Virtual construction system based on BIM progress plan simulation

Abstract

The invention discloses a virtual construction system based on BIM progress plan simulation, and the three-layer architecture comprises a data layer which is in butt joint with a DDE platform, a Prunus system and a PDM database through a standard interface; the technical support layer is built based on C # and NETCore technologies, and a data mapping engine and a business logic processor are arranged in the technical support layer; a service presentation layer, HTML, JS, CSS and VUE technology construction; the core function assembly comprises an LF space box model generator, a multi-dimensional data mapping engine, a four-view comparison analyzer and a dynamic simulation engine. The method has the beneficial effects that a qualitative leap is realized in the aspects of data integration and processing efficiency, and intelligent fusion of multi-source heterogeneous data is realized by establishing a standardized PBS-CBS-LF mapping rule base and an automatic data association engine; on the premise that progress semantics are reserved, the LF space box model enables Web end loading time to be effectively controlled, meanwhile, smooth interaction under the condition that the component scale is large is supported, and the performance dilemma of a traditional BIM system in a large scene is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of building engineering information technology, and in particular to a virtual construction system based on BIM schedule simulation. Background Technology

[0002] While existing technologies have achieved basic BIM model-to-schedule association, they suffer from significant shortcomings in practical applications: First, data integration is insufficient. Model, schedule, and cost data are stored in heterogeneous systems, lacking unified standards and real-time interfaces, creating data silos. The association process relies on manual intervention, leading to inefficiency and a high risk of errors. Second, there is an imbalance between visualization performance and usability. Macro-level schedule displays require loading a fully detailed BIM model, resulting in slow rendering and hindering real-time, smooth interaction on the web for large-scale projects. Existing lightweight technologies are largely limited to geometric simplification, lacking new model structures capable of simultaneously supporting schedule semantics. Third, schedule management suffers from low intelligence, relying on periodic reports and manual experience for deviation analysis, resulting in a delayed and subjective process. The system struggles to support automated comparisons of planned and actual progress across multiple dimensions, including spatial and professional dimensions, making it difficult to quickly pinpoint the root causes of deviations and leading to untimely risk warnings. Finally, simulation and verification capabilities are limited. Construction simulations are mostly static animations, lacking interactive simulations based on real-time data, making it difficult to effectively support scheme pre-visualization and optimization.

[0003] In the field of digital construction engineering, existing technologies have attempted to achieve visualization and informatization of project management through various means. Mainstream 4D BIM systems can link 3D models with schedules to achieve visual simulation of the construction process; various lightweight BIM engines improve the loading performance of large models on the web through geometric simplification, instantiation, and other technologies; project management software also provides comparative analysis functions between planned and actual progress. However, these existing technologies still have significant shortcomings in solving practical problems: First, model data, schedule data, and business data are scattered across different systems, lacking an effective automatic mapping mechanism, forming data silos and resulting in low information collaboration efficiency; second, traditional lightweight model methods mainly focus on geometric simplification and fail to establish a new model structure that takes into account both spatial hierarchy and schedule semantics from a project management perspective, making macro-level schedule display still reliant on cumbersome model data processing; existing schedule management is mostly limited to comparative analysis at the report level, unable to achieve accurate positioning and visual traceability of planned and actual progress in three-dimensional space; construction process simulation is mostly based on preset animations, lacking dynamic simulation and interactive analysis capabilities based on real-time schedule data. These technical deficiencies collectively lead to the overall inadequacy of current virtual construction systems in terms of data fusion depth, visualization efficiency, analysis accuracy, and interactive flexibility, which urgently require breakthroughs through technological innovation.

[0004] Therefore, it is necessary to propose a virtual construction system based on BIM schedule simulation to address the aforementioned technical problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a virtual construction system based on BIM schedule simulation to solve the above problems.

