BIM-based process simulation and consulting service support system and method

Abstract

This invention discloses a BIM-based process simulation and consulting service support system and method, relating to the field of 3D modeling and visualization technology. The system includes a BIM holographic data acquisition module, a process logic intelligent deconstruction module, a dynamic process simulation engine module, a physical field real-time monitoring module, an expert knowledge decision-making module, and a visualization scheme generation module. By establishing a real-time matching mechanism between physical field data and simulation data, and setting multi-dimensional deviation warning thresholds for different construction stages, the system ensures the accuracy of various process conflict detections. Simultaneously, by linking the ambient lighting parameters collected by sensors with the BIM rendering engine in real time, it can dynamically correct the light and shadow distortion problem in the digital twin scene, avoid misjudgment of material textures caused by light and color temperature deviations, improve the accuracy of process conflict warnings, and reduce construction safety risks and resource scheduling errors.

Description

Technical Field

[0001] This invention relates to the field of 3D modeling and visualization technology, and in particular to a BIM-based process simulation and consulting service support system and method. Background Technology

[0002] BIM is a new tool for architecture, engineering, and civil engineering. It describes computer-aided design that is based on three-dimensional graphics, object-oriented, and related to architecture. The core of BIM is to create a virtual three-dimensional model of the building project and use digital technology to provide this model with a complete and consistent building project information database.

[0003] In existing technologies, BIM-based process simulation and consulting service support systems and methods have shortcomings: due to the dynamic integration of multi-source heterogeneous data involved in the process of process simulation, when the construction physical field data interacts with the BIM model in real time, it is impossible to intelligently detect whether the lighting parameters of the simulation environment have shifted. In the visualization and simulation of key process nodes, if the ambient lighting is uneven and color temperature deviation occurs, it will cause the material texture and shadow rendering in the digital twin scene to be distorted, which will lead to misjudgment of process conflict warning parameters and resource scheduling optimization parameters.

[0004] Therefore, a BIM-based process simulation and consulting service support system and method are proposed to solve the above problems. Summary of the Invention

[0005] The main objective of this invention is to provide a BIM-based process simulation and consulting service support system and method to solve the problems mentioned in the background above.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a BIM-based process simulation and consulting service support system and method, the method comprising the following steps:

[0007] S1. Obtain the full-element BIM model data of the target project through BIM data acquisition equipment. The full-element BIM model data includes building structure parameters, electromechanical pipeline layout parameters, construction resource distribution parameters, and schedule parameters.

[0008] S2. Based on the full-element BIM model data, perform intelligent decomposition processing of construction procedures to generate multi-level process logical relationship chain data, which includes key process nodes, process dependencies, and process timing constraint parameters.

[0009] S3. Combine the construction environment simulation engine to perform dynamic process simulation and deduction on the multi-level process logical relationship chain data to generate dynamic simulation data of construction process. The dynamic simulation data of construction process includes process conflict early warning parameters, resource scheduling optimization parameters and schedule deviation prediction parameters.

[0010] S4. Real-time monitoring of the matching degree between construction physical field data and dynamic simulation data of construction procedures, generating process execution status deviation data;

[0011] S5. Based on the process execution status deviation data and the preset expert knowledge base, perform intelligent matching analysis to generate process optimization consulting strategy data;

[0012] S6. Dynamically correct the full-element BIM model data based on the process optimization consulting strategy data, and generate a visualized process optimization scheme report data.

[0013] S1 includes the following specific steps:

[0014] S11. Collect real-world point cloud data of the construction site with a resolution of 0.1mm using a high-precision ground laser scanning device, and at the same time, obtain parametric model data of building components by connecting to the API interface of BIM modeling software.

[0015] S12. The ICP point cloud registration algorithm is used to align the real-time collected point cloud data with the BIM parametric model in spatial coordinates to generate an initial BIM structural model with sub-millimeter accuracy.

[0016] S13. Synchronize the Gantt chart time node data of the construction progress management platform with the resource inventory list data of the material management platform through the ODBC database connector;

[0017] S14. Establish a three-dimensional mapping relationship between time, space, and resources in the BIM data fusion engine, and dynamically associate the schedule parameters with building components through time axis binding technology to generate full-element BIM model data with time sequence attributes.

