Traffic engineering virtual construction system data integration and management method and system based on knowledge graph

Abstract

The invention relates to a data integration and management method and system for a traffic engineering virtual construction system based on a knowledge graph. The system comprises a data acquisition module, a data preprocessing module, a knowledge graph construction module, a three-dimensional scene data integration module, a data management and query module and a third-party system interface module. The data acquisition module is used for realizing data source identification, data interface development, data acquisition strategy formulation and data transmission encryption and backup; the data preprocessing module is used for realizing data cleaning, format conversion, standardized processing and metadata annotation; the knowledge graph construction module is used for realizing knowledge base establishment, entity and relationship definition, graph database application and semantic enhancement; the three-dimensional scene data integration module is used for realizing data mapping, data association, data fusion and data synchronization; the data management and query module is used for realizing semantic query, data visualization and authority management; and the third-party system interface module is used for realizing support of an API (Application Program Interface), a data exchange protocol and a communication protocol. According to the method, the problems of difficulty in multi-source heterogeneous data integration, low management efficiency, inconvenience in query and the like in the existing virtual construction technology are effectively solved, and the method has remarkable technical effects and wide application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic engineering data management, and in particular to a method and system for data integration and management of a traffic engineering virtual construction system based on a knowledge graph. Background Art

[0002] In modern virtual construction transportation engineering projects, data sources are diverse, including BIM (Building Information Modeling), CAD (Computer-Aided Design) drawings, GIS (Geographic Information System) data, IoT sensor data, construction records, text, pictures and videos. These data vary in form and content, and are distributed in different systems and platforms, independent of each other, and lack a unified integration and management method. This decentralized data management leads to serious information island phenomenon, low data utilization, and difficulty in fully realizing the potential value of data. With the increase in project scale and complexity, the amount and types of data are growing rapidly, bringing huge challenges to data management. Effectively integrating and managing these multi-source heterogeneous data has become an important issue that needs to be urgently solved in modern transportation engineering projects.

[0003] Traditional data management methods seem to be powerless when faced with complex traffic engineering data. Existing methods lack unified standards and efficient integration methods, resulting in poor data compatibility and low management and query efficiency. Although there are methods in other fields that use knowledge graphs to build engineering databases, these methods mostly focus on the management of document materials, such as design specifications, design standards, and instructions. Although there is management of BIM model data, the three-dimensional data type is single and mostly static management of engineering databases. In the field of virtual construction of traffic engineering, data not only includes text materials, but also covers a large amount of three-dimensional scene data and multi-source heterogeneous data. Existing methods lack the ability to efficiently integrate and manage these complex data, especially in real-time updating and dynamic management. There are major deficiencies, which cannot meet the efficiency and real-time requirements of modern traffic engineering projects for data management.

[0004] The existing technologies in the field of traffic engineering data management have the following major shortcomings, which limit their effectiveness and efficiency in practical applications: (1) Data island phenomenon: Due to the diversity and dispersion of data sources and the lack of effective integration between different data sources, the phenomenon of information islands is serious, which affects the comprehensive utilization of data.

[0005] (2) Insufficient data standardization: The data formats from different sources are inconsistent and lack unified standards, which makes data difficult to be compatible and integrated, increasing the complexity of management.

[0006] (3) Low management efficiency: Traditional data management systems have low query and processing efficiency when faced with large and complex data sets, and are unable to meet real-time requirements.

[0007] (4) Lack of data association: The relationship between different data has not been effectively established, which limits the association analysis and comprehensive utilization of data.

[0008] (5) Insufficient 3D scene management: The existing system lacks the functions of integrating and managing 3D scene data, making it difficult to achieve data fusion and real-time display of 3D scenes (such as roads, bridges, tunnels, etc.) in transportation engineering.

[0009] (6) Insufficient real-time data updating and dynamic management: The existing system is unable to support real-time updating and dynamic management of data and cannot reflect the real-time progress and changes of transportation engineering projects, thus affecting the timeliness and accuracy of decision-making.

[0010] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0011] The purpose of the present invention is to provide a method and system for data integration and management of a virtual construction system of a transportation engineering project based on a knowledge graph, which utilizes knowledge graph technology to efficiently integrate and manage multi-source heterogeneous data, especially to provide an innovative solution for the integration and real-time updating of three-dimensional scene data.

[0012] In order to achieve the above object, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a system for data integration and management of a virtual construction system of a traffic engineering project based on a knowledge graph, which includes: a data acquisition module, a data preprocessing module, a knowledge graph construction module, a three-dimensional scene data integration module, a data management and query module, and a third-party system interface module; the data acquisition module is connected to the data preprocessing module, and the data acquisition module is used to realize data source identification, data interface development, data acquisition strategy formulation, data transmission encryption and backup; the data preprocessing module is respectively connected to the knowledge graph construction module and the three-dimensional scene data integration module, and the data preprocessing module is used to realize data cleaning, format conversion, standardization processing management and metadata annotation; the knowledge graph construction module is connected with the data management and query module, and the knowledge graph construction module is used to realize knowledge base establishment, entity and relationship definition, graph database application, and semantic enhancement; the three-dimensional scene data integration module is connected with the data management and query module, and the three-dimensional scene data integration module is used to realize data mapping, data association, data fusion, and data synchronization; the data management and query module is connected with the third-party system interface module, and the data management and query module is used to realize semantic query, data visualization, and permission management; the third-party system interface module is used to realize API interface, data exchange protocol, and communication protocol support.

[0013] Preferably, the data acquisition module includes: a data source identification module, a data interface development module, a data acquisition strategy formulation module, and a data transmission encryption and backup module; The data source identification module is used to scan and classify data sources; The data interface development module is used to develop specific data acquisition interfaces for different data sources; The data collection strategy formulation module is used to formulate the collection frequency according to the data type and requirements; The data transmission encryption and backup module is used to ensure the security of data during transmission through the SSL / TLS protocol, and to back up data once at a time and save it to local or cloud storage.

[0014] Preferably, the data preprocessing module includes: a data cleaning module, a format conversion module, a standardization processing module, and a metadata annotation module; The data cleaning module is used to filter irrelevant information, remove duplicate data, and fill in missing values ​​in the collected data; The format conversion module is used to convert data from an original format into a unified processing format; The standardization processing module is used to perform consistency processing on data from different sources. After standardization, the data has a unified format and data structure in the platform; The metadata annotation module is used to add detailed metadata tags to each piece of data.

[0015] Preferably, the knowledge graph construction module includes: a knowledge base establishment module, an entity and relationship definition module, a graph database application module, and a semantic enhancement module; The knowledge base building module builds a knowledge base based on the integration of multiple data sources; The entity and relationship definition module defines key entities in the field of traffic engineering based on the data in the knowledge base establishment module, ensuring that the knowledge graph can accurately represent the various components in the traffic engineering system; The graph database application module is used to store the data of the entity and relationship definition module, and is used to store and query entities and the complex relationships between them; The semantic enhancement module utilizes natural language processing and reasoning techniques to enable the knowledge graph construction module to understand and process complex user queries and semantic reasoning.