[0006] A virtual construction system based on BIM schedule simulation includes a three-tier architecture and is equipped with core functional components. The three-tier architecture includes: The data layer connects to the DDE platform, Puhua system and PDM database through standardized interfaces, automatically collects heterogeneous data from multiple sources, and is equipped with a Redis caching module; The technical support layer is built on C# and .NET Core technologies, with a built-in data mapping engine and business logic processor to complete the structure mapping and progress status calculation of LF / CBS / PBS. The business presentation layer is built on HTML, JS, CSS and VUE technologies, and provides a multi-view visualization interface based on the WebGL graphics engine; The core functional components include an LF space box model generator, a multi-dimensional data mapping engine, a four-view comparison analyzer, and a dynamic simulation engine.

[0007] The LF spatial box model generator can perform spatial topology analysis and semantic extraction on BIM models, generating a lightweight spatial structure tree with hierarchical relationships, and reducing the model data volume by 70%-80% while preserving the progress semantics.

[0008] The multi-dimensional data mapping engine automatically establishes the association between model components and schedule tasks and cost items based on a predefined coding rule library.

[0009] The four-view comparison analyzer supports synchronous rendering and linkage operations of delayed status, planned completion, actual completion, and reference model.

[0010] The dynamic simulation engine drives model state changes based on the progress timeline, enabling interactive simulation of the construction process.

[0011] The algorithm steps of the LF spatial box model generator are as follows: S1. Preprocessing of BIM model component geometry and attributes: Normalize the component data of the original BIM model and extract the basic geometric parameters and initial attributes of the components. S2. Calculate the spatial topology of components, construct the spatial adjacency and hierarchical relationships between components, and provide a topological basis for subsequent hierarchical division; S3. Progress semantic extraction and mapping: bind progress tasks with components and assign progress semantic attributes to space boxes; S4. Hierarchical LF space box partitioning and generation: Based on the topological clustering results, multi-level LF space boxes are generated from bottom to top to achieve hierarchical and lightweight model. S5, Model Lightweight Compression and Structure Tree Construction.

[0012] Step S11 involves extracting component geometric parameters for any BIM component. Extract its vertex set , For three-dimensional coordinates, the minimum bounding box of a component is calculated using the following formula: ;in This represents any component in the Building Information Model (BIM). Representation of components The The three-dimensional spatial coordinates of each vertex; These are the coordinates of the vertex on the x (horizontal), y (horizontal), and z (vertical) axes, respectively. Then the component The bounding box space range is ; in For components The spatial range in the x-axis (horizontal) direction; For components The spatial range in the y-axis (horizontal longitudinal direction); For components The spatial range in the Z-axis (vertical height) direction; Extract components Basic attributes and initial identifiers associated with progress The discrete attributes are encoded using the following formula: ; in The index value corresponding to the attribute category. These are the minimum and maximum values ​​of the professional category index, respectively.

[0013] Step S12 involves constructing the spatial adjacency matrix and defining the adjacency determination threshold. For any two components Calculate the center distance of its bounding box. , indicating components With components The three-dimensional spatial distance between the centers of the bounding boxes;

[0014] in They are components The center coordinates of the bounding box along the x, y, and z axes; , Similarly, for components The center coordinates of the bounding box in the y and z directions; It is a component The center coordinates of the bounding box along the x, y, and z axes; like Then determine and Spatial adjacency, adjacency matrix elements ,otherwise ,Right now: .

[0015] This includes virtual construction methods, the steps of which are as follows: S11. Data Acquisition and Association: The system acquires multi-source data through the data layer, and the multi-dimensional data mapping engine automatically establishes and maintains data associations. S12, Multi-dimensional filtering interaction: Users can filter data through LF tree and CBS tree, and the system updates the model display in real time; S13. Progress comparison analysis: The system renders four model states in parallel to realize three-dimensional visualization and traceability of progress deviation. S14. Dynamic construction simulation: The system drives the evolution of the model state according to the time sequence, and completes the backtracking of the construction process or the pre-scheme simulation.

