and reliability.

[0052] Furthermore, in step S2, high-precision point cloud data of highway pavement, bridges, tunnels, traffic safety facilities, and energy facilities are acquired using 3D laser scanning equipment. Combined with BIM technology, a full-element digital model is constructed to achieve millimeter-level digital mapping of physical entities.

[0053] Based on the finite element multiphysics coupling algorithm, a correlation model is established between traffic flow load, meteorological environment changes and the stress and energy consumption of highway infrastructure structures. The model simulates the highway operation status under different traffic and meteorological conditions in real time, realizes dynamic simulation, and optimizes the dynamic response accuracy of the model.

[0054] In step S3, a digital twin platform is constructed to realize functions such as data collection, storage, management, analysis, and visualization, providing data support and model services for AI simulation engines and intelligent decision-making platforms.

[0055] Establish an interface between the digital twin platform and the existing highway management system to achieve seamless integration and data sharing between the systems.

[0056] Furthermore, in step S3, a digital twin foundation platform is built using a microservice architecture, integrating data acquisition, distributed storage, intelligent analysis, and 3D visualization functions.

[0057] Develop standardized API interfaces to achieve seamless integration between the digital twin platform and the existing maintenance management system, traffic monitoring system, and energy management system of the road section, enabling real-time bidirectional data transmission.

[0058] The optimized digital twin model is pushed to the AI ​​simulation engine to provide model services for traffic accident prediction, maintenance decision-making, and energy consumption optimization.

[0059] Example 2:

[0060] In another typical embodiment of the present invention, a construction system for a high-precision highway digital twin base is proposed, comprising:

[0061] The multi-source dynamic data integration module is used to collect multi-source dynamic data, clean, merge and mine the data, and realize the interconnection and sharing of multi-source data.

[0062] The full-element digital twin model construction module is used to build a full-element digital twin model of highways based on processed multi-source data; based on multi-physics coupling, it integrates multiple influencing factors to optimize the digital twin model and improve the model's accuracy and reliability.

[0063] The digital twin foundation platform building module is used to construct the digital twin foundation platform, establish the interface between the digital twin foundation platform and the existing highway management system, realize seamless connection and data sharing between systems, and provide data and model services for AI simulation engine and intelligent decision-making platform.

[0064] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing a high-precision digital twin base for highways, characterized in that, Includes the following steps: S1: Multi-source dynamic data integration; Collect dynamic data from multiple sources, clean, merge, and mine the data to achieve interconnection and sharing of multi-source data; S2: Construction of a full-element digital twin model; Based on the processed multi-source data, a full-element digital twin model of the highway is constructed; based on multi-physics coupling, multiple influencing factors are integrated to optimize the digital twin model; S3: Construction of a digital twin foundation platform; Construct a digital twin platform and establish an interface between the digital twin platform and the existing highway management system to achieve seamless integration and data sharing between the systems.

2. The method for constructing a high-precision highway digital twin base as described in claim 1, characterized in that, In step S1, the multi-source dynamic data includes highway pavement structure, traffic flow, energy consumption, and meteorological environment data.

3. The method for constructing a high-precision highway digital twin base as described in claim 1, characterized in that, In step S1, the data cleaning process specifically includes: removing duplicate data, correcting abnormal data, filling in missing data, and using a normalization algorithm to unify the data dimensions and units.

4. The method for constructing a high-precision highway digital twin base as described in claim 1, characterized in that, Data fusion processing employs a multi-source data spatiotemporal alignment algorithm to synchronize the temporal and spatial dimensions of data from different sources; data mining processing utilizes big data correlation analysis to obtain the coupling relationships between data and extract the core features and operational patterns of the data.

5. The method for constructing a high-precision highway digital twin base as described in claim 1, characterized in that, In step S2, the full-element digital twin model of the highway includes road surface, bridges, tunnels, traffic safety facilities, and energy facilities, and a digital mapping between these physical entities and the virtual model is established.

6. The method for constructing a high-precision highway digital twin base as described in claim 1 or 5, characterized in that, Based on a multiphysics coupling algorithm, the digital twin model is optimized by integrating the coupled influencing factors of traffic flow, energy consumption, and meteorological environment, as well as the mutual influence of these factors.

7. The method for constructing a high-precision highway digital twin base as described in claim 1, characterized in that, In step S2, high-precision point cloud data of highway pavement, bridges, tunnels, traffic safety facilities, and energy facilities are acquired using 3D laser scanning equipment, and a full-element digital model is constructed by combining it with BIM technology.

8. The method for constructing a high-precision highway digital twin base as described in claim 1 or 7, characterized in that, Based on the finite element multiphysics coupling algorithm, a correlation model is established between traffic flow load, meteorological environment changes and the stress and energy consumption of highway infrastructure structures. This model simulates the operation status of highways under different traffic and meteorological conditions in real time, achieving dynamic simulation.

9. The method for constructing a high-precision highway digital twin base as described in claim 1, characterized in that, In step S3, a digital twin foundation platform is built using a microservice architecture, integrating data acquisition, distributed storage, intelligent analysis, and 3D visualization functions; Develop standardized API interfaces to achieve seamless integration between the digital twin platform and the existing maintenance management system, traffic monitoring system, and energy management system of the road section, enabling real-time bidirectional data transmission; push the optimized digital twin model to the AI ​​simulation engine to provide model services for traffic accident prediction, maintenance decision-making, and energy consumption optimization.

10. A system for constructing a high-precision digital twin base for highways, characterized in that, include: The multi-source dynamic data integration module is used to collect multi-source dynamic data, clean, merge and mine the data, and realize the interconnection and sharing of multi-source data. The full-element digital twin model building module is used to build a full-element digital twin model of highways based on processed multi-source data; Based on multi-physics coupling, multiple influencing factors are integrated to optimize the digital twin model; The digital twin platform building module is used to construct the digital twin platform and establish an interface between the digital twin platform and the existing highway management system, so as to achieve seamless connection and data sharing between the systems.

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