Virtual construction analogue simulation and optimization method and system based on GrgphRAG

Abstract

The invention relates to the technical field of construction engineering management, in particular to a virtual construction simulation and optimization method and system based on GrgphRAG. The system comprises a data acquisition module, a data preprocessing module, a knowledge graph construction module, a virtual construction scene generation module, a construction process simulation and optimization module, a VR / AR integration module, a visual interaction module, a knowledge base updating and maintenance module and the like. By introducing the GrgphRAG technology, accurate retrieval and enhanced generation of the data are realized, the analogue simulation and intelligent analysis capabilities and the user interaction experience function of the system are improved, and real-time updating of the knowledge base is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering management, and in particular to a virtual construction simulation and optimization method based on GrgphRAG and a system thereof. Background Art

[0002] As the scale and complexity of construction projects continue to increase, traditional construction management methods can no longer meet the needs of the modern construction industry. Virtual construction technology provides visual management of the entire construction process through digitalization and simulation technology, which helps to improve project quality and efficiency. However, existing virtual construction methods still have many shortcomings in information integration, simulation, and optimization decision-making. They cannot fully utilize the huge construction data resources, resulting in certain limitations in project planning, construction management, and resource optimization.

[0003] At present, the construction project management system mainly relies on independent data platforms and applications, with serious information islands and a lack of unified data sharing and comprehensive analysis capabilities. Since the data is scattered and unrelated, the construction information cannot be effectively integrated and utilized, which affects the overall decision-making efficiency and execution effect of the project. At the same time, the existing system lacks intelligent construction process simulation and optimization, making it difficult to accurately predict potential problems in the construction process and provide optimized construction plans, resulting in low construction efficiency and quality. The user interaction experience is also poor, making it difficult to quickly obtain the required information, which affects management efficiency.

[0004] The existing virtual construction system simulation and optimization have technical problems in the following aspects, which limit its effectiveness and efficiency in modern construction project management: Insufficient data integration: The existing system is difficult to integrate information from multiple data sources, resulting in serious information islands, which affects the comprehensiveness and accuracy of decision-making. The lack of unified standards and interfaces between different data sources makes it difficult to share and interoperate data, which in turn affects the efficiency of construction management and resource allocation.

[0005] Insufficient simulation and optimization: The existing system is insufficient in simulation and optimization of the construction process. It lacks efficient simulation tools and optimization algorithms, making it difficult to accurately predict potential problems in the construction process and provide optimized construction solutions. Traditional simulation and optimization methods are relatively simple and difficult to handle complex construction scenarios and multi-variable construction conditions, resulting in low construction efficiency and quality.

[0006] Limited intelligent analysis capabilities: Lack of intelligent analysis and prediction capabilities for complex building data, unable to provide timely and accurate optimization suggestions. The existing system mainly relies on manual analysis and experience judgment, and it is difficult to make full use of a large amount of historical data and real-time data for scientific decision-making, resulting in insufficient timeliness and accuracy in risk identification and problem solving during the construction process.

[0007] Knowledge base updates are lagging: There is a lack of an effective knowledge base update mechanism, and the knowledge graph content cannot reflect the latest industry standards and project data in a timely manner. The knowledge base in the existing system is mostly static information with low update frequency, which makes it difficult to keep up with the rapidly changing industry dynamics and project requirements, resulting in outdated decision-making basis, affecting the scientificity and effectiveness of decision-making. Summary of the invention

[0008] The purpose of the present invention is to provide a virtual construction simulation and optimization method and system based on GrgphRAG, which can realize accurate retrieval and enhanced generation of data by introducing GrgphRAG technology, improve the system's simulation, intelligent analysis capabilities, and user interactive experience functions, and ensure real-time updating of the knowledge base.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a virtual construction simulation and optimization system based on GrgphRAG, which includes: a data acquisition module, a data preprocessing module, a knowledge graph construction module, a virtual construction scene generation module, a construction process simulation and optimization module, a VR / AR integration module, a visualization interaction module and a knowledge base update and maintenance module; the data acquisition module is connected to the data preprocessing module, and the data acquisition module is used to collect data from BIM models, CAD drawings, sensor data, construction records and historical project data, and feed the collected data back to the data preprocessing module; the data preprocessing module is connected to the knowledge graph construction module, and the data The preprocessing module cleans, converts the format and removes duplicates from the collected data, and gives the preprocessed data to the knowledge graph construction module; the knowledge graph construction module is connected to the virtual construction scene generation module, the knowledge graph construction module is used to extract architectural elements and relationships, build a comprehensive knowledge graph, and use GrgphRAG technology for data enhancement and retrieval; the virtual construction scene generation module is respectively connected to the construction process simulation and optimization module and the visual interaction module, the virtual construction scene generation module generates a realistic virtual construction scene based on the knowledge graph, simulates each step and link of the construction process, and uses GrgphRAG technology to optimize the generation process; The virtual construction scene generation module provides a virtual construction scene to the construction process simulation and optimization module, and the virtual construction scene generation module provides a construction scene to the visualization interaction module; the visualization interaction module is used to provide an intuitive user interface, display construction progress, simulation results and optimization suggestions, and use GrgphRAG technology for intelligent data display; the construction process simulation and optimization module is connected to the VR / AR integration module and the knowledge base update and maintenance module respectively, and the construction process simulation and optimization module simulates and optimizes the construction process in detail, identifies potential problems and provides optimization suggestions, and uses GrgphRAG technology for intelligent analysis and optimization. The construction process simulation and optimization module provides simulation results and optimization suggestions to the VR / AR integration module, and the construction process simulation and optimization module provides optimization data to the knowledge base update and maintenance module; the VR / AR integration module is used to integrate virtual reality and augmented reality technologies to provide immersive experience and real-time interaction, and uses GrgphRAG technology to enhance user interactive experience; the knowledge base update and maintenance module is connected to the knowledge graph construction module to ensure the timeliness and accuracy of the knowledge graph and related data, automatically update the knowledge base content, use GrgphRAG technology to automatically update and maintain, and provide the updated knowledge graph data to the knowledge graph construction module.

[0010] In the second aspect, the present invention provides a method for virtual construction simulation and optimization based on GrgphRAG, which adopts the virtual construction simulation and optimization system based on GrgphRAG described in claim 1 to realize data collection, data preprocessing, knowledge graph construction, virtual construction scene generation, construction process simulation and optimization, VR / AR integration, visual interaction, knowledge base update and maintenance.