[0016] The multi-dimensional data mapping engine algorithm steps are as follows: S21. Multi-source data coding standardization: The basic information of BIM components, schedule tasks, cost items, and LF space boxes is uniformly coded to provide standardized identification for subsequent mapping. S22. Mapping rule base construction and matching: Based on engineering business logic, a multi-dimensional mapping rule base is constructed, and preliminary data association is achieved through rule matching. S23, the association between the LF spatial hierarchy and multi-dimensional data, binds the already associated "component-schedule-cost" data with the LF spatial box in spatial semantics, and realizes four-dimensional data mapping; S24. Dynamic verification and updating of mapping relationships: Establish a real-time verification mechanism for mapping relationships to ensure the timeliness and accuracy of data association. S25. Generation and output of mapping association library: The final multi-dimensional mapping relationship is stored as a structured association library for the virtual construction system to call; Representing BIM components Progress task Cost items The comprehensive correlation weight among the three; the formula is: ; Where Q is the total number of rules in the rule base. Let q be the confidence level of rule q; This indicates the BIM component under the q-th mapping rule. Progress task Cost items The single-rule association weights of the three.

[0017] Compared with existing technologies, the virtual construction system created by this invention has the following significant advantages and positive effects: This invention represents a qualitative leap in data integration and processing efficiency. Traditional methods require manual data docking and association between different systems, which is time-consuming and prone to errors. This invention achieves intelligent fusion of multi-source heterogeneous data by establishing a standardized PBS-CBS-LF mapping rule base and an automated data association engine.

[0018] Significant breakthroughs have been achieved in visualization performance and user experience. Addressing the pain points of slow loading and sluggish interaction in traditional BIM systems in large-scale scenarios, the innovative LF spatial box model of this invention reduces the model data volume by 70%-80% while preserving progress semantics. This effectively controls the loading time on the web end and supports smooth interaction even with a large number of components, effectively solving the performance dilemma of traditional BIM systems in large scenarios.

[0019] This invention demonstrates outstanding performance in progress analysis and decision support. Most existing systems only compare progress at the report level, while this invention achieves accurate visualization and comparison of planned and actual progress in three-dimensional space through a four-view comparison and analysis mechanism, improving analysis efficiency and discovering potential risks that are difficult to identify by traditional methods.

[0020] Creating significant value in construction management and collaboration efficiency, the system's dynamic simulation function not only supports visualized backtracking of the construction process, but more importantly, it enables forward-looking verification of solutions. Simultaneously, the unified visualization platform breaks down professional barriers, qualitatively improving communication and collaboration efficiency among all participants.

[0021] It has unique advantages in extending the value of the entire life cycle. Unlike traditional systems that only focus on the construction phase, the digital twin constructed by this invention provides complete digital assets for the project operation and maintenance phase, realizing a seamless connection from the construction phase to the operation phase and significantly improving the value of the entire life cycle of the project. Attached Figure Description

[0022] Figure 1 This is a system schematic diagram of the present invention; Figure 2This is the architectural design diagram of the present invention; Figure 3 This is a functional diagram of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0024] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0025] like Figure 1 and combined Figures 2 to 3 As shown, a virtual construction system based on BIM schedule simulation includes a three-tier architecture and is equipped with core functional components. The three-tier architecture includes: The data layer connects to the DDE platform, Puhua system and PDM database through standardized interfaces, automatically collects heterogeneous data from multiple sources, and is equipped with a Redis caching module; The technical support layer is built on C# and .NET Core technologies, with a built-in data mapping engine and business logic processor to complete the structure mapping and progress status calculation of LF / CBS / PBS. The business presentation layer is built on HTML, JS, CSS and VUE technologies, and provides a multi-view visualization interface based on the WebGL graphics engine; The core functional components include an LF space box model generator, a multi-dimensional data mapping engine, a four-view comparison analyzer, and a dynamic simulation engine.

[0026] The LF spatial box model generator can perform spatial topology analysis and semantic extraction on BIM models, generating a lightweight spatial structure tree with hierarchical relationships, and reducing the model data volume by 70%-80% while preserving the progress semantics.