[0016] Preferably, the three-dimensional scene data integration module includes: a data mapping module, a data association module, a data fusion module, and a data synchronization module; The data mapping module is used to map the pre-processed data from different sources to the corresponding entities and relationship nodes in the knowledge graph for visual display in a three-dimensional scene; The data association module is used to establish association relationships between data from different sources through relationship definitions in the knowledge graph to achieve data interconnection and unified management; The data fusion module is used to integrate multi-source heterogeneous data to form a consistent data view, thereby realizing seamless display of data in a three-dimensional scene; The data synchronization module ensures that the data in the three-dimensional scene is consistent with the actual data source through real-time or scheduled synchronization to reflect the real-time status of the traffic engineering project.

[0017] Preferably, the data management and query module includes: a semantic query module, a data visualization module, and a rights management module; The semantic query module uses natural language processing and semantic reasoning functions of knowledge graphs. The system allows users to use natural language to query traffic engineering data in order to quickly obtain accurate information. The data visualization module displays data in various forms such as graphs, charts, and three-dimensional models to help users intuitively understand and analyze traffic engineering data; The authority management module sets different access rights for different users, and controls the data information that users can view, modify and manage.

[0018] Preferably, the third-party system interface module includes: an API interface module, a data exchange protocol module and a communication protocol support module; The API interface module realizes data exchange and function integration with other systems through the API interface; The data exchange protocol module supports multiple data exchange protocols to ensure data intercommunication and compatibility with different types of third-party systems; The communication protocol support module supports multiple communication protocols to adapt to different application scenarios and ensure the security, stability and real-time performance of data transmission.

[0019] In the second aspect, the present invention also provides a method for data integration and management of a virtual construction system of a transportation engineering project based on a knowledge graph, which adopts the system for data integration and management of a virtual construction system of a transportation engineering project based on a knowledge graph as described in any one of claims 1 to 7 to realize data collection, data preprocessing, knowledge graph construction, three-dimensional scene data integration, data management and query, and third-party system interface.

[0020] By adopting the above technical solution, the present invention has the following beneficial effects: The present invention provides a method and system for data integration and management of a virtual construction system of a transportation engineering project based on a knowledge graph, which effectively solves the problems of difficult integration of multi-source heterogeneous data, low management efficiency, and inconvenient query in existing virtual construction technologies, and has significant technical effects and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 This is a diagram of the data integration and management system architecture of a traffic engineering virtual construction system based on a knowledge graph provided in an embodiment of the present invention.

[0023] Figure 2 A flowchart of a method for data integration and management of a virtual construction system for transportation engineering based on a knowledge graph provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention. Example

[0026] Combination Figure 1 and Figure 2 As shown, this embodiment provides a system for data integration and management of a traffic engineering virtual construction system based on a knowledge graph, which includes: a data acquisition module, a data preprocessing module, a knowledge graph construction module, a three-dimensional scene data integration module, a data management and query module, and a third-party system interface module; the data acquisition module is connected to the data preprocessing module, and the data acquisition module is used to realize data source identification, data interface development, data acquisition strategy formulation, data transmission encryption and backup; the data preprocessing module is respectively connected to the knowledge graph construction module and the three-dimensional scene data integration module, and the data preprocessing module is used to realize data cleaning, format conversion, standardization processing management and metadata annotation; the knowledge graph construction module is connected with the data management and query module, and the knowledge graph construction module is used to realize knowledge base establishment, entity and relationship definition, graph database application, and semantic enhancement; the three-dimensional scene data integration module is connected with the data management and query module, and the three-dimensional scene data integration module is used to realize data mapping, data association, data fusion, and data synchronization; the data management and query module is connected with the third-party system interface module, and the data management and query module is used to realize semantic query, data visualization, and permission management; the third-party system interface module is used to realize API interface, data exchange protocol, and communication protocol support.

[0027] The overall purpose of this embodiment is to provide a comprehensive and integrated data management solution. Through these interrelated modules, the system can effectively integrate and manage data, improve data utilization efficiency and management level. Through modular design, the system has good scalability and flexibility, and can be expanded and upgraded according to actual needs.

[0028] In this embodiment, preferably, the data acquisition module includes: a data source identification module, a data interface development module, a data acquisition strategy formulation module, and a data transmission encryption and backup module; the data source identification module is used to scan and classify data sources; the data interface development module is used to develop specific data acquisition interfaces for different data sources; the data acquisition strategy formulation module is used to formulate the acquisition frequency according to the data type and requirements; the data transmission encryption and backup module is used to ensure the security and no leakage of data during transmission through the SSL / TLS protocol, and to back up the data once regularly and save it to local or cloud storage.

[0029] The data acquisition module is responsible for collecting traffic engineering related data from various data sources (such as BIM models, CAD drawings, GIS data, IoT sensor data, construction records, texts, pictures and videos, etc.). This module ensures real-time and stable data acquisition by developing and configuring acquisition interfaces for each data source. The data acquisition module adopts a distributed architecture design, which can handle the acquisition tasks of multiple data sources at the same time, ensuring the efficiency and real-time performance of data acquisition.

[0030] The core functions of the data collection module include data source identification, data interface development, and data collection strategy formulation. By identifying and classifying all possible data sources, a reasonable data collection strategy is formulated, including data collection frequency and data update mechanism, to ensure the timeliness and integrity of the data. During the data transmission process, encrypted transmission and data backup mechanisms are used to ensure the security and reliability of data transmission.

[0031] In this embodiment, preferably, the data preprocessing module includes: a data cleaning module, a format conversion module, a standardization processing module, and a metadata annotation module; the data cleaning module is used to filter irrelevant information, remove duplicate data, and fill missing values ​​in the collected data; the format conversion module is used to convert the data from the original format to a unified processing format; the standardization processing module is used to perform consistency processing on data from different sources, and after standardization, the data has a unified format and data structure in the platform; the metadata annotation module is used to add detailed metadata tags for each piece of data.

[0032] The data preprocessing module cleans, converts and standardizes the collected data to ensure data accuracy and consistency. It removes noise and redundant information from the data, repairs missing data, and ensures data integrity. It converts data in different formats into a unified standard format for subsequent processing and integration. At the same time, it adds metadata tags to each piece of data, including data source, data time, data type, etc., for subsequent classification and retrieval. Through standardization, it ensures that data from different sources have good compatibility and operability on the same platform, reduces the complexity of data processing, and improves data utilization efficiency.

[0033] In this embodiment, preferably, the knowledge graph construction module includes: a knowledge base establishment module, an entity and relationship definition module, a graph database application module, and a semantic enhancement module; the knowledge base establishment module establishes a knowledge base based on the integration of multiple data sources; the entity and relationship definition module defines key entities in the field of traffic engineering based on the data in the knowledge base establishment module, ensuring that the knowledge graph can accurately represent the various components in the traffic engineering system; the graph database application module is used to store the data of the entity and relationship definition module, and is used to store and query entities and the complex relationships between them; the semantic enhancement module uses natural language processing and reasoning technology to enable the knowledge graph construction module to understand and process complex user queries and semantic reasoning.

[0034] The knowledge graph construction module builds a knowledge graph covering multi-dimensional information such as transportation components, materials, process flow, personnel, and equipment based on professional knowledge and industry standards in the field of transportation engineering. This module collects and organizes professional knowledge in the field of transportation engineering to form a knowledge base and defines entities and their relationships in the knowledge graph. The knowledge graph is constructed using a graph database, and a semantic network of entities and their relationships is established to achieve mapping from the knowledge base to the knowledge graph, ensuring data consistency and integrity. The expressive power of the knowledge graph is improved by introducing semantic enhancement technology. Natural language processing technology is used to automatically extract semantic information from traffic engineering specifications and supplement it to the knowledge graph.