[0018] S2 includes the following deep processing flow:

[0019] S21. Based on the IFC standard format of the BIM model, the spatial topological relationship of building components is analyzed, and the graph neural network algorithm is used to identify the three-dimensional spatial constraint relationship of beam-column nodes and pipeline intersections.

[0020] S22. When constructing the process logic network diagram, the installation path of critical equipment is inserted as a virtual node into the CPM critical path, and the process dependency weight coefficient is iteratively optimized through a genetic algorithm:

[0021] ;

[0022] in For time cost function, For resource conflict functions, For risk coefficient function, , , As a dynamic weighting factor, + + =1;

[0023] S23. The Monte Carlo simulation method is used to conduct sensitivity analysis on the process sequence under extreme weather conditions, and multi-level process logical relationship chain data including risk buffer period is generated.

[0024] S24. Push the process logic relationship chain data to the field mobile terminal device in real time via the WebSocket protocol.

[0025] S3 includes the following key operations:

[0026] S31. Create a 1:1 digital twin scene in the Unity3D engine and load multi-level process logic relationship chain data to drive the simulation of tower crane running trajectory;

[0027] S32. Calculate the optimal worker movement path using the A* pathfinding algorithm, and detect equipment collision risks and violations of safety protection distances in real time.

[0028] ;

[0029] in The actual cost from the starting point to point n. Let n be the estimated cost from point n to the destination. For dynamic obstacle distance factor, For safety factor;

[0030] S33. Establish a progress delay prediction model. When the deviation of concrete setting time is detected to exceed the threshold, a three-level early warning mechanism will be automatically triggered.

[0031] S34. Calculate the impact of material loading on temporary support structures based on the physics engine, and generate dynamic simulation data of construction procedures including stress distribution cloud maps.

[0032] S4 includes the following monitoring mechanisms:

[0033] S41. Deploy distributed IoT sensor clusters at key nodes on the construction site, including tower crane tilt sensors, concrete temperature and humidity sensors, and UWB worker positioning base stations.

[0034] S42. Transmit physical field data to the central processing platform in real time through the 5G edge computing gateway, and use timestamp alignment technology to ensure the spatiotemporal synchronization of data acquisition and simulation.

[0035] S43. Construct a sliding time window for the Dynamic Time Warping (DTW) algorithm and calculate the shape similarity between the actual progress curve and the simulated progress curve.

[0036] S44. When the deviation value of three consecutive sampling cycles exceeds 5%, process execution status deviation data with positioning coordinates is automatically generated.

[0037] The S5 performs the following intelligent decision-making process:

[0038] S51. Construct a multi-dimensional expert knowledge base, including a historical engineering case library, an industry standard clause library, and a technical standard text library, with each library linked through a knowledge graph;

[0039] S52. Use the BERT pre-trained model to analyze the semantic features of process deviation data and extract key entities including: conflict type, scope of impact, and urgency.

[0040] S53. Perform multi-dimensional similarity retrieval in the expert knowledge base, including spatial feature similarity, temporal feature similarity, and resource type similarity;

[0041] S54. By integrating the solutions of the top-3 similar cases through a weighted voting mechanism, process optimization consulting strategy data containing implementation priority markers is generated.

[0042] S6 includes the following implementation details:

[0043] S61. Based on process optimization consulting strategy data, automatically adjust the coordinates of equipment path control points and process time parameters through the BIM parametric drive engine;

[0044] S62. Establish a modification impact assessment model to quantitatively analyze the chain effect of scheme adjustment on subsequent processes;

[0045] S63. Generates a 3D interactive process optimization report using the WebGL visualization engine, supporting the following interactive functions:

[0046] 360° scene rotation and viewing, process timeline zoom control, and cross-sectional perspective of conflict areas;

[0047] S64. Use color enhancement technology to mark three types of key modification areas, and use differentiated dynamic identifiers for different areas.