[0027] The multi-dimensional data mapping engine automatically establishes the association between model components and schedule tasks and cost items based on a predefined coding rule library.

[0028] The four-view comparison analyzer supports synchronous rendering and linkage operations of delayed status, planned completion, actual completion, and reference model.

[0029] The dynamic simulation engine drives model state changes based on the progress timeline, enabling interactive simulation of the construction process.

[0030] The algorithm steps of the LF spatial box model generator are as follows: S1. Preprocessing of BIM model component geometry and attributes: Normalize the component data of the original BIM model and extract the basic geometric parameters and initial attributes of the components. S2. Calculate the spatial topology of components, construct the spatial adjacency and hierarchical relationships between components, and provide a topological basis for subsequent hierarchical division; S3. Progress semantic extraction and mapping: bind progress tasks with components and assign progress semantic attributes to space boxes; S4. Hierarchical LF space box partitioning and generation: Based on the topological clustering results, multi-level LF space boxes are generated from bottom to top to achieve hierarchical and lightweight model. S5, Model Lightweight Compression and Structure Tree Construction.

[0031] Step S11 involves extracting component geometric parameters for any BIM component. Extract its vertex set , For three-dimensional coordinates, the minimum bounding box of a component is calculated using the following formula: ;in This represents any component in the Building Information Model (BIM). Representation of components The The three-dimensional spatial coordinates of each vertex; These are the coordinates of the vertex on the x (horizontal), y (horizontal), and z (vertical) axes, respectively. Then the component The bounding box space range is ; in For components The spatial range in the x-axis (horizontal) direction; For components The spatial range in the y-axis (horizontal longitudinal direction); For components The spatial range in the Z-axis (vertical height) direction; Extract components Basic attributes and initial identifiers associated with progress The discrete attributes are encoded using the following formula: ; in The index value corresponding to the attribute category. These are the minimum and maximum values ​​of the professional category index, respectively.

[0032] Step S12 involves constructing the spatial adjacency matrix and defining the adjacency determination threshold. For any two components Calculate the center distance of its bounding box. , indicating components With components The three-dimensional spatial distance between the centers of the bounding boxes;

[0033] in They are components The center coordinates of the bounding box along the x, y, and z axes; , Similarly, for components The center coordinates of the bounding box in the y and z directions; It is a component The center coordinates of the bounding box along the x, y, and z axes; like Then determine and Spatial adjacency, adjacency matrix elements ,otherwise ,Right now: .

[0034] It also includes a virtual construction method, the steps of which are as follows: S11. Data Acquisition and Association: The system acquires multi-source data through the data layer, and the multi-dimensional data mapping engine automatically establishes and maintains data associations. S12, Multi-dimensional filtering interaction: Users can filter data through LF tree and CBS tree, and the system updates the model display in real time; S13. Progress comparison analysis: The system renders four model states in parallel to realize three-dimensional visualization and traceability of progress deviation. S14. Dynamic construction simulation: The system drives the evolution of the model state according to the time sequence, and completes the backtracking of the construction process or the pre-scheme simulation.

[0035] The multi-dimensional data mapping engine algorithm steps are as follows: S21. Multi-source data coding standardization: The basic information of BIM components, schedule tasks, cost items, and LF space boxes is uniformly coded to provide standardized identification for subsequent mapping. S22. Mapping rule base construction and matching: Based on engineering business logic, a multi-dimensional mapping rule base is constructed, and preliminary data association is achieved through rule matching. S23, the association between the LF spatial hierarchy and multi-dimensional data, binds the already associated "component-schedule-cost" data with the LF spatial box in spatial semantics, and realizes four-dimensional data mapping; S24. Dynamic verification and updating of mapping relationships: Establish a real-time verification mechanism for mapping relationships to ensure the timeliness and accuracy of data association. S25. Generation and output of mapping association library: The final multi-dimensional mapping relationship is stored as a structured association library for the virtual construction system to call; Representing BIM components Progress task Cost items The comprehensive correlation weight among the three; the formula is: ; Where Q is the total number of rules in the rule base. Let q be the confidence level of rule q; This indicates the BIM component under the q-th mapping rule. Progress task Cost items The single-rule association weights of the three.