[0011] Specifically, S1. Data collection: collect data from BIM models, CAD drawings, sensor data, construction records, and historical project data; S2. Data preprocessing: cleaning, format conversion and deduplication of collected data; S3, Knowledge Graph Construction: Extract architectural elements and relationships, build a comprehensive knowledge graph, and use GrgphRAG technology for data enhancement and retrieval; S4. Virtual construction scene generation: Generate realistic virtual construction scenes based on knowledge graphs, simulate each step and link of the construction process, and use GrgphRAG technology to optimize the generation process; Wherein, a virtual construction scene is provided for step S5; Wherein, a construction scene is provided for step S7; S5. Construction process simulation and optimization: Detailed simulation and optimization of the construction process, identification of potential problems and provision of optimization suggestions, and intelligent analysis and optimization using GrgphRAG technology; Among them, providing simulation results and optimization suggestions for step S6; Wherein, optimization data is provided for step S8; S6. VR / AR integration: Integrate virtual reality and augmented reality technologies to provide immersive experience and real-time interaction, and use GrgphRAG technology to enhance user interaction experience; S7, Visual Interaction: Provides an intuitive user interface to display construction progress, simulation results and optimization suggestions, and uses GrgphRAG technology for intelligent data display; S8. Knowledge base update and maintenance: Ensure the timeliness and accuracy of the knowledge graph and related data, automatically update the knowledge base content, use GrgphRAG technology to automatically update and maintain, and provide the updated knowledge graph data to step S3.

[0012] By adopting the above technical solution, the present invention has the following beneficial effects: The present invention introduces GrgphRAG technology to achieve accurate retrieval and enhanced generation of data, improve the system's simulation, intelligent analysis capabilities, and user interactive experience functions, and ensure real-time updating of the knowledge base. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] 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.

[0014] Figure 1 A flowchart of a virtual construction simulation and optimization method based on GrgphRAG provided in an embodiment of the present invention.

[0015] Figure 2 An architectural diagram of a virtual construction simulation and optimization system based on GrgphRAG provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] 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

[0018] Combination Figure 1 and Figure 2As shown, the first embodiment of the present invention provides a virtual construction simulation and optimization system based on GrgphRAG, which includes: a data acquisition module, a data preprocessing module, a knowledge graph construction module, a virtual construction scene generation module, a construction process simulation and optimization module, a VR / AR integration module, a visualization interaction module and a knowledge base update and maintenance module; the data acquisition module is connected to the data preprocessing module, and the data acquisition module is used to collect data from BIM models, CAD drawings, sensor data, construction records and historical project data, and feed the collected data back to the data preprocessing module; the data preprocessing module is connected to the knowledge graph construction module The data preprocessing module cleans, converts the format and removes duplicates of the collected data, and gives the preprocessed data to the knowledge graph construction module; the knowledge graph construction module is connected to the virtual construction scene generation module, the knowledge graph construction module is used to extract architectural elements and relationships, build a comprehensive knowledge graph, and use GrgphRAG technology for data enhancement and retrieval; the virtual construction scene generation module is respectively connected to the construction process simulation and optimization module and the visualization interaction module, the virtual construction scene generation module generates a realistic virtual construction scene based on the knowledge graph, simulates each step and link of the construction process, and uses GrgphRAG technology to optimize the generation process; the virtual construction scene generation module provides the virtual construction scene to the construction process simulation and optimization module, and the virtual construction scene generation module provides the construction scene to the visualization interaction module; the visualization interaction module is used to provide an intuitive user interface, display the construction progress, simulation results and optimization suggestions, and use GrgphRAG technology for intelligent data display; the construction process simulation and optimization module is connected to the VR / AR integration module and the knowledge base update and maintenance module respectively, and the construction process simulation and optimization module simulates and optimizes the construction process in detail, identifies potential problems and provides optimization suggestions, and uses GrgphRAG technology for intelligent analysis and Optimization, the construction process simulation and optimization module provides simulation results and optimization suggestions to the VR / AR integration module, and the construction process simulation and optimization module provides optimization data to the knowledge base update and maintenance module; the VR / AR integration module is used to integrate virtual reality and augmented reality technologies to provide immersive experience and real-time interaction, and use GrgphRAG technology to enhance user interactive experience; the knowledge base update and maintenance module is connected to the knowledge graph construction module to ensure the timeliness and accuracy of the knowledge graph and related data, automatically update the knowledge base content, use GrgphRAG technology to automatically update and maintain, and provide the updated knowledge graph data to the knowledge graph construction module.

[0019] This embodiment is based on GrgphRAG's virtual construction system simulation and optimization, and is specifically designed to solve complex problems in modern construction project management. The system includes a data acquisition and preprocessing module, a knowledge graph construction module, a virtual construction scene generation module, a construction process simulation and optimization module, a VR / AR integration module, a visualization and user interaction module, and a knowledge base update and maintenance module. These modules are closely connected to form a whole, providing a comprehensive construction management solution to improve engineering efficiency and decision-making accuracy.

[0020] In this embodiment, preferably, the data acquisition and preprocessing module is not only responsible for collecting building-related data from multiple sources such as BIM models, CAD drawings, sensor data, and historical project data, but also uses GrgphRAG technology to automatically extract valuable information from a large amount of unstructured data and convert it into a structured graph data form. The collected data is cleaned, formatted, and deduplicated through advanced data processing technology to ensure the accuracy and consistency of the data and establish a high-quality building information foundation. These data include not only building design and construction plans, but also real-time construction status, environmental monitoring data, and equipment operation status, forming a comprehensive database that provides reliable data support and foundation for other modules of the system, ensuring that each decision is based on the latest and most comprehensive information.

[0021] In this embodiment, preferably, the knowledge graph construction module extracts architectural elements and their relationships from the data through natural language processing and machine learning technology to construct a comprehensive knowledge graph, including multi-dimensional information such as building structure, construction steps and resource allocation. Using GrgphRAG technology, information from different data sources is integrated into the knowledge graph, and the knowledge graph is dynamically updated through graph retrieval and enhanced generation technology to ensure the timeliness and accuracy of its content. This module forms a dynamic and scalable knowledge network through deep mining and association analysis of architectural data, which helps the system understand the various components of the construction project and their relationships, and provides key support for the generation and optimization of virtual construction scenes. In addition, the knowledge graph construction module also supports cross-project data integration and knowledge sharing, so that different projects can learn from each other and further improve the overall level of project management.

[0022] In this embodiment, preferably, the virtual construction scene generation module generates a realistic virtual construction scene based on the knowledge graph, simulating each step and link of the actual construction process. Using advanced 3D modeling and scene rendering technology, a highly simulated virtual environment is created, allowing engineering managers to preview and adjust construction plans in a virtual space. GrgphRAG technology can retrieve relevant information from the knowledge graph and generate a detailed virtual construction plan. By enhancing the generation technology, the accuracy and realism of the virtual construction scene are ensured. Users can quickly adjust and optimize the construction plan by interacting with the knowledge graph. The module also supports multi-user collaboration and real-time editing, promotes communication and coordination among team members, improves the feasibility and optimization level of the construction plan, and provides a reliable reference and guidance for actual construction.