[0048] The identification rules for key modification areas in S64 include:

[0049] High-risk conflict zone: Uses red pulse conflict display signs, superimposed with safety warning icons, and automatically plays an accident simulation animation when the user focuses on it;

[0050] Resource Optimization Area: Identified with a blue breathing light effect, clicking it displays a resource scheduling comparison chart and cost-saving data;

[0051] Progress Compression Zone: Identified by a yellow flowing light trail, the time compression scheme and key technical measures are displayed when hovering over it;

[0052] New green channel areas: Newly added areas such as construction access roads resulting from optimization will be marked with semi-transparent green high-brightness signs and associated with equipment traffic capacity parameters.

[0053] The system includes: a BIM holographic data acquisition module, a process logic intelligent deconstruction module, a dynamic process simulation engine module, a physical field real-time monitoring module, an expert knowledge decision-making module, and a visualization solution generation module;

[0054] The BIM holographic data acquisition module uses a laser scanner and BIM software to collaboratively construct a full-element BIM model with temporal attributes.

[0055] The process logic intelligent deconstruction module generates a multi-level process logic relationship chain based on topology analysis and genetic algorithm.

[0056] The dynamic process simulation engine module simulates construction conflicts and generates early warning parameters in the Unity3D environment;

[0057] The real-time physical field monitoring module collects data through an IoT sensor cluster and calculates the deviation with the simulation results.

[0058] The expert knowledge decision-making module combines the BERT model with the case library to output process optimization consultation strategies.

[0059] The visualization solution generation module drives the WebGL engine to generate a 3D interactive optimization solution report.

[0060] The expert knowledge decision-making module includes a conflict resolution strategy library, a resource allocation template library, and a legal compliance standard library. Each sub-library is stored in a graph database and supports multi-dimensional case similarity retrieval of the proposed questions.

[0061] The present invention has the following beneficial effects:

[0062] 1. In this invention, when performing dynamic simulation and deduction of construction procedures, a real-time matching mechanism between physical field data and simulation data is established, and multi-dimensional deviation warning thresholds are set for different construction stages to ensure the accuracy of various procedure conflict detection. At the same time, the ambient lighting parameters collected by the sensors are linked with the BIM rendering engine in real time, which can dynamically correct the light and shadow distortion problem in the digital twin scene, avoid misjudgment of material texture caused by light color temperature deviation, improve the accuracy of procedure conflict warning, and reduce construction safety risks and resource scheduling errors.

[0063] 2. In this invention, when making process optimization strategy decisions, a semantic feature-driven expert knowledge matching algorithm is constructed to analyze the correlation features between process execution deviations and historical cases in real time. This enables the system to adaptively select the optimal solution type. Furthermore, when a sudden change in construction logic features is detected, the parameterized BIM model correction engine is automatically triggered. By dynamically adjusting the coordinates of equipment path control points and the sequence of time parameters, the process optimization strategy is ensured to be accurately matched with the current construction scenario, eliminating the accumulation of deviations caused by algorithm mismatch in traditional decision-making.

[0064] 3. In this invention, when generating a 3D visualization scheme, key modification areas are identified through spatial topology analysis technology, and differentiated dynamic identifiers are loaded to achieve multi-dimensional optimization effect presentation. This enables the system to display high-risk conflict resolution schemes, resource scheduling paths, and schedule compression strategies in a hierarchical manner. Furthermore, based on interactive timeline control, the entire process logic chain can be traced, enhancing the construction party's understanding of complex optimization schemes and execution efficiency, and avoiding process execution deviations caused by information transmission distortion. Attached Figure Description

[0065] Figure 1 This is a flowchart of the BIM-based process simulation and consulting service support method of the present invention;

[0066] Figure 2 This is a diagram illustrating the architecture of the BIM-based process simulation and consulting service support system of this invention. Detailed Implementation

[0067] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Specific implementation examples:

[0069] Please refer to Figures 1 to 2 As shown: A BIM-based process simulation and consulting service support system and method, the method includes the following steps:

[0070] S1. Obtain the full-element BIM model data of the target project through BIM data acquisition equipment. The full-element BIM model data includes building structure parameters, electromechanical pipeline layout parameters, construction resource distribution parameters, and schedule parameters.