[0036] Compared with existing technologies, the virtual construction system created by this invention has the following significant advantages and positive effects: This invention represents a qualitative leap in data integration and processing efficiency. Traditional methods require manual data docking and association between different systems, which is time-consuming and prone to errors. This invention achieves intelligent fusion of multi-source heterogeneous data by establishing a standardized PBS-CBS-LF mapping rule base and an automated data association engine.

[0037] Significant breakthroughs have been achieved in visualization performance and user experience. Addressing the pain points of slow loading and sluggish interaction in traditional BIM systems in large-scale scenarios, the innovative LF spatial box model of this invention reduces the model data volume by 70%-80% while preserving progress semantics. This effectively controls the loading time on the web end and supports smooth interaction even with a large number of components, effectively solving the performance dilemma of traditional BIM systems in large scenarios.

[0038] This invention demonstrates outstanding performance in progress analysis and decision support. Most existing systems only compare progress at the report level, while this invention achieves accurate visualization and comparison of planned and actual progress in three-dimensional space through a four-view comparison and analysis mechanism, improving analysis efficiency and discovering potential risks that are difficult to identify by traditional methods.

[0039] Creating significant value in construction management and collaboration efficiency, the system's dynamic simulation function not only supports visualized backtracking of the construction process, but more importantly, it enables forward-looking verification of solutions. Simultaneously, the unified visualization platform breaks down professional barriers, qualitatively improving communication and collaboration efficiency among all participants.

[0040] It has unique advantages in extending the value of the entire life cycle. Unlike traditional systems that only focus on the construction phase, the digital twin constructed by this invention provides complete digital assets for the project operation and maintenance phase, realizing a seamless connection from the construction phase to the operation phase and significantly improving the value of the entire life cycle of the project.

[0041] Example 1: Virtual Construction Progress Control Example for Urban Complex Residential Projects Technical Application Scenarios: This embodiment is applied to a comprehensive residential complex project in the core urban area with a building area of ​​320,000 square meters. The project includes 5 super high-rise residential buildings, 2 commercial podiums and 2 underground parking garages, involving 8 major disciplines such as civil engineering, mechanical and electrical engineering, and curtain wall. The total number of BIM model components is 1.2 million. It is necessary to solve the technical problems of slow loading, data silos from multiple sources and inefficient correlation between schedule and cost in traditional BIM systems.

[0042] System deployment and technical implementation steps: Data Layer Integration and Acquisition: The system connects to the project's DDE data acquisition system, PwC PowerPIP schedule management system, and PDM model database through standardized RESTful interfaces. The Redis caching module is configured with a caching strategy of "prioritizing high-frequency data caching + 24-hour automatic updates". It automatically collects BIM component geometric attribute data, three-level schedule task data (including milestone nodes and critical path tasks), and bill of quantities cost data. The data acquisition cycle is shortened from 24 hours in the traditional manual mode to 2 hours, and the data transmission accuracy reaches 100%.

[0043] LF spatial box model generation: Perform component geometry and attribute preprocessing steps, extract the three-dimensional coordinates of the vertex set of any component, and calculate the minimum bounding box according to the formula (for example, the frame beam component of the 15th floor of Building 3, the x-axis range is 12.3m-18.7m, the y-axis range is 25.1m-30.4m, and the z-axis range is 45.0m-47.2m). Encode discrete attributes such as professional category and progress association identifier through the attribute coding formula (the professional category index value ranges from 1 to 8, corresponding to 8 major professions). Set the adjacency determination threshold to 0.5m, calculate the center distance between the bounding boxes of any two components according to the spatial adjacency matrix formula, and construct the spatial topology relationship of "component-component group"; By binding progress semantics (such as associating the task of "casting beams and slabs on the 15th floor of Building #3" with the corresponding beam and slab components), a four-level LF space box of "building-floor-functional area-component group" is generated from bottom to top. The original 18GB BIM model data is compressed to 3.2GB, reducing the data volume by 82.2%. The loading time on the web client (browser Chrome version 108.0) is ≤90 seconds, and it supports 10 people to interact online at the same time.