[0023] In this embodiment, preferably, the construction process simulation and optimization module simulates and optimizes the construction process in detail in a virtual environment, including construction steps, time scheduling and resource allocation. By simulating various construction scenarios, the module can identify potential problems and bottlenecks and propose optimization solutions. First, GrgphRAG technology uses the graph retrieval function to quickly extract and use relevant information from the knowledge graph, including the relationship between building elements, construction steps and resource allocation, to ensure the accuracy and completeness of the simulation process. Secondly, using GrgphRAG technology, a variety of optimization solutions are generated, and detailed feasibility analysis is performed on these solutions. In addition, the system can also retrieve best practices and historical data in the construction process from the knowledge graph, generate optimization suggestions, and use enhanced generation technology to further optimize the simulation results to improve construction efficiency and quality.

[0024] In this embodiment, preferably, the intelligent prediction and optimization module supports real-time monitoring and dynamic adjustment to ensure the continuity and smoothness of the construction process and provide timely optimization suggestions and feedback. By identifying potential construction risks and problems, the system can provide prediction and optimization suggestions. It helps to foresee possible construction risks, make preventive adjustments, and ensure that the project is completed on time, with quality, and in quantity.

[0025] In this embodiment, preferably, the VR / AR integration module enables users to experience and inspect virtual construction scenes in person by integrating virtual reality (VR) and augmented reality (AR) technologies, providing immersive experience and real-time interaction. GrgphRAG technology provides the latest building information and optimization suggestions through real-time retrieval and generation. Users interact with the knowledge graph in a VR / AR environment, can quickly obtain the required information, and make real-time adjustments and optimizations. Through VR / AR devices, users can simulate actual construction scenes, conduct virtual inspections and acceptances, enhance the intuitiveness and accuracy of construction management, and improve user participation and decision-making efficiency. This module not only improves the visualization of the construction process, but also facilitates remote collaboration and off-site expert guidance, so that problems on the construction site can be solved in a timely manner.

[0026] In this embodiment, preferably, the visualization interaction module provides an intuitive user interface to display the construction progress, simulation results and optimization suggestions, so that managers can grasp the project status in real time and make timely adjustments. GrgphRAG technology provides users with comprehensive and detailed visualization data through graph retrieval and enhanced generation. Users can make complex query requests through the interactive interface, and GrgphRAG technology will quickly retrieve and generate corresponding visualization results to enhance the user's decision support capabilities. The module uses a variety of methods such as charts, dashboards and 3D models to display data to improve the readability and ease of use of information.

[0027] In this embodiment, preferably, a knowledge base update and maintenance module. This module continuously collects and integrates the latest construction industry information and project data from various data sources in an automated manner, dynamically updates the knowledge graph in the system, and ensures that the knowledge base always reflects the latest industry standards and project requirements. Using GrgphRAG technology, the system can accurately retrieve and generate high-quality knowledge content, improving the real-time and accuracy of the knowledge base. In addition, the module also provides intelligent knowledge management functions, including automatic classification, label generation, version control, and change tracking, etc., to support users to easily access and update knowledge base content. In this way, the system can provide the latest reference basis for virtual construction simulation and optimization, improve the scientificity and effectiveness of decision-making, and at the same time enhance the system's self-learning and continuous improvement capabilities to ensure efficient execution and intelligent optimization of construction project management. Example

[0028] Combination Figure 1 and Figure 2 As shown, this second embodiment provides a virtual construction simulation and optimization method based on GrgphRAG on the basis of the first embodiment, which includes: S1. Data collection: Collect data from BIM models, CAD drawings, sensor data, construction records, and historical project data; S2. Data preprocessing: cleaning, format conversion and deduplication of collected data; S3, Knowledge Graph Construction: Extract architectural elements and relationships, build a comprehensive knowledge graph, and use GrgphRAG technology for data enhancement and retrieval; For example, in a bridge construction project, the knowledge graph needs to include information such as bridge components, construction steps, materials and equipment used, and construction environment. The knowledge graph construction and data enhancement process can include the following steps: Extract architectural elements and relationships: The system extracts building elements (such as piers, bridge decks, supporting structures) and their properties (such as materials, dimensions, installation locations) from BIM models, CAD drawings and construction records.

[0029] Next, the system identifies the relationships between construction elements. For example, the relationship between the bridge deck and the piers is "support", the relationship between the bridge deck and steel is "material used", and the relationship between equipment and piers is "construction equipment".

[0030] In addition, the relationships between building elements and construction steps are identified, such as "the bridge deck is cast over the piers" or "the supporting structure is installed under the piers."

[0031] Data augmentation: graph expansion and association: Using GrgphRAG technology, the system performs data enhancement on nodes in the knowledge graph. Taking "bridge piers" as an example, the system can automatically find the best construction materials related to bridge piers, common problems (such as material aging, durability issues), and best practices for bridge pier construction from other construction records through enhanced generation methods.

[0032] The system searches for relevant experience data from other similar projects. For example, for bridge projects that also use reinforced concrete, the system will automatically enhance the information of the pier nodes, including "the most suitable steel strength for the piers" and "durability optimization measures".

[0033] At the same time, GrgphRAG technology helps the system automatically generate and introduce new relationships, such as identifying that "the durability of bridge piers is greatly affected by environmental humidity" and enhancing this information to the bridge pier nodes. The enhanced knowledge graph is more complete, providing more reference data for subsequent simulation and optimization.

[0034] Data Retrieval: Intelligent Query and Problem Solving: Suppose an engineer wants to query the "durability optimization suggestions for the supporting structure" of a bridge, the system can automatically perform deep search in the knowledge graph through GrgphRAG technology. The system first finds the "supporting structure" node, and then automatically searches and generates durability optimization suggestions based on the related material nodes (such as "steel") and construction environment nodes.

[0035] The system can retrieve durability improvement measures related to "support structures" from the knowledge graph. For example, in environments with high humidity, the system can recommend the use of anti-corrosion steel and recommend regular maintenance. At the same time, the system will retrieve historical data, identify optimization methods used in other similar projects, and sort them according to the actual effects of these projects, presenting the most effective suggestions to users first.

[0036] If engineers want to query common problems and optimal solutions during the construction of bridge piers, the system will use GrgphRAG technology to retrieve the corresponding historical records from the knowledge graph, such as "common foundation settlement problems during bridge pier construction" and their solutions (such as reinforcement measures, material replacement, etc.). The system combines data enhancement functions, analyzes the processing methods of similar projects through GrgphRAG technology, and automatically updates the knowledge graph, further enhancing the practicality of the data.

[0037] The above examples show how to use GrgphRAG technology in the bridge construction knowledge graph to achieve data enhancement and intelligent retrieval. Data enhancement automatically integrates relevant data of other similar projects into the knowledge graph of the current project, providing richer references for project management. Intelligent retrieval provides engineers with optimization suggestions and solutions that are highly relevant to the actual construction scene through association analysis of the knowledge graph. This approach not only improves the system's understanding of construction data, but also helps engineers predict potential problems in virtual construction and improve construction efficiency and quality.