[0035] In this embodiment, preferably, the core of the knowledge graph construction module is to build a comprehensive and dynamic traffic engineering knowledge system, and realize the intelligent association and display of data through the definition of entities and relationships. The knowledge graph can not only store and display static information in traffic engineering, but also reflect the real-time progress and changes of engineering projects through a dynamic update mechanism, providing a solid data foundation for decision support.

[0036] In this embodiment, preferably, the three-dimensional scene data integration module includes: a data mapping module, a data association module, a data fusion module, and a data synchronization module; the data mapping module is used to map the pre-processed data from different sources to the corresponding entities and relationship nodes in the knowledge graph for visual display in the three-dimensional scene; the data association module is used to establish association relationships between data from different sources through the relationship definitions in the knowledge graph to achieve data interconnection and unified management; the data fusion module is used to integrate multi-source heterogeneous data to form a consistent data view, thereby achieving seamless display of data in the three-dimensional scene; the data synchronization module ensures that the data in the three-dimensional scene is consistent with the actual data source through real-time or timed synchronization to reflect the real-time status of the traffic engineering project.

[0037] The 3D scene data integration module uses the knowledge graph to associate and integrate 3D data from different sources and formats, and realize seamless fusion and integrated management of multi-source data. Through data mapping, the module maps the pre-processed data to the corresponding entities and relationships in the knowledge graph, and establishes the association relationship between data according to the definition in the knowledge graph to form a unified data integration view. At the same time, through data fusion technology, data from different data sources are comprehensively processed to eliminate data conflicts and redundancies and form a consistent data view. Ensure the synchronous update of data sources and knowledge graphs to maintain data consistency and real-time performance.

[0038] Through the intelligent association of knowledge graphs, the system can automatically identify and process complex relationships between data, providing a comprehensive and comprehensive data view. The design of the 3D scene data integration module aims to improve the efficiency of data utilization, support cross-platform and cross-system data sharing and collaboration, and provide strong support for the management and decision-making of transportation engineering projects.

[0039] In this embodiment, preferably, the data management and query module includes: a semantic query module, a data visualization module, and a permission management module; the semantic query module uses natural language processing and the semantic reasoning function of the knowledge graph, and the system allows users to use natural language to query traffic engineering data in order to quickly obtain accurate information; the data visualization module displays data in the form of graphics, charts, and three-dimensional models to help users intuitively understand and analyze traffic engineering data; the permission management module sets different access rights for different users, and the permission management module controls the data information that users can view, modify, and manage.

[0040] The data management and query module uses the semantic query capability provided by the knowledge graph to achieve efficient management and convenient query of traffic engineering three-dimensional data. This module designs a user-friendly query interface that supports natural language-based queries and multi-dimensional data retrieval. The semantic query function of the knowledge graph is used to optimize the processing of complex queries and ensure the efficiency and accuracy of queries. At the same time, it provides a wealth of data visualization tools to support three-dimensional model display, data relationship map display, etc., to enhance the user's query experience. Control data access according to user permissions to ensure data security and privacy. Develop a detailed permission management strategy, including role permissions and data permissions, to ensure that only authorized users can access the corresponding data.

[0041] In this embodiment, preferably, the module can provide users with convenient and intelligent data query and analysis tools. Through natural language processing technology, users can perform complex queries through simple statements, and the system can respond quickly and return accurate results. The application of data visualization tools allows users to intuitively view and analyze data, improve user experience and data utilization efficiency. The authority management mechanism ensures the security of data and prevents unauthorized access and operation.

[0042] In this embodiment, preferably, the third-party system interface module includes: an API interface module, a data exchange protocol module and a communication protocol support module; the API interface module realizes data exchange and function integration with other systems through an API interface; the data exchange protocol module supports multiple data exchange protocols to ensure data interoperability and compatibility with different types of third-party systems; the communication protocol support module supports multiple communication protocols to adapt to different application scenarios and ensure the security, stability and real-time performance of data transmission.

[0043] The third-party system interface module provides a standardized interface to support integration and interoperability with external third-party systems. This module implements data exchange and functional interoperability with other systems through API (application programming interface) and standardized data exchange protocols. It supports multiple data exchange formats (such as JSON, XML, etc.) and communication protocols (such as HTTP, HTTPS, MQTT, etc.) to ensure compatibility and interoperability with various third-party systems.

[0044] The core functions of this module include interface definition, data exchange format conversion and communication protocol support. By defining standardized interfaces, the system can be seamlessly integrated with external systems and share data. The module also supports the real-time and reliability of data exchange, ensuring the security and integrity of data transmission between different systems.

[0045] Compared with the prior art, the invention has the following significant technical advantages and beneficial effects: (1) Efficient data integration: This invention realizes the integration and unified management of multi-source heterogeneous data, solves the problem of data islands, and improves data interoperability and utilization efficiency. Through knowledge graph technology, multidimensional data from BIM, CAD, GIS, IoT sensors, construction records, text, pictures, and videos are efficiently integrated, significantly improving the efficiency and quality of data integration.

[0046] (2) Data standardization: Through the data preprocessing module, data cleaning, format conversion and standardization are achieved to ensure data consistency and availability, laying a solid foundation for efficient data integration and management. Standardized data ensures the stability and reliability of the system and reduces errors and inconsistencies in data processing.

[0047] (3) 3D scene integration and management: The system focuses on the data integration and management of 3D scenes, especially the data fusion and real-time display of 3D scenes in traffic engineering (such as roads, bridges, tunnels, etc.). Users can intuitively view and analyze 3D scene data, which improves the accuracy and timeliness of project management and decision-making.

[0048] (4) Intelligent management: Through the construction and application of knowledge graphs, the level of intelligent data management has been improved, and the complex relationships between data can be automatically associated and displayed. The system can intelligently associate and manage multi-source data in traffic engineering projects, realize intelligent data management, significantly improve management efficiency, and reduce the need for human intervention.

[0049] (5) Convenient query and analysis: The semantic query capability of the knowledge graph is used to achieve efficient data management and convenient query, supporting rapid retrieval and intelligent analysis of multi-dimensional data. Users can quickly and accurately obtain the required information through the natural language query interface, significantly improving work efficiency and data utilization efficiency. The system's intelligent analysis capabilities enable users to better understand and use data.

[0050] (6) Real-time data update and dynamic management: Supporting real-time data update and dynamic management, it can reflect the real-time progress and changes of traffic engineering projects and provide more timely and accurate decision support. The system can update data in real time, ensure the latest and validity of data, and help managers make faster and better decisions.

[0051] (6) External system integration: Through the third-party system interface module, the system can seamlessly integrate and share data with external third-party systems, expanding the system's functions and application scope, and enhancing the system's flexibility and scalability. It supports multiple data exchange formats and communication protocols to ensure the system's compatibility and interoperability.

[0052] Through the above detailed technical scheme, the present invention provides a three-dimensional data integration and management method for a virtual construction system of a transportation engineering project based on a knowledge graph, which effectively solves the problems of difficult integration of multi-source heterogeneous data, low management efficiency, and inconvenient query in existing virtual construction technologies, and has significant technical effects and broad application prospects. Example

[0053] Combination Figure 1 and Figure 2 As shown, this embodiment 2 provides a method for data integration and management of a traffic engineering virtual construction system based on a knowledge graph, which includes: S1, data collection, which includes: data source identification, data interface development, data collection strategy formulation, data transmission encryption and backup; preferably, in S1, by identifying and classifying all possible data sources, formulate a reasonable data collection strategy, including data collection frequency and data update mechanism, to ensure the timeliness and integrity of the data; by developing and configuring the collection interface for each data source, ensure that data can be obtained in real time and stably; during the data transmission process, use encrypted transmission and data backup mechanisms to ensure the security and reliability of data transmission.