[0044] Multi-dimensional data mapping: Following the multi-dimensional data mapping engine algorithm steps, complete the unified coding of BIM components, schedule tasks, and cost items (the coding rule is "project number-professional code-component type-schedule level-cost item"). Based on the PBS-CBS-LF mapping rule base (containing 286 engineering business logic rules), preliminary association is achieved through rule matching. For example, “15th floor frame beam of Building 3 (BIM component code ZHZ-01-LB-03-025)” is automatically bound to “15th floor main structure construction (progress task code JD-3-15-008)” and “frame beam concrete pouring (cost item code CB-01-03-126)”. Dynamic verification of the mapping relationship was performed. The association weight of the group was calculated to be 0.92 using the comprehensive association weight formula (total number of rules in the rule base Q=286, average rule confidence 0.85, average single rule association weight 0.98). The mapping accuracy was ≥99.7%, and the number of monthly data mismatch issues decreased from 12 to 0.

[0045] Progress comparison analysis and dynamic simulation: The four-view comparison analyzer was launched to simultaneously render the four views: "Delayed Status, Planned Completion, Actual Completion, and Reference Model". The progress status was distinguished by color coding (green = on time, yellow = delayed ≤ 3 days, red = delayed > 3 days). In the 8th month of the project, the root cause of the delay deviation of "installation of electromechanical pipelines on the 15th floor of Building 3" was accurately located as the delay in the arrival of duct components. By calling the dynamic simulation engine and driving the model state changes based on the progress time axis, the construction plan of "ductwork arriving 3 days in advance + pipeline route optimization" was simulated. The simulation step size was set to 1 hour to verify the feasibility of the plan. In the end, the delayed construction period was brought back from 7 days to 0 days, avoiding delays in subsequent procedures.

[0046] Technical results: The project's overall schedule deviation rate decreased from 8.3% to 2.1%, professional collaboration and communication efficiency improved by 75%, data association labor costs decreased by 88%, and web-based model loading efficiency improved by 91.1%, meeting the needs of multi-terminal and multi-professional collaborative schedule management for large-scale complex projects.

[0047] Example 2: Virtual Simulation and Progress Tracking of Long-Span Cross-Sea Bridge Construction Project Technical Application Scenarios: This embodiment is applied to a double-tower, double-cable-stayed bridge project with a main span of 580m. The main bridge includes high-risk and complex processes such as deep-water foundation, tower column casting, and cable tensioning. The total number of BIM model components is 860,000. It is necessary to solve technical problems such as the lack of visual traceability of the construction process, low matching degree between progress and working conditions, and delayed risk warning of high-risk processes under the traditional management model.

[0048] System deployment and technical implementation steps: Specialized data adaptation and collection: The data layer is specifically integrated with the bridge engineering DDE monitoring system (collecting real-time monitoring data such as pile foundation stress and tower column verticality), Puhua Bridge Special Progress System and PDM steel structure component database. The Redis caching module focuses on caching the monitoring data and progress-related data of core components such as cable towers and stay cables, with a cache hit rate of 92% and a core data retrieval response time of ≤50ms.