[0038] S4. Virtual construction scene generation: Generate realistic virtual construction scenes based on knowledge graphs, simulate each step and link of the construction process, and use GrgphRAG technology to optimize the generation process; Wherein, a virtual construction scene is provided for step S5; Wherein, a construction scene is provided for step S7; Suppose in a bridge construction project, the system needs to generate a virtual construction scene of the bridge, including the construction steps of the piers, bridge deck and supporting structure. GrgphRAG technology is used here to optimize each step and link of scene generation.

[0039] 1. Basic data processing and generation scenarios (1) The system first obtains information about bridge components (such as the size, material, and location of piers, bridge decks, and supporting structures) from the knowledge graph and extracts the relationship data between construction steps and components.

[0040] (2) After preliminary analysis, these data are input into the virtual scene generation module to generate the basic scene. For example, when the bridge pier node is generated, the knowledge nodes related to the bridge pier construction will be automatically called to provide the geometric data and position of the building elements.

[0041] (3) Strategy example: The system uses 3D modeling technology to generate the geometric structure of the bridge pier based on the data in the BIM model and construction records, and simulates its installation process, including foundation excavation, support installation, pouring and other steps.

[0042] 2. Scene optimization based on GrgphRAG technology (1) Data enhancement: In the process of generating scenarios, the system uses GrgphRAG technology to retrieve relevant historical data, optimization solutions, and risk control measures from the knowledge graph. For example, during the pouring process of bridge pier construction, the system analyzes historical project data in the knowledge graph to find the best pouring materials and methods, and automatically adds these data to the scenario.

[0043] (2) Intelligent optimization algorithm: The system uses graph retrieval and enhanced generation (Graph Generation) algorithms to dynamically optimize each construction step. Assuming that GrgphRAG technology uses an optimization algorithm based on a graph convolutional network (GCN), the optimization formula is as follows:

[0044] Where: A Represents the adjacency matrix in the knowledge graph, reflecting the connection relationship between each node (such as construction steps, component types); D Represents the degree matrix, which is used to normalize the graph structure and improve the efficiency of convolution calculation. H (k) Indicates k The feature matrix of the layer. W(k) represents the weight matrix of the layer. σ represents the activation function.

[0045] Functional description: The system uses this optimization algorithm to extract local information from the construction scene and optimize based on historical data. For example, if the best time to install the bridge deck is within 24 hours after the structural support is completed, the system will generate the optimal construction steps and time based on this information in the simulation scene.

[0046] (3) Dynamic scene adjustment: GrgphRAG technology also supports dynamic adjustments based on real-time data, such as adjusting the construction sequence based on real-time sensor data (temperature, humidity, etc.) on site. If the humidity is too high when pouring the bridge deck, the system can delay the pouring step in the scenario and add humidity control measures based on best practices in historical data.

[0047] Strategy example: The system can use data augmentation to insert construction suggestions suitable for high-humidity environments into the scene (such as adding shelters and using quick-drying concrete) and automatically generate corresponding construction scene animations.

[0048] 3. Refined generation and presentation of virtual scenes (1) Based on GrgphRAG technology, the system can subdivide construction steps into subtasks. For example, bridge pier construction can be subdivided into steel support installation, formwork pouring, and concrete curing. The system performs dependency analysis on these subtasks to ensure that the timing and sequence of each construction step are optimal when the virtual scene is generated.

[0049] (2) Recursive generation algorithm: GrgphRAG technology uses a recursive generation algorithm to find the best path in the construction simulation, that is, the best combination of construction steps. For example, in the process of simulating the installation of a bridge deck, the system can recursively analyze the time interval between the installation of the support structure and the pouring of the bridge deck to generate the optimal solution.

[0050] (3) Formula application:

[0051] Where: S t represents the total time of the construction step, W i represents the workload of this step, P i Indicates the priority of the step. R i represents resource constraints, and the f function is used to adjust these variables to obtain the best schedule. The system uses this formula to analyze the resource allocation and schedule of each construction step, and dynamically adjusts it in combination with historical data in the knowledge graph to generate a construction simulation scenario with higher accuracy.

[0052] In the above process, GrgphRAG technology optimizes each link of virtual construction scene generation through data enhancement and intelligent retrieval, and improves simulation accuracy and intelligence. By realizing enhanced generation of construction data through graph convolutional networks and dynamically adjusting construction steps with recursive optimization algorithms, the system can simulate complex construction scenes and generate optimal solutions. This method provides technical support for simulation optimization of the construction process, while providing users with a more intelligent and interactive virtual construction experience.

[0053] S5. Construction process simulation and optimization: Detailed simulation and optimization of the construction process, identification of potential problems and provision of optimization suggestions, and intelligent analysis and optimization using GrgphRAG technology; Among them, providing simulation results and optimization suggestions for step S6; Wherein, optimization data is provided for step S8; Assume that in a bridge construction project, the system needs to simulate and optimize the construction process of the bridge pier to ensure the efficiency and quality of the construction. GrgphRAG technology is used to intelligently analyze possible problems during the construction process and provide optimization suggestions based on historical data.

[0054] 1. Detailed simulation of the construction process (1) Data input: The system extracts the design parameters of the bridge piers (such as size and material) from the BIM model, obtains the construction steps (such as foundation treatment, steel bar erection, concrete pouring, etc.) from the construction plan, and obtains real-time environmental data (such as temperature and humidity) from sensors.

[0055] (2) Simulation of construction steps: In the virtual scene, the system gradually simulates the various steps of pier construction. GrgphRAG technology automatically retrieves the optimal construction sequence and method based on the information in the knowledge graph.

[0056] (3) Environmental simulation: For example, when the system detects that the current humidity is high, it will dynamically simulate the effect of humidity on the setting time of concrete during the pouring process. In this process, GrgphRAG technology associates environmental data with historical data in the knowledge graph to generate a construction step simulation that best meets the current conditions.

[0057] 2. Intelligent problem identification and optimization (1) Problem identification: The system uses the reasoning power of the Graph Convolutional Network (GCN) in GrgphRAG technology to predict potential problems during the construction process. For example, by analyzing historical data on bridge pier construction, the system discovered the potential risk that the construction time of the steel structure exceeded expectations, which may be caused by delayed material supply or insufficient steel processing.

[0058] GCN’s reasoning formula:

[0059] Where: A is the adjacency matrix in the knowledge graph, D is the degree matrix, H (k) For the k The feature matrix of the layer W(k) is the weight matrix and σ is the activation function.

[0060] Practical application: The system uses this formula to analyze the various steps, process relationships and resource requirements of bridge pier construction layer by layer, and finds the material supply risks of the steel structure step. Through these predictions, the system can dynamically display possible delays and bottlenecks during the simulation process. (2) Data enhancement and optimization: The system further enhances construction data based on GrgphRAG technology and provides optimization suggestions for the construction process by combining multi-project knowledge graph data. For example, when the system finds that the setting time of concrete is prolonged under high humidity conditions, it will automatically suggest the construction team to add a fast setting agent or postpone the pouring time to reduce the impact of humidity on construction quality.