[0054] Specifically, data collection includes: Data source identification (1) Identification strategy: The system will first scan and classify common data sources. For example, use automatic scanning tools to detect different data sources in the project and classify them according to preset rules. These rules can be based on file format, data type, data source address (such as database, external API), etc.

[0055] (2) Identified data types: Structured data: such as BIM (Building Information Modeling) files, CAD (Computer-Aided Design) drawings, etc. These files usually exist in formats such as IFC, RVT, DWG, and DXF.

[0056] Geospatial data: such as GIS (Geographic Information System) data, commonly used formats include SHP, GeoJSON, etc.

[0057] Real-time sensor data: information such as temperature, humidity, vibration, etc. collected by IoT sensors.

[0058] Text and image data: such as construction logs, inspection reports, pictures and videos.

[0059] (3) Identification method: For BIM model files, open source tools such as IFCOpenShell can be used to parse the IFC format and extract building elements and attributes from the file structure. For GIS data, the GDAL library can be used to automatically identify and read the spatial coordinate information of the SHP file. For sensor data, the device ID and data stream can be automatically obtained through IoT protocols (such as MQTT).

[0060] Data interface development (1) Interface development strategy: Develop specific data collection interfaces for different data sources.

[0061] (2) Example interface: BIM model interface: Use tools such as IFCOpenShell to parse IFC files and extract structural information of building components (such as component type, location, etc.).

[0062] GIS data interface: Through the PostGIS database connection, the system can query and update geospatial data in real time.

[0063] Sensor data interface: Develop an interface based on MQTT or HTTP protocol to collect real-time data from sensors. The system automatically receives and stores data and supports real-time data updates.

[0064] (3) Interface testing: Testing the stability and performance of the interface. For example, for a sensor interface, load testing is performed to ensure that it can still work stably when multiple devices send data at the same time.

[0065] Data collection strategy development (1) Collection strategy: Collection frequency: The collection frequency is determined based on the data type and requirements. For example, sensor data is collected once a minute, while building model data is only collected when the model is updated.

[0066] Update mechanism: Use a timing or event trigger mechanism. Real-time data is updated through timing collection, and building model data is collected when an update event occurs.

[0067] (2) Example scenario: The temperature and humidity data at the construction site are collected and updated every minute.

[0068] BIM model files are immediately triggered for collection after being updated to ensure the consistency of model data.

[0069] 4. Data transmission encryption and backup (1) Encrypted transmission: The SSL / TLS protocol is used to ensure data security and no leakage during transmission. For example, sensor data is encrypted and transmitted to the server via HTTPS to prevent data from being eavesdropped or tampered with.

[0070] (2) Data backup: Backup strategy: The system backs up data once a day and saves it to local or cloud storage. Backup is performed in different levels according to the importance of the data. For example, sensor data is backed up once a day, while BIM model data is backed up immediately after being updated.

[0071] Backup scenario: Construction log data is encrypted and backed up to the cloud daily. The backup files are encrypted using AES to ensure data security.

[0072] (3) Example scenario: Every morning, the system automatically backs up all collected data to a secure storage server and generates a backup log to record the backup time, data volume, and backup success status.

[0073] S2, data preprocessing, which includes: data cleaning, format conversion, standardization, metadata annotation; preferably, in S2, the collected data is cleaned, format converted and standardized to ensure the accuracy and consistency of the data; by removing noise and redundant information in the data, missing data is repaired to ensure data integrity; data in different formats are converted into a unified standard format for subsequent processing and integration, and metadata tags are added to each data for subsequent classification and retrieval. Through standardization, it is ensured that data from different sources have good compatibility and operability on the same platform.

[0074] Specifically, data preprocessing includes: Data cleaning (1) Content cleaning: Filter out irrelevant information, remove duplicate data, fill in missing values, etc. to ensure the accuracy and completeness of the data.

[0075] (2) Example: Remove noisy data: Remove extreme values ​​or unreasonable data points (such as sudden jumps to values ​​that do not conform to the actual situation) from the temperature and humidity data collected by the sensor to ensure data quality.

[0076] Filling missing values: For missing information in the construction log (such as missing timestamps for some entries), the system can fill it through average interpolation or historical data.

[0077] Removal of redundant data: For example, duplicate BIM component records are detected and removed to ensure that there are no redundant items in the dataset.

[0078] Format Conversion (1) Conversion type: Convert data from its original format into a unified processing format to facilitate subsequent integration and management.

[0079] (2) Example: BIM model format conversion: Convert the original RVT file format to the universal IFC format for easy sharing between different platforms.

[0080] Geographic data format conversion: Convert the SHP file format in GIS data into GeoJSON format so that it can be directly displayed and used on the Web platform.

[0081] Image data conversion: Convert construction image data from HEIC format to common JPEG or PNG format for easy image processing and display.

[0082] Standardization (1) Standardized objects: According to system requirements, data from different sources are processed for consistency. After standardization, the data has a unified format and data structure in the platform.

[0083] (2) Example: Standardize temperature data: All temperature data collected by sensors are expressed in degrees Celsius (°C) to avoid misunderstandings caused by different units during data use.

[0084] Unified coordinate system: For GIS data from different sources, the geographic coordinates are uniformly converted to the WGS 84 coordinate system for easy map display and analysis.

[0085] Time format standardization: All date and time data are standardized into ISO 8601 format (such as YYYY-MM-DDTHH:MM) to ensure time data consistency across platforms.

[0086] Metadata annotation (1) Annotation content: Add detailed metadata tags to each piece of data to facilitate classification, retrieval, and management. Metadata usually includes information such as data source, timestamp, data type, version number, etc.

[0087] (2) Example: Data source labeling: Add collection device ID and location tags to all sensor data so that the data source can be quickly located during data management.

[0088] Timestamp annotation: Add an accurate timestamp (such as 2024-10-08T15:30:00) to each piece of data collected at the construction site to record the data collection time and facilitate time series analysis.

[0089] Version marking: For CAD drawing data, add version number marking (such as v1.2) to track project progress and change records.

[0090] Data type annotation: Add component type annotation (such as "beam" and "column") to the BIM model data to facilitate classification display in subsequent analysis and 3D model display.

[0091] S3, knowledge graph construction, which includes: knowledge base establishment, entity and relationship definition, graph database application, and semantic enhancement; preferably, in S3, by collecting and organizing professional knowledge in the field of traffic engineering, a knowledge base is formed, and the entities and their relationships in the knowledge graph are defined. Based on the preprocessed data, a knowledge graph is constructed using a graph database, and a semantic network of entities and their relationships is established to achieve mapping from the knowledge base to the knowledge graph, thereby ensuring data consistency and integrity; by introducing semantic enhancement technology, the expressive power of the knowledge graph is improved; using natural language processing technology, semantic information in traffic engineering specifications is automatically extracted and supplemented into the knowledge graph.