[0049] LF spatial box model generation: In response to the characteristics of the linear spatial structure of bridges, the topological clustering logic is adjusted. When performing component geometry and attribute preprocessing, the minimum bounding boxes of key components such as tower segments and main beam segments are extracted (for example, the x-axis range of a standard tower segment is 12.0m-24.0m, the y-axis range is 8.0m-16.0m, and the z-axis range is 6.0m-12.0m). The adjacency determination threshold is set to 1.2m. The center distance between component segments is calculated according to the spatial adjacency matrix formula, and the spatial topology relationship of "tower column segment - main beam segment - cable group" is constructed. By binding specific progress semantics (such as "casting of the 12th segment of the tower" and "tensioning of the 15th pair of stay cables"), a four-level LF space box is generated from bottom to top, namely "bridge site - main bridge / approach bridge - tower column / main beam / foundation - component segment". The original 22GB BIM model data is compressed to 4.1GB, reducing the data volume by 81.4%. It supports smooth interaction on mobile terminals (Android 12 system, iOS 16 system) at the construction site, with a loading time of ≤100 seconds.

[0050] Process-level data mapping and verification: The multi-dimensional data mapping engine completes standardized coding according to the coding rules of "bridge component segment-process task-specific cost", and builds a rule base containing 198 bridge-specific mapping rules; The system automatically associates "20th segment of main bridge steel box girder (BIM component code QL-02-ZL-20-058)" with "main girder cantilever assembly process (progress task code JD-QL-20-033)" and "steel box girder hoisting cost item (cost code CB-QL-02-089)", and synchronizes the processing, transportation, and installation status data of steel box girder in real time through a dynamic verification mechanism. When transportation is delayed, a progress warning is automatically triggered. The calculated overall association weight for this group is 0.93 (Q=198, mean rule confidence 0.87, mean single rule association weight 0.99), and the mapping timeliness reaches the minute level.

[0051] Deviation tracing and simulation optimization: During the casting stage of the tower column, the verticality deviation (deviation value 3.2cm) caused by the offset of the template on the east side of the 8th segment was accurately located by superimposing the "actual casting model, planned model, reference model and delayed state model" using a four-view comparison analyzer. The dynamic simulation engine was started, and the model state changes of the template installation and concrete vibration processes were traced back in time. The simulation step was set to 30 minutes, and the root cause of the deviation was found to be the deformation of the support. Based on the simulation results, the support reinforcement scheme was optimized (by increasing the density of horizontal diagonal bracing), and the feasibility of the scheme was verified by simulation again (the deviation value was reduced to 0.8cm, meeting the specification requirements). On-site rectification was guided to avoid rework losses after the cable tower was completed.

[0052] Technical results: The time for identifying progress deviations in key processes was reduced from 48 hours to 2 hours; the efficiency of verifying construction plans for high-risk processes was improved by 60%; the overall project schedule was completed 18 days ahead of schedule; and the project achieved visualized progress control and risk pre-control for complex processes of long-span bridges. The practicality and reliability of the technical solution were verified.

[0053] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A virtual construction system based on BIM schedule simulation, characterized in that: It includes a three-tier architecture design and is equipped with core functional components, the three-tier architecture including: The data layer connects to the DDE platform, Puhua system and PDM database through standardized interfaces, automatically collects heterogeneous data from multiple sources, and is equipped with a Redis caching module; The technical support layer is built on C# and .NET Core technologies, with a built-in data mapping engine and business logic processor to complete the structure mapping and progress status calculation of LF / CBS / PBS. The business presentation layer is built on HTML, JS, CSS and VUE technologies, and provides a multi-view visualization interface based on the WebGL graphics engine; The core functional components include an LF space box model generator, a multi-dimensional data mapping engine, a four-view comparison analyzer, and a dynamic simulation engine.

2. The virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: The LF spatial box model generator can perform spatial topology analysis and semantic extraction on BIM models to generate a lightweight spatial structure tree with hierarchical relationships.

3. The virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: The multi-dimensional data mapping engine, based on a predefined coding rule library, automatically establishes the association between model components and schedule tasks and cost items.

4. A virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: The four-view comparison analyzer supports synchronous rendering and linkage operations of delayed status, planned completion, actual completion, and reference model.

5. The virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: The dynamic simulation engine drives model state changes based on the progress time axis, enabling interactive simulation of the construction process.