[0061] Algorithm optimization: The system uses an optimization algorithm based on graph retrieval to analyze each step of pier construction and recommend the optimal resource allocation and time schedule. Assume that the optimization formula is as follows:

[0062] In the formula, C represents the total cost and time expenditure of the construction process, W i is the weight factor of the 1st construction task; T i For construction time, R i For resource consumption.

[0063] The system uses a formula to calculate the optimal time and resource consumption for each construction task to minimize the total cost. Taking the pouring of bridge piers as an example, after system optimization, it was found that delaying the pouring time by 2 hours can reduce the impact of humidity on materials, thereby improving construction quality.

[0064] 3. Generation of dynamic optimization suggestions (1) Real-time adjustment: The system makes dynamic adjustments based on real-time data during the construction simulation. For example, if the system detects a shortage of materials during the erection of pier reinforcement during the simulation, GrgphRAG technology automatically recommends adjusting the supply plan or calling for alternative materials based on historical data.

[0065] (2) Generation of optimization schemes: Based on multi-objective optimization algorithms (such as genetic algorithms), the system generates multiple optimization schemes for users to choose from. Assuming that the optimization objectives are construction time and material costs, the system calculates the comprehensive costs of multiple schemes and selects the best scheme through genetic algorithms:

[0066] Where: F(x) represents the optimization objective function; TFor construction time; C For material cost; α and β is the weight coefficient used to balance time and cost.

[0067] The system dynamically adjusts based on historical data α and β The system can adjust the target weights to suit different construction conditions. For example, if the bridge pier construction must be completed on time, the system will increase α Weight, taking time as the main optimization goal, and generating a construction plan that prioritizes time.

[0068] This example demonstrates how to use GrgphRAG technology to perform detailed construction simulation and optimization during the construction of bridge piers. Through the reasoning function of the graph convolutional network, the system can identify potential problems; and through data enhancement, dynamic adjustment and multi-objective optimization algorithms, the system provides a series of optimization solutions. These optimization solutions are visualized in 3D scenes in a virtual environment, and users can interact and adjust solutions in real time in VR / AR, thereby improving construction efficiency and quality and providing intelligent support for project managers.

[0069] S6. VR / AR integration: Integrate virtual reality and augmented reality technologies to provide immersive experience and real-time interaction, and use GrgphRAG technology to enhance user interaction experience; Assume that users participate in the virtual construction process of bridge construction through VR / AR devices (such as VR headsets or AR glasses). The system integrates GrgphRAG technology to provide an immersive experience and uses knowledge graphs and data enhancement functions to achieve real-time interaction.

[0070] Construction of VR / AR immersive scenes (1) The system generates a 3D model of the bridge construction, including piers, deck, and supporting structure, and displays it in a VR / AR environment.

[0071] (2) Users wear VR headsets and enter a virtual scene, where they can view every detail of the bridge construction in 360 degrees. Through AR glasses, users can also overlay virtual models on the real construction site to view the construction progress in real time and compare it with the actual situation.

[0072] (3) Scene generation: The system generates a virtual model of the bridge through BIM model data and real-time sensor data, and dynamically updates it based on real-time data. For example, when the construction progress reaches the pier construction stage, the virtual scene automatically displays the 3D model of the pier and its components.

[0073] Intelligent interaction enhanced by GrgphRAG technology (1) Interaction method: Users can interact with the virtual scene through gesture control and voice commands in the VR / AR environment. When the user selects a bridge pier, the system will display relevant construction data and provide actionable interaction options.

[0074] (2) Data enhancement and intelligent feedback: The system uses GrgphRAG technology to retrieve relevant information about bridge pier construction from the knowledge graph and enhance the user experience. For example, when a user clicks on a bridge pier, the system immediately displays the construction material information of the pier (such as concrete strength and the type of steel bars used) and provides optimization suggestions.

[0075] (3) Intelligent feedback algorithm: The system provides intelligent feedback and interactive optimization through the following formula:

[0076] Where: R feedback is the response weight of the system feedback; w i is the weight value of each construction step (such as the importance in bridge pier construction); S i It is the relevant data score of the construction steps in the knowledge graph.

[0077] Practical application: The system provides different levels of information based on the feedback weight value. If the importance of a pier is high, the system will automatically display the detailed construction history of the component and provide relevant construction suggestions.

[0078] Real-time problem detection and optimization suggestions (1) Dynamic detection: The system detects possible problems that may occur during construction in real time in VR / AR scenarios. For example, if the humidity is too high during the pouring of the bridge deck, the system analyzes the data using GrgphRAG technology to identify potential delay risks.

[0079] (2) Intelligent suggestion generation: The system uses a multi-objective optimization algorithm to provide users with solutions and visualizes them in a VR / AR environment. For example, the system recommends adding humidity control equipment or adjusting the construction time:

[0080] Where: O suggestion The comprehensive score of the optimization suggestion; T delay The possible construction delays; C cost Cost of controlling humidity; α and β is the weight coefficient used to balance time and cost.

[0081] The system displays the optimization plan in the form of graphics and text prompts in the VR / AR environment, and users can see the recommended construction time adjustment, required equipment and estimated costs.

[0082] Enhanced user interaction (1) Real-time interaction: When users view the pier model in the AR environment, they can select different parts of the model (such as the pier reinforcement and concrete layer) through gestures. GrgphRAG technology automatically extracts detailed data of the corresponding parts from the knowledge graph, such as the reinforcement model and supplier information, and displays it on the AR interface.

[0083] (2) Virtual interaction and information prompts: The system implements intelligent prompt functions in virtual scenes. When the user clicks on the pier support structure, the system calculates the bearing capacity of the support structure in real time and displays "whether the bearing capacity meets the safety standards" through a virtual prompt box. The user can choose to view detailed information, and the system will call similar component data from other projects from the knowledge graph for comparative analysis and provide more specific optimization suggestions.

[0084] (3) Personalized recommendations: By analyzing user behavior and preferences, GrgphRAG technology can intelligently recommend optimization measures that best suit the current construction scenario. For example, if users frequently query bridge deck material information, the system will automatically display the bridge deck material optimization measures and update status in a prominent position so that users can view them at any time.

[0085] Intelligent navigation and scene switching (1) VR Navigation: In a VR environment, users can switch between construction areas through virtual navigation. GrgphRAG technology recommends the next construction step or area of ​​possible interest based on the user’s current scene.

[0086] (2) Scene switching optimization: GrgphRAG generates personalized recommendation paths based on user behavior data. For example, after a user views the construction details of a bridge pier, the system recommends that the user view the installation of the supporting structure based on the relationship analysis of the knowledge graph to ensure that the construction progress meets the design requirements.

[0087] (3) Intelligent navigation algorithm: Path optimization based on data recommendation:

[0088] In the formula, P opt represents the optimal value of the recommended path; d i The distance the user moves in the virtual scene; The system recommends the shortest path to users based on a formula, allowing them to quickly switch between scenes while ensuring that the order of construction steps is reasonable.