[0092] Specifically, knowledge graph construction includes: 1. Knowledge base establishment (1) Integration of multi-source heterogeneous data: The knowledge base is established based on the integration of multiple data sources (such as BIM model data, CAD drawings, GIS data, IoT sensor data, text materials, etc.), laying the foundation for the subsequent construction of the knowledge graph.

[0093] (2) Example: BIM data: Extract detailed information of structural components (such as beams, columns, piers, etc.) from the BIM model, including attributes such as size, material, and bearing capacity, and convert them into a standard data format and store them in the knowledge base.

[0094] CAD drawings: Extract the spatial layout information of transportation components such as roads, bridges, and tunnels from CAD drawings. After format conversion, convert this information into images and vector data, and record the relative positions of the components.

[0095] GIS data: Integrate geospatial data (such as regional coordinates and traffic routes) in the GIS system into the knowledge base to ensure that the geographic information of different project areas and components is associated with BIM and CAD data.

[0096] IoT sensor data: Data from sensors (such as temperature, humidity, and vibration data) are stored in the knowledge base. The system records the type, installation location, and data source of each sensor for easy use in the knowledge graph.

[0097] Text data: extract structured information from engineering documents, such as bridge design requirements and load-bearing standards from the Highway Bridge Design Code, convert them into structured data and import them into the knowledge base to ensure consistency with the graph data.

[0098] 2. Entity and relationship definitions (1) Entity definition: Based on the data in the knowledge base, define the key entities in the field of traffic engineering to ensure that the knowledge graph can accurately represent the various components of the traffic engineering system.

[0099] (2) Example: Entity example: The system defines entities such as "bridge", "road", "tunnel", "sensor", "equipment", "material", etc. Each entity has independent properties: The bridge entity includes properties such as type (such as suspension bridge, arch bridge), span, bearing capacity, and main materials.

[0100] Sensor entities include: type (such as temperature, vibration), location, data collection frequency, etc.

[0101] Property enhancement: Define detailed properties on a per-entity basis. For example, the “Material” entity records information such as the material name (such as steel, concrete), strength grade, and applicable scenarios.

[0102] (3) Relationship definition: Define the relationship between entities in the knowledge graph to reflect the association between different entities. For example: Structural relationships: The relationship between a bridge entity and a material entity can be defined as "Use" (the bridge uses a specific material), such as "Bridge A uses steel."

[0103] Geographic Relationship: The relationship between Road and Sensor entities can be defined as "Installed at", i.e. "The temperature sensor is installed at a specified location on a certain road".

[0104] Logical relationship: The relationship between a sensor and a device can be defined as "monitoring", such as "the vibration sensor monitors the vibration status of device B". This can help the system understand the status information of a device or component.

[0105] Hierarchical relationships: For example, divide the bridge entity into sub-entities (such as bridge piers and bridge decks), and define the "include" relationship in the knowledge graph to ensure that data can be related to each other at different levels.

[0106] 3. Graph Database Application (1) Database selection and application: The entity and relationship data in the knowledge graph are stored in a graph database, such as Neo4j. Graph databases can effectively store and query entities and the complex relationships between them.

[0107] (2) Example: Data import: Import the entities and relationships defined in the knowledge base into the graph database. For example, use entities such as "Bridge A" and "Steel B" as nodes, and use "Bridge A uses Steel B" as an edge to connect the nodes, thereby establishing a graph structure in the database.

[0108] Query scenarios: Using graph databases, the system can easily perform multi-level relationship queries. For example, through simple query statements, you can find "all bridges using steel" or "all equipment monitored by a certain sensor."

[0109] Path analysis: In project management, graph databases can be used for path analysis, such as finding the "shortest path from road A to tunnel B", which is very helpful for the construction and planning management of transportation engineering projects.

[0110] Dynamic update: Graph databases support real-time updates. For example, when the material of a component changes, the relationship in the database can be directly modified through a simple update statement to keep the data real-time.

[0111] 4. Semantic Enhancement (1) Semantic extension: Using natural language processing (NLP) and reasoning technology, knowledge graphs can understand and process complex user queries and semantic reasoning, thereby providing users with more intelligent query and analysis capabilities.

[0112] (2) Example: Semantic query: Through NLP technology, the system supports natural language queries, such as "What equipment is suitable for bridge construction?" The NLP module parses the user's query, converts it into a query statement for the graph database, and returns the result.

[0113] Semantic reasoning: Through the reasoning engine, the system can automatically infer information based on existing data. For example, the system can automatically determine that "bridges that have exceeded the specified service life need maintenance" and mark the "maintenance required" status on the bridge entity.

[0114] Automatic graph update: Through the semantic enhancement module, the system can parse new knowledge in traffic engineering documents and automatically update the knowledge graph. For example, when new construction standards are released, the system can extract key information and automatically add it to the knowledge graph, allowing the system to reflect the latest industry standards in real time.

[0115] Semantic recommendation: Based on the knowledge graph, the system can recommend other information related to the current task to the user. For example, when a user queries a certain bridge material, the system can recommend the applicable construction methods and equipment for that material.

[0116] S4, three-dimensional scene data integration, which includes: data mapping, data association, data fusion, and data synchronization; preferably, in S4, through data mapping, the preprocessed data is mapped to the corresponding entities and relationships in the knowledge graph, and according to the definition in the knowledge graph, the association relationship between the data is established to form a unified data integration view; through data fusion technology, data from different data sources are comprehensively processed to eliminate data conflicts and redundancies, form a consistent data view, ensure the synchronous update of the data source and the knowledge graph, and maintain data consistency and real-time.

[0117] Specifically, 3D scene data integration includes: 1. Data Mapping (1) Data mapping function: Map preprocessed data from different sources to corresponding entities and relationship nodes in the knowledge graph for visualization in a three-dimensional scene.

[0118] (2) Example: BIM data mapping: The system maps the components in the BIM model (such as bridges, tunnels, roads) to the "component" entity nodes in the knowledge graph. For example, the geometry, material properties, dimensions and other information of the bridge are mapped to the corresponding attribute fields so that they can be correctly displayed in the 3D scene.

[0119] GIS data mapping: Map the geographic coordinates in the GIS data to the location information of the corresponding components. For example, the longitude and latitude information of a bridge is associated with the bridge entity, so that the bridge can be accurately located according to the geographic coordinates in the 3D scene.

[0120] Real-time data mapping: The system maps the real-time data (such as temperature and humidity) collected from IoT sensors to the corresponding components in the 3D scene. For example, the data from the vibration sensor is associated with the bridge entity to display the real-time status in the 3D view.

[0121] 2. Data Association (1) Data association function: Data from different sources are associated through the relationship definitions in the knowledge graph to achieve data interconnection and unified management.

[0122] (2) Example: Association between components and materials: The system associates bridge components with the materials they use, for example, "Bridge A uses steel B." In this way, when you click on a bridge component in a 3D scene, you can view information about the material used in the component, such as material strength grade, applicable scope, etc.

[0123] Association between components and construction equipment: During the construction process, components such as bridges and roads may require specific construction equipment. The system establishes a "construction" relationship between bridges and equipment, such as "bridge B is constructed by crane C", so that when you select a bridge in the 3D scene, you can see the relevant equipment information.

[0124] Association of sensors with monitoring data: The system associates specific sensors with components, such as "Sensor X monitors the vibration data of Bridge A". This allows users to see the sensor data updated and displayed in real time when viewing the bridge in a 3D scene.