6. A virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: The algorithm steps of the LF spatial box model generator are as follows: S1. Preprocessing of BIM model component geometry and attributes: Normalize the component data of the original BIM model and extract the basic geometric parameters and initial attributes of the components. S2. Calculate the spatial topology of components, construct the spatial adjacency and hierarchical relationships between components, and provide a topological basis for subsequent hierarchical division; S3. Progress semantic extraction and mapping: bind progress tasks with components and assign progress semantic attributes to space boxes; S4. Hierarchical LF space box partitioning and generation: Based on the topological clustering results, multi-level LF space boxes are generated from bottom to top to achieve hierarchical and lightweight model. S5, Model Lightweight Compression and Structure Tree Construction.

7. A virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: Step S11 involves extracting component geometric parameters for any BIM component. Extract its vertex set , For three-dimensional coordinates, the minimum bounding box of a component is calculated using the following formula: ;in This represents any component in the Building Information Model (BIM). Representation of components The The three-dimensional spatial coordinates of each vertex; These are the coordinates of the vertex on the x, y, and z axes, respectively. Then the component The bounding box space range is ; in For components Spatial range in the x-axis direction; For components The spatial range in the y-axis direction; For components Spatial range in the Z-axis direction; Extract components Basic attributes and initial identifiers associated with progress The discrete attributes are encoded using the following formula: ； in The index value corresponding to the attribute category. These are the minimum and maximum values ​​of the professional category index, respectively.

8. A virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: Step S12 involves constructing the spatial adjacency matrix and defining the adjacency determination threshold. For any two components Calculate the center distance of its bounding box. , indicating components With components The three-dimensional spatial distance between the centers of the bounding boxes; ， in They are components The center coordinates of the bounding box along the x, y, and z axes; , Similarly, for components The center coordinates of the bounding box in the y and z directions; It is a component The center coordinates of the bounding box along the x, y, and z axes; like Then determine and Spatial adjacency, adjacency matrix elements ,otherwise ,Right now: .

9. A virtual construction system based on BIM schedule simulation as described in claim 1, characterized in that: It also includes a virtual construction method, the steps of which are as follows: S11. Data Acquisition and Association: The system acquires multi-source data through the data layer, and the multi-dimensional data mapping engine automatically establishes and maintains data associations. S12, Multi-dimensional filtering interaction: Users can filter data through LF tree and CBS tree, and the system updates the model display in real time; S13. Progress comparison analysis: The system renders four model states in parallel to realize three-dimensional visualization and traceability of progress deviation. S14. Dynamic construction simulation: The system drives the evolution of the model state according to the time sequence, and completes the backtracking of the construction process or the pre-scheme simulation.

10. A virtual construction system based on BIM schedule simulation as described in claim 9, characterized in that: The multi-dimensional data mapping engine algorithm steps are as follows: S21. Multi-source data coding standardization: The basic information of BIM components, schedule tasks, cost items, and LF space boxes is uniformly coded to provide standardized identification for subsequent mapping. S22. Mapping rule base construction and matching: Based on engineering business logic, a multi-dimensional mapping rule base is constructed, and preliminary data association is achieved through rule matching. S23, the association between the LF spatial hierarchy and multi-dimensional data, binds the associated "component-schedule-cost" data with the LF spatial box in spatial semantics to achieve four-dimensional data mapping; S24. Dynamic verification and updating of mapping relationships: Establish a real-time verification mechanism for mapping relationships to ensure the timeliness and accuracy of data association. S25. Generation and output of mapping association library: The final multi-dimensional mapping relationship is stored as a structured association library for the virtual construction system to call; Representing BIM components Progress task Cost items The comprehensive correlation weight among the three; the formula is: ; Where Q is the total number of rules in the rule base. Let q be the confidence level of rule q; This indicates the BIM component under the q-th mapping rule. Progress task Cost items The single-rule association weights of the three.

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