[0089] In the above process, GrgphRAG technology enhances the intelligent interaction capabilities in VR / AR scenes through knowledge graphs, providing users with multi-level construction information and optimization suggestions. Through real-time detection and personalized feedback algorithms, the system can identify potential problems in the virtual construction environment and provide solutions. Users can experience the construction process immersively through VR / AR devices and use virtual prompts and optimization suggestions to improve decision-making efficiency.

[0090] S7, Visual Interaction: Provides an intuitive user interface to display construction progress, simulation results and optimization suggestions, and uses GrgphRAG technology for intelligent data display; Assume that users view the overall progress of a bridge construction project, the detailed process of construction simulation, and obtain optimization suggestions provided by the system through the system interface. The system uses GrgphRAG technology to display data in the form of graphics, charts, and 3D models, allowing users to intuitively understand the construction status and make real-time adjustments.

[0091] 1. Intelligent display of construction progress (1) Progress display: The system uses a progress bar and Gantt chart on the user interface to display the overall construction progress of the project. Each construction stage (such as pier construction, bridge deck pouring, etc.) has a corresponding node, which displays the construction start time, estimated completion time and current progress.

[0092] (2) Data integration and enhancement: GrgphRAG technology obtains historical construction progress data from the knowledge graph and automatically enhances the progress display of the current project. For example, the system compares the construction progress of other projects with the current project. If a construction step has a risk of delay, the system will automatically mark and highlight it on the progress bar.

[0093] (3) Progress prediction: The system predicts future progress through historical data modeling and regression analysis. For example, the remaining construction time is predicted through the following regression model:

[0094] In the formula, T remaining represents the remaining time of the prediction, D current Indicates the current amount of completed construction. α and β is the regression coefficient based on historical data.

[0095] The system updates the progress bar in real time based on the prediction model, allowing users to better understand the construction status and estimated completion time.

[0096] 2. 3D display of construction simulation results (1) 3D model generation: The system displays the 3D model of the bridge in the user interface based on the BIM model and real-time construction data. Users can rotate and zoom the model to view the construction status from different angles.

[0097] (2) Dynamic data overlay: GrgphRAG technology overlays sensor data (such as temperature, humidity, vibration, etc.) into the three-dimensional model in real time. For example, when the user clicks on a bridge pier, the system displays the current vibration data of the pier and intuitively demonstrates its safety through a thermal map.

[0098] (3) Algorithm support: The system uses an enhanced display algorithm based on graph neural networks to compare and analyze local data of the 3D model with historical data. For example, the system overlays colors on the thermal map of the bridge pier to indicate vibration changes:

[0099] In the formula, A ij Represents the pier node i Vibration value; A i is the connection relationship in the adjacency matrix; h j Represents the characteristics of neighbor nodes.

[0100] The system uses this formula to calculate the vibration impact range of the bridge pier and displays the vibration intensity in a gradually changing color manner, allowing users to intuitively understand the vibration distribution and the stress conditions of the bridge pier.

[0101] In order to more specifically illustrate how to use GrgphRAG technology in visual interaction to achieve intelligent data display, a detailed example is provided below. This example shows how to intuitively display the construction progress, simulation results and optimization suggestions on the user interface of a bridge construction project to enhance the user experience.

[0102] 3. Intelligent display of optimization suggestions (1) Suggestion generation: The system generates optimization suggestions based on real-time data analysis to help users adjust construction strategies. For example, when the humidity of the bridge deck pouring is too high, the system automatically recommends delaying the pouring time or adding humidity control measures according to priority.

[0103] (2) Intelligent sorting and display: GrgphRAG technology retrieves knowledge graph data to assist in obtaining contextual information for recommendations, and uses a weighted scoring algorithm to prioritize recommendations. The most important recommendations are displayed first. The formula for calculating recommendation priority is as follows:

[0104] S optIndicates the priority score of the optimization suggestion; w i Indicates the weight of the recommendation; R i Indicates the benefit of the proposal.

[0105] The system uses this formula to sort multiple suggestions and highlights the highest priority suggestions on the user interface. Users can choose to view detailed information, including the implementation steps of the suggestions, expected results, etc.

[0106] Visual cues: The system uses colors and markers on the 3D model to display recommended areas. For example, if the system recommends a delay in the bridge deck pour, a red warning marker is displayed on the bridge deck area in the 3D model, along with a "High Moisture Impact" alert and a list of recommended control measures.

[0107] In the above process, GrgphRAG technology provides intelligent support for the visual interaction of the system, enabling users to intuitively understand the construction progress and optimization suggestions through automatic data enhancement, real-time feedback and intelligent sorting. Based on graph neural networks and sorting algorithms, the system updates and displays construction data in real time, and generates personalized optimization suggestions based on user selections. Through the combined display of 3D models and charts, users can easily obtain key information and interact in real time in virtual construction scenes, thereby improving decision-making efficiency and the level of intelligent construction management.

[0108] S8. Knowledge base update and maintenance: Ensure the timeliness and accuracy of the knowledge graph and related data, automatically update the knowledge base content, use GrgphRAG technology to automatically update and maintain, and provide the updated knowledge graph data to step S3.

[0109] Assume that in a bridge construction project, the system needs to ensure that the data in the knowledge graph is synchronized with the actual construction information and automatically update the knowledge base content according to the new data. GrgphRAG technology is used to obtain updated information from multiple data sources, intelligently maintain the knowledge base, and feed back the latest knowledge graph data to step S3 for building the latest virtual construction scene.

[0110] 1. Automatic data collection and analysis (1) The system collects data from multiple data sources, including BIM models, construction logs, sensor data (such as temperature, humidity, and vibration data), and external data (such as weather forecasts and supplier updates).

[0111] (2) For example, when the system detects a newly uploaded construction log (containing the latest progress of bridge pier construction), GrgphRAG technology first performs natural language processing (NLP) on the text data, parses the key information, and compares it with the existing data in the knowledge graph.

[0112] (3) Parsing algorithm: GrgphRAG technology uses graph convolutional networks (GCN) to automatically parse data, map new data in the construction log to the corresponding nodes in the knowledge graph, and determine whether it needs to be updated.

[0113]

[0114] In the formula, A Represents the adjacency matrix in the knowledge graph, reflecting the connection relationship between each node (such as construction steps, component type). D Represents the degree matrix, which is used to normalize the graph structure and improve the efficiency of convolution calculation. (k) Indicates k The feature matrix of the layer. (k) represents the weight matrix of this layer. σ represents the activation function.

[0115] Practical application: The system compares the new data with the existing knowledge graph data layer by layer according to the formula, automatically identifies the nodes that need to be updated and modifies them.

[0116] 2. Data augmentation and automatic updating (1) Data enhancement: When the construction log mentions that the pier construction progress is ahead of schedule, the system retrieves similar projects from the historical knowledge graph through GrgphRAG technology, and automatically adds the successful experience of these projects (such as specific measures to optimize construction time) to the pier node to form data enhancement.

[0117] (2) Data update strategy: The system analyzes the difference between each new data and historical data, and uses the following formula to determine the data difference and decide whether to update:

[0118] In the formula, U Indicates data differences. D neu,i Represents new data, D old,i Indicates old data.