[0125] 3. Data Fusion (1) Data fusion function: Integrate multi-source heterogeneous data to form a consistent data view, thereby achieving seamless display of data in a three-dimensional scene.

[0126] (2) Example: BIM and GIS data fusion: Through data fusion, the building information in the BIM model is integrated with the GIS geographic information for easy viewing in a 3D scene. For example, the 3D model data of a bridge is aligned with its actual coordinates in the geographical location, so that users can see the exact location of the bridge in the 3D map.

[0127] Fusion of sensor data and 3D model: Dynamically overlay real-time data collected by sensors on the bridge model. For example, overlay temperature sensor data on the 3D bridge model, and overlay the temperature data on the bridge model in the form of a color heat map, so that users can intuitively see the temperature changes in different parts.

[0128] Construction progress data integration: Integrate construction progress data with 3D models. For example, the construction progress can be displayed by changing the color of the model, with completed parts displayed in green, parts under construction displayed in yellow, and parts to be constructed displayed in gray, so that users can intuitively understand the construction progress.

[0129] 4. Data Synchronization (1) Data synchronization function: The system ensures that the data in the 3D scene is consistent with the actual data source through real-time or scheduled synchronization to reflect the real-time status of the traffic engineering project.

[0130] (2) Example: BIM model synchronization update: When the BIM model of the project is updated (such as design changes or construction progress), the system will automatically synchronize the model in the 3D scene. For example, if the design of a bridge changes during construction, the bridge model in the 3D scene will automatically update to the new design to ensure that the virtual scene is consistent with the actual scene.

[0131] Real-time sensor data synchronization: The system collects data from vibration sensors installed on the bridge and synchronizes it to the 3D model in real time. In the 3D scene, users can see the real-time vibration data updates on the bridge components to ensure that the scene data is consistent with the actual situation.

[0132] Construction progress synchronization: Synchronize construction progress data from the construction log every day and update the construction status in the 3D model. For example, the system synchronizes the progress data of the construction site every day and displays the latest construction status of each component in the 3D model to help project managers monitor the construction progress in real time.

[0133] Equipment status synchronization: When the status of construction equipment changes (such as equipment maintenance or replacement), the system can automatically and synchronously update the equipment information in the 3D scene. For example, when the status of a crane equipment is updated from "operating" to "maintenance", the corresponding equipment status label in the 3D scene also changes accordingly, allowing managers to understand the equipment status in a timely manner.

[0134] S5, data management and query, which includes: semantic query, data visualization, and permission management; preferably, in S5, a user-friendly query interface is designed to support natural language-based query and multi-dimensional data retrieval, and the semantic query function of the knowledge graph is used to optimize the processing of complex queries to ensure the efficiency and accuracy of the query, provide rich data visualization tools, support three-dimensional model display and data relationship map display, enhance the user's query experience, control data access according to user permissions, and ensure data security and privacy; formulate a detailed permission management strategy, including role permissions and data permissions, to ensure that only authorized users can access the corresponding data.

[0135] Specifically, data management and query include: 1. Semantic Query (1) Semantic query function: Through the semantic reasoning function of natural language processing (NLP) and knowledge graph, the system allows users to use natural language to query traffic engineering data in order to quickly obtain accurate information.

[0136] (2) Example: Natural language query: Users can enter "Which bridges use steel and were built after 2010?" The NLP module parses the query and converts it into a query statement for the graph database. The system will match all eligible bridge nodes and return relevant bridge information.

[0137] Fuzzy query: The system supports fuzzy query so that users can query results that do not fully match. For example, if a user enters "materials suitable for road construction", the system will return a variety of materials related to "road construction" based on the semantic relationship of the knowledge graph.

[0138] Multi-level query: For example, when querying "all sensors and related equipment installed on road A", the system can return detailed information on all relevant sensors and equipment on the road based on the relationship between "installed on" and "monitoring" in the knowledge graph, and display the status of each device.

[0139] 2. Data Visualization (1) Visualization function: The system displays data in various forms such as graphics, charts, and three-dimensional models to help users intuitively understand and analyze traffic engineering data and improve decision-making efficiency.

[0140] (2) Example: Graph display: Different entities and their relationships in the knowledge graph can be displayed through a node graph. When a user queries a bridge, the system displays the relevant nodes of the bridge, including materials, equipment, maintenance history, etc. The relationship between the nodes is represented by lines, helping users quickly understand all the related information of the bridge.

[0141] 3D model display: The system can combine BIM models with GIS data to display traffic engineering components in 3D. For example, a 3D map can be used to display the bridge and road structures in a certain area. Users can rotate and zoom the model to better view the component details.

[0142] Real-time data visualization: For example, the real-time temperature and humidity data collected by sensors are displayed through line charts and heat maps to help users understand the data changes within a certain period of time and identify anomalies.

[0143] Chart display: The system can generate bar charts, pie charts and other charts to display the statistics of various data. For example, it can generate a distribution map of bridge materials used to show the proportion of different types of bridge materials in a certain area, helping users analyze the trend of material use.

[0144] 3. Permission Management (1) Permission management function: In order to protect data security and privacy, the system sets different access rights for different users. The permission management module controls the data information that users can view, modify and manage.

[0145] (2) Example: Role permission settings: The system can set different data access permissions for different roles (such as administrators, engineers, and construction personnel). Administrators can access and modify all data, engineers can view and edit construction data but can only view equipment information, and construction personnel can only view construction progress and some data.

[0146] Data-level permissions: In terms of data permission control, the system can set access permissions based on the sensitivity of the data. For example, sensitive design drawings or cost information can only be accessed by authorized users, while other users can only view project progress and non-sensitive information.

[0147] Regional authority management: In large-scale transportation engineering projects, data in different regions can be divided into independent authority groups. For example, an engineering company is carrying out multiple projects in different regions at the same time. The data of each project can only be accessed by authorized users of that project, and users of other projects have no right to view it.

[0148] Operation log recording: To enhance security, the system records the operation logs of all users, including data access time, user ID, operation type, etc. In this way, when data anomalies occur, the source of the problem can be quickly tracked and confirmed to ensure data security.

[0149] S6, third-party system interface, including: API interface, data exchange protocol, communication protocol support. Preferably, in S6, data exchange and functional intercommunication with other systems are realized through API interface and standardized data exchange protocol, and multiple data exchange formats and communication protocols are supported to ensure compatibility and interoperability with various third-party systems.

[0150] Specifically, the third-party system interfaces include: 1. API interface (1) API interface function: The system implements data exchange and functional integration with other systems through the API interface. The API interface supports different data access methods (such as GET, POST) and provides standardized data formats (such as JSON, XML) for easy call and integration with other systems.

[0151] (2) Example: BIM system integration: The system provides a RESTful API interface, allowing the BIM platform to transmit building information model data through API requests. For example, the BIM system can upload component information (such as the geometry and material properties of a bridge) to this system by calling the API interface to facilitate 3D scene data integration.

[0152] GIS platform connection: Provide API interface, so that the GIS platform can directly access the geospatial data in the system. For example, the GIS platform calls the API interface to obtain the location information and structural information of roads and bridges, and displays the location and status of these components on the map.

[0153] Equipment management system integration: The system connects with the equipment management platform through the API interface, for example, to obtain real-time status data of construction equipment (such as operating status, maintenance status). The equipment management platform updates the equipment status data through the API interface so that the system can display the equipment status changes in real time in the 3D scene.