[0071] S2. Based on the full-element BIM model data, intelligent decomposition and processing of construction procedures are performed to generate multi-level logical relationship chain data of procedures. The multi-level logical relationship chain data of procedures includes key procedure nodes, procedure dependencies and procedure timing constraint parameters.

[0072] S3. Combine the construction environment simulation engine to perform dynamic process simulation and deduction on the multi-level process logical relationship chain data, and generate dynamic simulation data of construction process. The dynamic simulation data of construction process includes process conflict early warning parameters, resource scheduling optimization parameters and schedule deviation prediction parameters.

[0073] S4. Real-time monitoring of the matching degree between construction physical field data and construction process dynamic simulation data, generating process execution status deviation data;

[0074] S5. Based on the process execution status deviation data and the pre-set expert knowledge base, intelligent matching analysis is performed to generate process optimization consulting strategy data;

[0075] S6. Dynamically correct the full-element BIM model data based on the process optimization consulting strategy data, and generate a visualized process optimization scheme report data.

[0076] This method constructs a closed-loop control process of "data acquisition → process deconstruction → dynamic simulation → deviation monitoring → decision correction". Through real-time matching of physical field data and simulation models, a dynamic optimization mechanism for process conflict early warning and resource scheduling is formed, which solves the problem of dynamic integration of multi-source heterogeneous data: schedule, resources and structure, and avoids misjudgment of conflicts caused by data fragmentation.

[0077] By comparing physical fields and simulation data in real time, millisecond-level early warnings of process deviations can be achieved, reducing construction safety risks.

[0078] S1 includes the following specific steps:

[0079] S11. Collect real-world point cloud data of the construction site with a resolution of 0.1mm using a high-precision ground laser scanning device, and at the same time, obtain parametric model data of building components by connecting to the API interface of BIM modeling software.

[0080] S12. The ICP point cloud registration algorithm is used to align the real-time collected point cloud data with the BIM parametric model in spatial coordinates, generating an initial BIM structural model with sub-millimeter accuracy:

[0081]

[0082] in Let be a rotation matrix. It is a translation vector. To scan point cloud coordinates, The coordinates of the corresponding points in the BIM model. The feature point weighting factor;

[0083] S13. Synchronize the Gantt chart time node data of the construction progress management platform with the resource inventory list data of the material management platform through the ODBC database connector;

[0084] S14. Establish a three-dimensional mapping relationship between time, space, and resources in the BIM data fusion engine, and dynamically associate the schedule parameters with building components through time axis binding technology to generate full-element BIM model data with time sequence attributes.

[0085] This step uses the ICP point cloud registration algorithm to achieve spatial alignment between the point cloud and the BIM model, and uses time axis binding technology to associate progress parameters.

[0086] Sub-millimeter precision models provide a reliable foundation for process simulation, eliminating collision detection failures caused by accumulated errors in traditional modeling.

[0087] The three-dimensional mapping relationship between time, space, and resources ensures that the process simulation and on-site progress are updated synchronously.

[0088] S2 includes the following depth processing flow:

[0089] S21. Based on the IFC standard format of the BIM model, the spatial topological relationship of building components is analyzed, and the graph neural network algorithm is used to identify the three-dimensional spatial constraint relationship of beam-column nodes and pipeline intersections.

[0090] S22. When constructing the process logic network diagram, the installation path of critical equipment is inserted as a virtual node into the CPM critical path, and the process dependency weight coefficient is iteratively optimized through a genetic algorithm:

[0091] ;

[0092] in For time cost function, For resource conflict functions, For risk coefficient function, , , As a dynamic weighting factor, + + =1;

[0093] S23. The Monte Carlo simulation method is used to conduct sensitivity analysis on the process sequence under extreme weather conditions, and multi-level process logical relationship chain data including risk buffer period is generated.

[0094] S24. Push the process logic relationship chain data to the field mobile terminal device in real time via the WebSocket protocol.

[0095] This process uses graph neural networks to analyze spatial topological relationships, optimizes weight coefficients using genetic algorithms, and generates logical chains with risk buffer periods using Monte Carlo simulation.

[0096] Inserting virtual nodes into the critical path avoids equipment installation path conflicts and reduces the error rate of project schedule prediction.