[0119] The system sets a threshold, and when U exceeds the threshold, it automatically triggers the knowledge graph update operation. For example, when the construction material of a bridge pier is replaced, the system finds that the new material has significant differences in strength or durability from the original material, and the system will automatically update the node data and record the reason for the change.

[0120] In order to illustrate in detail how to use GrgphRAG technology to automatically update and maintain the knowledge graph in knowledge base update and maintenance, and ensure the timeliness and accuracy of the data, a specific example is provided below to demonstrate the application of this function in a construction project.

[0121] 3. Knowledge base maintenance and timeliness check (1) Timeliness check: The system regularly checks the data in the knowledge graph to ensure that there is no outdated information. For example, the system sets a check cycle for the material information of the pier node. If it is not updated within the preset time, it will automatically trigger data re-collection.

[0122] (2) Maintenance algorithm: GrgphRAG technology uses a graph embedding algorithm to embed each node into a low-dimensional space and analyze the timeliness differences between nodes.

[0123]

[0124] In the formula, E is the embedding energy function, which is used to measure the temporal consistency of adjacent nodes; A ij Representation Node i With Node j connection relationship; f(x) is the embedding mapping function.

[0125] Practical application: The system uses this formula to detect the timeliness difference between the pier node and other nodes, automatically marks those nodes with low timeliness, and triggers regular update or deletion operations.

[0126] In order to illustrate in detail how to use GrgphRAG technology to automatically update and maintain the knowledge graph in knowledge base update and maintenance, and ensure the timeliness and accuracy of the data, a specific example is provided below to demonstrate the application of this function in a construction project.

[0127] 4. Data synchronization and feedback mechanism (1) Data synchronization: During the construction process, the system uses GrgphRAG technology to ensure real-time synchronization of knowledge base data with sensor data and construction logs. For example, when the sensor records changes in the vibration data of the bridge pier, the system synchronizes the data to the bridge pier node and updates the vibration characteristics.

[0128] (2) Feedback mechanism: After the update is completed, the system feeds back the new knowledge graph data to step S3 to generate the latest virtual construction scene. GrgphRAG technology analyzes data changes during the construction process, which will help generate more accurate simulation scenes.

[0129] (3) Feedback mechanism formula:

[0130] In the formula, F update Indicates the priority of feedback updates; wk is the importance weight of the updated data; k For updates in the knowledge graph.

[0131] Application scenario: The system calculates the priority of feedback updates and promptly feeds back high-priority update data to the virtual construction scene. Taking the update of bridge pier materials as an example, the system calculates that the updated material is of high importance and gives priority to applying the updated content to the virtual scene.

[0132] 5. Visualization of automatic updates of knowledge base (1) The system displays the update status of the knowledge base on a 3D visualization platform. Each time an update is made, users can view the historical changes of construction nodes in the virtual scene and understand the reasons and effects of each update.

[0133] (2) Update log management: The system generates a log for each knowledge base update, including the update content, update source, and operation time. Users can select any construction node in the virtual scene to view the detailed update history, which is convenient for subsequent recording and review.

[0134] Through the above process, the role of GrgphRAG technology in knowledge base update and maintenance is reflected. The system automatically collects data from multiple data sources, performs data enhancement and timeliness check through data difference analysis and graph embedding algorithm, and triggers data update and synchronization when necessary. At the same time, the updated knowledge graph data is fed back to the virtual construction module (step S3) to provide support for the next simulation. Through the system's automated update and maintenance process, the timeliness and accuracy of the knowledge base are ensured, providing technical support for the intelligent management of bridge construction.

[0135] In summary, this embodiment proposes an application method based on GrgphRAG in a virtual construction system. By introducing GrgphRAG technology, accurate data retrieval and enhanced generation are achieved, the system's simulation, intelligent analysis capabilities, and user interaction experience functions are improved, and the real-time update of the knowledge base is ensured. The specific technical problems solved include: Achieve high data integration: Through GrgphRAG technology, the system can extract and integrate information from multiple data sources, eliminate information islands, achieve seamless data sharing and interoperability, and improve data utilization efficiency and decision-making accuracy.

[0136] Enhanced simulation and optimization capabilities: By introducing GrgphRAG technology, the system can efficiently simulate and optimize the construction process. Using advanced simulation tools and optimization algorithms, the system can accurately predict potential problems in the construction process and provide optimized construction plans to improve construction efficiency and quality. The simulation module can reflect the dynamic changes of the construction site in real time, and the optimization module can adjust the construction plan in time according to the simulation results to ensure the continuity and efficiency of the construction process.

[0137] Improve intelligent analysis capabilities: Use GrgphRAG technology to further optimize simulation results and improve construction efficiency and quality. The module supports real-time monitoring and dynamic adjustment to ensure the continuity and smoothness of the construction process and provide timely optimization suggestions and feedback.

[0138] Optimize user interaction experience: Enhance user interface design through GrgphRAG technology to make the user interface more intuitive and interactive, allowing users to quickly obtain required information and improve operational and management efficiency.

[0139] Ensure real-time updating of the knowledge base: GrgphRAG technology can automatically retrieve and integrate the latest industry information and project data, dynamically update the knowledge graph content, ensure that the knowledge base always reflects the latest industry standards and project requirements, and improve the scientificity and effectiveness of decision-making.

[0140] Through these improvements, the present invention significantly enhances the capabilities of the virtual construction system in data integration, simulation optimization, intelligent analysis, user experience, and knowledge management, and provides a set of efficient and intelligent solutions for modern construction project management.

[0141] Embodiment 3 This embodiment 3 takes a specific project as an example to specifically explain the application process of the present application.

[0142] Project Background and Environment 1. Project Overview: This example takes a bridge construction project as an example, the goal is to build a reinforced concrete bridge with a span of 120 meters. The project involves a complex construction process, including the construction of bridge piers, bridge decks, and supporting structures, and the project requires a construction period of 6 months.

[0143] 2. Data sources: The system integrates BIM model data, real-time sensor data (such as temperature, humidity, vibration), construction logs and meteorological data for the generation and optimization of the virtual construction process.

[0144] 3. Construction site environment: Temperature and humidity sensors are installed on site, construction logs record progress every day, and the system automatically obtains meteorological data and analyzes its impact on construction. Construction materials mainly include C30 concrete, steel bars and anti-corrosion coatings.

[0145] Method flow 1. Data collection and knowledge graph construction: (1) BIM model data import: The system first imports bridge component data from the BIM model, including the geometry and material properties of piers, bridge decks, steel skeletons, etc.

[0146] (2) Sensor data integration: The temperature sensor and humidity sensor update data every 15 minutes. The system receives the data through the MQTT protocol and stores it in the real-time database.

[0147] (3) Construction log analysis: The system automatically extracts the content of the construction log every day, uses natural language processing (NLP) technology to extract key information, such as completion progress, construction problems encountered, etc., and converts it into structured data in the knowledge graph.