[0154] 2. Data Exchange Protocol (1) Data exchange protocol function: The system supports multiple data exchange protocols to ensure data interoperability and compatibility with different types of third-party systems. Common data exchange protocols include JSON, XML, CSV, etc. These protocols format and standardize the data exchange process to ensure data consistency between different systems.

[0155] (2) Example: JSON format exchange: The system and the IoT sensor platform use JSON format for data exchange. The temperature, humidity and other data collected by the sensor are transmitted to the system in JSON format so that the system can parse the data and display it in a three-dimensional scene.

[0156] XML format exchange: Exchange data with building information management systems (such as BIM systems) in XML format. For example, the BIM system exports building component information (such as materials and design parameters) in XML format. After receiving the XML file, the system parses and imports it into the database to ensure consistent data exchange between different platforms.

[0157] CSV format exchange: The system and the construction progress management system use CSV file format to exchange project progress data. Construction progress data is regularly exported to the system in CSV format. The system parses the file and updates the progress status in the 3D model, helping project managers to view the latest progress at any time.

[0158] 3. Communication protocol support (1) Communication protocol function: The system supports a variety of communication protocols to adapt to different application scenarios and ensure the security, stability and real-time performance of data transmission. Common communication protocols include HTTP, HTTPS, MQTT, WebSocket, etc.

[0159] (2) Example: HTTP / HTTPS protocol: The system communicates with the remote server via HTTP / HTTPS protocol to ensure the security of data transmission. For example, BIM data is encrypted and transmitted to the system via HTTPS to prevent data from being eavesdropped or tampered with during transmission.

[0160] MQTT protocol: For data transmission of IoT sensors, the system supports the lightweight MQTT protocol. The MQTT protocol is used for real-time data transmission in low-bandwidth environments. For example, the sensor transmits vibration data to the system through the MQTT protocol every minute, allowing the system to monitor the vibration of the bridge in real time.

[0161] WebSocket protocol: The system supports two-way communication with the front-end user interface, such as real-time display of device status changes in a 3D scene. Through the WebSocket protocol, when the device status is updated, the system immediately pushes the status update to the user interface to achieve real-time updates. For example, users can see a device switch from the "running" state to the "maintenance" state in a 3D scene.

[0162] FTP protocol: used for batch transmission of large data files, such as uploading complete BIM models or GIS map data to the system via FTP protocol. FTP protocol supports breakpoint resume function to ensure that large files will not lose data during transmission. Example

[0163] This embodiment 3 takes a specific project as an example to specifically explain the application process of the present application.

[0164] 1. Environment and parameter settings (1) Project background: In a large-scale transportation engineering project, a steel structure bridge with a span of 120 meters needs to be built. The project involves multiple data types, including BIM models, GIS data, IoT sensor data, construction progress data, as well as construction logs (text), on-site photos (pictures), and surveillance videos (videos).

[0165] (1) Data source: BIM model data: Contains the geometry, material and size of bridge components in IFC format.

[0166] GIS data: Geographic location data of the bridge, using the WGS 84 coordinate system and in GeoJSON format.

[0167] Sensor data: including temperature, humidity and vibration sensors, uploading data every minute and transmitted using the MQTT protocol.

[0168] Construction progress data: updated daily, recording construction stages and completion times in CSV format.

[0169] Text data: The construction log records key events, problems and progress during the construction process in TXT format.

[0170] Image data: Contains photos of the bridge construction site, uploaded in JPEG format for easy archiving and progress tracking.

[0171] Video data: Video data is collected through on-site surveillance cameras and stored in MP4 format for real-time monitoring and subsequent analysis.

[0172] (3) System settings: Graph database: Neo4j is used to store knowledge graph data.

[0173] Data visualization platform: Use a 3D visualization platform (such as Cesium or Unity) to display the 3D model, real-time data, pictures, and video content of the bridge.

[0174] API interface: RESTful interface is used to connect with BIM, GIS and sensor systems and realize real-time data access.

[0175] 2. Application Process (1) Data collection and preprocessing: 1 The system imports the bridge model file in IFC format from the BIM platform, parses the component geometry and material properties, and maps these data to the "component" node in the knowledge graph.

[0176] 2 The system obtains the geographic location information of the bridge from the GIS platform, converts it into GeoJSON format, and maps it to the "location information" attribute of the bridge entity node for display in the three-dimensional scene.

[0177] 3 The sensor data is transmitted to the system in real time through the MQTT protocol, recording parameters such as bridge temperature, humidity and vibration, and synchronizing the data to the "real-time status" attribute of the bridge model.

[0178] 4 The construction log (text data) is uploaded in TXT file format. The system uses natural language processing (NLP) technology to analyze the log content, extract key events (such as "completion of pouring", "delay"), and record them in the construction progress attributes of the bridge components.

[0179] 5. The image data (JPEG format) is automatically uploaded to the system database and associated with the corresponding construction phases and components to ensure that the latest construction progress is displayed in the 3D model. You can view related images by clicking on the model node.

[0180] 6 Video data (MP4 format) is uploaded to the system through the API interface, and the video content is associated with a specific construction stage or monitoring point. Users can click on the corresponding component in the 3D scene to call up the relevant video to view the actual situation of the component during the construction process.

[0181] (2) Knowledge graph construction: 1 The system defines the various components of a bridge in the knowledge graph, such as piers, bridge decks, and steel structures. Each component node is associated with the material used in the bridge (such as steel), and is annotated with detailed information such as material strength and specifications.

[0182] 2. Associate components with data types such as sensors, pictures, and videos. For example, the bridge deck is associated with temperature sensors, humidity sensors, and pictures and videos of the construction site to facilitate subsequent visualization.

[0183] 3 The system uses NLP technology to parse the text records in the construction log and associates the construction status with the bridge component nodes, such as "casting completed" and "construction delayed", so that the construction status of the component can be updated in the knowledge graph.

[0184] 4 The system further uses the knowledge graph to store historical images and video data, and annotates information such as upload date and shooting location, so that users can review and compare construction conditions in different time periods at any time.

[0185] (4) 3D scene data integration and visualization: 1The system integrates BIM models, GIS data, sensor data, pictures and videos on a 3D visualization platform. The bridge model is placed at the actual geographical location, and users can rotate and zoom the model to view the bridge structure from different angles.

[0186] 2 Real-time temperature and humidity data are superimposed on the bridge deck in the form of a heat map. Different temperature ranges are represented by different colors, and users can intuitively view the temperature distribution of the bridge deck.

[0187] 3 Users can click on a component in the 3D model to view relevant construction pictures of the component. For example, when clicking on a bridge pier, you can see photos taken during the construction process and check whether the component meets the design requirements.

[0188] 4. Construction progress data is marked on the 3D model with colors. The completed part is displayed in green, the part under construction is displayed in yellow, and the part to be constructed is displayed in gray, helping project managers quickly understand the construction status.

[0189] 5. The system supports embedded video playback. When the user selects a component in the 3D model, he can open the real-time video or historical video of the construction site to view the construction process or on-site conditions. In this way, managers can monitor the on-site construction situation in real time and review the construction records.

[0190] (5) Data management and semantic query: 1The system supports users to obtain the detailed status of bridge components through semantic query function. For example, if you input "bridge components showing excessive temperature", the system will automatically search for components whose temperature exceeds the safety threshold in the knowledge graph and highlight them in the 3D scene, or query "bridge components whose steel strength does not meet the specifications". The system will automatically identify whether the material strength of bridge components meets the specifications and return a list of components that do not meet the specifications.