[0097] The WebSocket protocol pushes logic chains in real time, improving on-site response efficiency.

[0098] S3 includes the following key operations:

[0099] S31. Create a 1:1 digital twin scene in the Unity3D engine and load multi-level process logic relationship chain data to drive the simulation of tower crane running trajectory;

[0100] S32. Calculate the optimal worker movement path using the A* pathfinding algorithm, and detect equipment collision risks and violations of safety protection distances in real time.

[0101] ;

[0102] in The actual cost from the starting point to point n. Let n be the estimated cost from point n to the destination. For dynamic obstacle distance factor, For safety factor;

[0103] S33. Establish a progress delay prediction model. When the deviation of concrete setting time is detected to exceed the threshold, a three-level early warning mechanism will be automatically triggered.

[0104] S34. Calculate the impact of material loading on temporary support structures based on the physics engine, and generate dynamic simulation data of construction procedures including stress distribution cloud maps.

[0105] This operation creates a digital twin scene in Unity3D, dynamically calculates safe paths using the A* pathfinding algorithm, and simulates material stress using the physics engine.

[0106] The safety factor λ dynamically adjusts the protective spacing to reduce the risk of equipment collision accidents.

[0107] The three-tiered early warning mechanism enables tiered intervention to avoid cascading delays.

[0108] S4 includes the following monitoring mechanisms:

[0109] S41. Deploy distributed IoT sensor clusters at key nodes on the construction site, including tower crane tilt sensors, concrete temperature and humidity sensors, and UWB worker positioning base stations.

[0110] S42. Transmit physical field data to the central processing platform in real time through the 5G edge computing gateway, and use timestamp alignment technology to ensure the spatiotemporal synchronization of data acquisition and simulation.

[0111] S43. Construct a sliding time window for the dynamic time warping algorithm and calculate the shape similarity between the actual progress curve and the simulated progress curve:

[0112]

[0113] in Let t be the deviation value. The time window length, Indicates the reference point at the current sampling time. For simulation data vectors, This is a vector of measured data;

[0114] S44. When the deviation value of three consecutive sampling cycles exceeds 5%, process execution status deviation data with positioning coordinates is automatically generated.

[0115] This step involves deploying an IoT sensor cluster: tilt angle, temperature and humidity, and UWB positioning, and using the DTW algorithm with a sliding time window to calculate the similarity of the progress curves.

[0116] When the deviation exceeds 5% for three consecutive cycles, the system automatically locates the problem coordinates, shortening the troubleshooting time.

[0117] 5G edge computing ensures spatiotemporal synchronization and eliminates decision-making delays caused by data latency.

[0118] The S5 performs the following intelligent decision-making process:

[0119] S51. Construct a multi-dimensional expert knowledge base, including a historical engineering case library, an industry standard clause library, and a technical standard text library, with each library linked through a knowledge graph;

[0120] S52. Use the BERT pre-trained model to analyze the semantic features of process deviation data and extract key entities including: conflict type, scope of impact, and urgency.

[0121] S53. Perform multi-dimensional similarity retrieval in the expert knowledge base, including spatial feature similarity, temporal feature similarity, and resource type similarity;

[0122] S54. By integrating the solutions of the top-3 similar cases through a weighted voting mechanism, process optimization consulting strategy data containing implementation priority markers is generated.

[0123] The BERT model extracts bias semantic features, the knowledge graph is linked to the historical case library, and a weighted voting fusion Top-3 solution is used.

[0124] Multi-dimensional similarity retrieval accurately matches historical cases, improving the adoption rate of optimization strategies.

[0125] Implement priority marking to guide resource allocation and avoid empirical decision-making biases.

[0126] S6 includes the following implementation details:

[0127] S61. Based on process optimization consulting strategy data, automatically adjust the coordinates of equipment path control points and process time parameters through the BIM parametric drive engine;

[0128] S62. Establish a modification impact assessment model to quantitatively analyze the chain effect of scheme adjustments on subsequent processes:

[0129] ;

[0130] in To influence the gradient, For the engineering objective function, To adjust the parameter vector, For parameter changes;

[0131] S63. Generates a 3D interactive process optimization report using the WebGL visualization engine, supporting the following interactive functions:

[0132] 360° scene rotation and viewing, process timeline zoom control, and cross-sectional perspective of conflict areas;

[0133] S64. Use color enhancement technology to mark three types of key modification areas, and use differentiated dynamic identifiers for different areas.