[0148] (4) Knowledge graph construction: The system generates a knowledge graph based on the above data, with components such as bridge piers and bridge decks as nodes, and construction steps (such as "casting" and "installation") as relationships connecting component nodes to form a complete graph of the construction process. Through GrgphRAG technology, historical project data is integrated with the best practices of other similar projects to enhance the graph content.

[0149] 2. Virtual construction scene generation and simulation: (1) Scenario generation: The system uses the BIM model to generate a three-dimensional model of the bridge and overlays sensor data for dynamic display. For example, a thermal map of temperature data is overlaid on top of the bridge pier to display the temperature distribution in the construction area in real time.

[0150] (2) Simulation steps: The specific steps of bridge pier construction include steel bar installation, formwork setting, concrete pouring, maintenance, etc. The system simulates these steps in sequence.

[0151] Taking concrete pouring as an example, the system will consider the impact of temperature and humidity data on the pouring process. Assuming that the humidity on that day is 85%, the system retrieves historical data through GrgphRAG technology to determine that the pouring time may be extended under this humidity condition, and thus automatically adjusts the construction time during the simulation process.

[0152] (3) Algorithm support: The system uses an enhanced generation algorithm based on graph convolutional networks to simulate the construction process. The formula is as follows:

[0153] In the formula, A is the adjacency matrix, D is the degree matrix, and H is (k) is the k-th layer feature matrix, W (k)is the weight matrix, and σ is the activation function. The system uses this algorithm to optimize the generation and data association of each node during the construction process to ensure the correct position and state of the components in the virtual scene.

[0154] 3. Intelligent problem identification and optimization suggestion generation: (1) Problem identification: The system identifies construction risks during the simulation process, such as detecting a possible delay in material supply during the bridge deck installation process. Based on historical data in the knowledge graph, the system determines the impact of this delay on the overall progress and marks the risk.

[0155] (2) Generation of optimization suggestions: The system uses a multi-objective optimization algorithm to generate construction suggestions. For example, when the system identifies that high humidity may cause delayed concrete solidification, it will give suggestions to add quick-drying agent or delay pouring.

[0156] (3) Optimization formula:

[0157] In the formula, C is the optimization objective function, S is the safety weight, T is the time weight, and R is the cost weight. s 、u t 、w c are the weight coefficients.

[0158] The system adjusts the weight parameters according to the construction requirements and environmental conditions, and generates optimization suggestions that meet different conditions. If time is the primary condition, the system will increase the weight of wt and give priority to generating time-saving construction plans.

[0159] 4. Optimize result display and user interaction: (1) Visual display: The system displays the optimized construction steps on a three-dimensional model, and users can view detailed data for each step through VR devices. For example, when the system recommends the use of a quick-drying agent, users can view the specific amount to be added, how to use, and other information on the model.

[0160] (2) User interaction: Users can choose to view different optimization suggestions on the interface and view detailed information by clicking on the construction node. The system uses GrgphRAG technology to provide users with historical construction cases of similar projects for reference.

[0161] (3) Real-time feedback: After the user accepts the suggestion, the system automatically records the feedback and updates the information to the knowledge graph, providing optimization reference for future projects.

[0162] 5. Knowledge base update and maintenance: (1) The system continuously collects data and updates the knowledge graph during the construction process. For example, each time new data is recorded in the construction log, it is automatically parsed and updated to the graph to keep the knowledge base real-time.

[0163] (2) Update algorithm: The system uses a graph embedding algorithm to dynamically update node weights to ensure the real-time and effectiveness of new construction conditions and environmental data in the graph.

[0164]

[0165] Where W i new is the updated weight, W i old is the old weight, D new and D old For new and old data, α is the adjustment factor. The system uses this formula to ensure that new data is updated first and the timeliness of the knowledge base is guaranteed.

[0166] Through the above method, the system realizes the whole process of data collection, knowledge graph construction, virtual construction simulation, intelligent optimization and dynamic update in bridge construction. GrgphRAG technology helps the system to enhance the generation of construction suggestions using historical data and optimize each step in the virtual construction process through intelligent algorithms. This embodiment shows how the system can automatically generate optimization suggestions and update the knowledge graph in real time, helping users to efficiently manage the construction process in a virtual environment, and providing technical support for the intelligent management of construction projects.

[0167] 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 virtual construction simulation and optimization system based on GrgphRAG, characterized in that: include: Data acquisition module, data preprocessing module, knowledge graph construction module, virtual construction scene generation module, construction process simulation and optimization module, VR / AR integration module, visualization interaction module and knowledge base update and maintenance module; The data acquisition module is connected to the data preprocessing module, and the data acquisition module is used to collect data from BIM models, CAD drawings, sensor data, construction records and historical project data, and feed the collected data back to the data preprocessing module; The data preprocessing module is connected to the knowledge graph construction module, and the data preprocessing module cleans, converts the format and removes duplicates of the collected data, and gives the preprocessed data to the knowledge graph construction module; The knowledge graph construction module is connected to the virtual construction scene generation module, and the knowledge graph construction module is used to extract architectural elements and relationships, construct a comprehensive knowledge graph, and use GrgphRAG technology for data enhancement and retrieval; The virtual construction scene generation module is connected to the construction process simulation and optimization module and the visualization interaction module respectively. The virtual construction scene generation module generates a realistic virtual construction scene based on the knowledge graph, simulates each step and link of the construction process, and uses GrgphRAG technology to optimize the generation process; the virtual construction scene generation module provides the virtual construction scene to the construction process simulation and optimization module, and the virtual construction scene generation module provides the construction scene to the visualization interaction module; The visual interaction module is used to provide an intuitive user interface to display construction progress, simulation results and optimization suggestions, and use GrgphRAG technology for intelligent data display; The construction process simulation and optimization module is connected to the VR / AR integration module and the knowledge base update and maintenance module respectively. The construction process simulation and optimization module simulates and optimizes the construction process in detail, identifies potential problems and provides optimization suggestions, uses GrgphRAG technology for intelligent analysis and optimization, and provides simulation results and optimization suggestions to the VR / AR integration module. The construction process simulation and optimization module provides optimization data to the knowledge base update and maintenance module; The VR / AR integration module is used to integrate virtual reality and augmented reality technologies to provide immersive experience and real-time interaction, and uses GrgphRAG technology to enhance user interaction experience; The knowledge base update and maintenance module is connected to the knowledge graph construction module to ensure the timeliness and accuracy of the knowledge graph and related data, automatically update the knowledge base content, use GrgphRAG technology to automatically update and maintain, and provide the updated knowledge graph data to the knowledge graph construction module.

2. A virtual construction simulation and optimization method based on GrgphRAG, characterized in that: The GrgphRAG-based virtual construction simulation and optimization system described in claim 1 is used to realize data collection, data preprocessing, knowledge graph construction, virtual construction scene generation, construction process simulation and optimization, VR / AR integration, visual interaction, knowledge base update and maintenance.

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