[0191] 2 Users can query "all pictures taken of the bridge deck during construction", and the system will return all photos associated with the bridge deck and display them in chronological order.

[0192] 3 The user searches for "all videos of the construction site last week". The system will filter the video data by date and display the relevant surveillance videos in a 3D scene. The user can select the video to play and view the construction situation on site last week.

[0193] 4 Users can query "Which sensors detected abnormal data". The system retrieves the real-time data of the sensors in the graph database, returns the sensors with abnormal values ​​(such as vibration exceeding the safety threshold), and highlights the locations of these sensors on the 3D model.

[0194] 5. The system supports natural language queries, such as "What materials are used for the bridge deck?" The system will parse the user query, return the material information of the bridge deck (such as steel, paint, etc.), and provide detailed specifications and applicable scenarios.

[0195] (6) Third-party system interface and data synchronization: 1The system connects with BIM and GIS platforms through API interfaces to ensure synchronous updates of models and geographic data. The updated data of the BIM model is transmitted to a dedicated database or file storage system in IFC format, and the relevant attributes are converted into JSON format for quick query and display through parsing and processing. The system automatically synchronizes and updates BIM data with the 3D model to achieve real-time display of the model on the visualization platform.

[0196] 2Through the MQTT protocol, the sensor platform realizes real-time data transmission, ensuring real-time synchronization of sensor data with the bridge model in the 3D scene. Users can view the real-time monitoring data of the bridge in the 3D scene.

[0197] 3. The pictures and videos of the construction site are uploaded to the system in batches via the FTP protocol and associated with the components in the knowledge graph. The system can synchronously display the latest pictures and video content in the 3D scene, making it easier for users to view the construction progress.

[0198] In summary, in this example, the system demonstrates how to process and integrate multi-source data (including text, images, and videos) to support bridge construction management. The system provides comprehensive support for bridge construction management through modules such as data collection, knowledge graph construction, 3D scene integration, semantic query, and third-party system interface. Users can view real-time sensor data, construction pictures, and monitoring videos in 3D scenes, and obtain key information of construction logs through semantic query, which greatly improves the visualization and intelligence level of construction management.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for data integration and management of virtual construction systems for transportation engineering based on knowledge graph, characterized in that: include: Data acquisition module, data preprocessing module, knowledge graph construction module, 3D scene data integration module, data management and query module, and third-party system interface module; The data acquisition module is connected to the data preprocessing module, and the data acquisition module is used to realize data source identification, data interface development, data acquisition strategy formulation, data transmission encryption and backup; The data preprocessing module is connected to the knowledge graph construction module and the three-dimensional scene data integration module respectively, and the data preprocessing module is used to realize data cleaning, format conversion, standardization processing, and metadata annotation; the knowledge graph construction module is connected to the data management and query module, and the knowledge graph construction module is used to realize knowledge base establishment, entity and relationship definition, graph database application, and semantic enhancement; the three-dimensional scene data integration module is connected to the data management and query module, and the three-dimensional scene data integration module is used to realize data mapping, data association, data fusion, and data synchronization; the data management and query module is connected to the third-party system interface module, and the data management and query module is used to realize semantic query, data visualization, and permission management; the third-party system interface module is used to realize API interface, data exchange protocol, and communication protocol support.

2. The system for data integration and management of traffic engineering virtual construction system based on knowledge graph according to claim 1 is characterized in that: The data acquisition module includes: a data source identification module, a data interface development module, a data acquisition strategy formulation module, and a data transmission encryption and backup module; The data source identification module is used to scan and classify data sources; The data interface development module is used to develop specific data acquisition interfaces for different data sources; The data collection strategy formulation module is used to formulate the collection frequency according to the data type and requirements; The data transmission encryption and backup module is used to ensure the security of data during transmission through the SSL / TLS protocol, and to back up data once at a time and save it to local or cloud storage.

3. The system for data integration and management of traffic engineering virtual construction system based on knowledge graph according to claim 1 is characterized in that: The data preprocessing module includes: a data cleaning module, a format conversion module, a standardization processing module, and a metadata annotation module; The data cleaning module is used to filter irrelevant information, remove duplicate data, and fill in missing values ​​in the collected data; The format conversion module is used to convert data from an original format into a unified processing format; The standardization processing module is used to perform consistency processing on data from different sources. After standardization, the data has a unified format and data structure in the platform; The metadata annotation module is used to add detailed metadata tags to each piece of data.

4. The system for data integration and management of traffic engineering virtual construction system based on knowledge graph according to claim 1 is characterized in that: The knowledge graph construction module includes: a knowledge base establishment module, an entity and relationship definition module, a graph database application module, and a semantic enhancement module; The knowledge base building module builds a knowledge base based on the integration of multiple data sources; The entity and relationship definition module defines key entities in the field of traffic engineering based on the data in the knowledge base establishment module, ensuring that the knowledge graph can accurately represent the various components in the traffic engineering system; The graph database application module is used to store data of the entity and relationship definition module, and is used to store and query entities and complex relationships between them; The semantic enhancement module utilizes natural language processing and reasoning techniques to enable the knowledge graph construction module to understand and process complex user queries and semantic reasoning.

5. The system for data integration and management of traffic engineering virtual construction system based on knowledge graph according to claim 1 is characterized in that: The three-dimensional scene data integration module includes: a data mapping module, a data association module, a data fusion module, and a data synchronization module; The data mapping module is used to map the pre-processed data from different sources to the corresponding entities and relationship nodes in the knowledge graph for visual display in a three-dimensional scene; The data association module is used to establish association relationships between data from different sources through relationship definitions in the knowledge graph to achieve data interconnection and unified management; The data fusion module is used to integrate multi-source heterogeneous data to form a consistent data view, thereby realizing seamless display of data in a three-dimensional scene; The data synchronization module ensures that the data in the three-dimensional scene is consistent with the actual data source through real-time or scheduled synchronization to reflect the real-time status of the traffic engineering project.

6. The system for data integration and management of traffic engineering virtual construction system based on knowledge graph according to claim 1 is characterized in that: The data management and query module includes: a semantic query module, a data visualization module, and a rights management module; The semantic query module uses natural language processing and semantic reasoning functions of knowledge graphs. The system allows users to use natural language to query traffic engineering data in order to quickly obtain accurate information. The data visualization module displays data in various forms such as graphs, charts, and three-dimensional models to help users intuitively understand and analyze traffic engineering data; The authority management module sets different access rights for different users, and controls the data information that users can view, modify and manage.

7. The system for data integration and management of traffic engineering virtual construction system based on knowledge graph according to claim 1 is characterized in that: The third-party system interface module includes: an API interface module, a data exchange protocol module and a communication protocol support module; The API interface module realizes data exchange and function integration with other systems through the API interface; The data exchange protocol module supports multiple data exchange protocols to ensure data intercommunication and compatibility with different types of third-party systems; The communication protocol support module supports multiple communication protocols to adapt to different application scenarios and ensure the security, stability and real-time performance of data transmission.

8. A method for data integration and management of a traffic engineering virtual construction system based on knowledge graph, characterized in that: The system for data integration and management of a virtual construction system for traffic engineering based on a knowledge graph as described in any one of claims 1 to 7 is used to implement data collection, data preprocessing, knowledge graph construction, three-dimensional scene data integration, data management and query, and a third-party system interface.

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