[0134] The BIM parametric engine automatically adjusts path coordinates and time parameters, while the WebGL engine supports interactive operations such as 360° rotation and timeline scaling.

[0135] This step modifies the impact assessment model to quantify the cascading effects and prevent the optimization plan from causing secondary conflicts.

[0136] Interactive reports improve the efficiency of understanding the plan and reduce the deviation rate of the construction party in execution.

[0137] The identification rules for key modification areas in S64 include:

[0138] High-risk conflict zone: Uses red pulse conflict display signs, superimposed with safety warning icons, and automatically plays an accident simulation animation when the user focuses on it;

[0139] Resource Optimization Area: Identified with a blue breathing light effect, clicking it displays a resource scheduling comparison chart and cost-saving data;

[0140] Progress Compression Zone: Identified by a yellow flowing light trail, the time compression scheme and key technical measures are displayed when hovering over it;

[0141] New green channel areas: Newly added areas such as construction access roads resulting from optimization will be marked with semi-transparent green high-brightness signs and associated with equipment traffic capacity parameters.

[0142] Color enhancement technology provides differentiated labeling for four types of areas: high-risk conflict areas, resource optimization areas, progress compression areas, and new channel areas, and integrates dynamic interactive functions.

[0143] Red pulse warning signs and accident animations enhance awareness of high-risk areas, while accident simulations reduce human error.

[0144] Semi-transparent green channel markers are associated with access parameters to optimize equipment scheduling paths.

[0145] The system includes: a BIM holographic data acquisition module, a process logic intelligent deconstruction module, a dynamic process simulation engine module, a physical field real-time monitoring module, an expert knowledge decision-making module, and a visualization solution generation module;

[0146] The BIM holographic data acquisition module uses a laser scanner and BIM software to collaboratively construct a full-element BIM model with time-series attributes.

[0147] The intelligent deconstruction module for process logic generates multi-level process logic relationship chains based on topology analysis and genetic algorithms.

[0148] The dynamic process simulation engine module simulates construction conflicts and generates early warning parameters in the Unity3D environment;

[0149] The real-time physical field monitoring module collects data through an IoT sensor cluster and calculates the deviation from the simulation results.

[0150] The expert knowledge decision-making module combines the BERT model with a case library to output process optimization consulting strategies.

[0151] The visualization solution generation module drives the WebGL engine to generate 3D interactive optimization solution reports.

[0152] The six modules—data acquisition, process deconstruction, simulation engine, monitoring module, decision-making module, and visualization module—work together to achieve closed-loop control.

[0153] The physics field monitoring module works in real time with the simulation engine to resolve texture misjudgment caused by light and shadow distortion.

[0154] The expert knowledge decision-making module outputs a strategy-driven visualization module, forming a closed loop for decision execution.

[0155] The expert knowledge decision-making module includes a conflict resolution strategy library, a resource allocation template library, and a legal compliance standard library. These sub-libraries are linked and stored through a graph database, supporting multi-dimensional case similarity retrieval of proposed questions.

[0156] The conflict resolution library, resource allocation library, and legal library are linked through a graph database, supporting multi-dimensional case retrieval.

[0157] Graph database association technology enables cross-database semantic retrieval, improving the speed of strategy generation.

[0158] The compliance standards library automatically verifies the legality of solutions, avoiding the risk of illegal construction.

[0159] The operation steps of this system and method are as follows:

[0160] Step 1: Dynamic Construction of Full-Element BIM Model

[0161] Millimeter-level point cloud data of the construction site is collected using high-precision laser scanning equipment, and a digital twin of the building structure is generated by combining it with the parametric component library of BIM modeling software. Simultaneously, construction schedule and resource scheduling data are integrated, and a dynamic association mapping between building components and process logic is established in the spatiotemporal fusion engine, forming a full-element BIM model with temporal attributes. This model reflects the spatial topological relationships of key construction elements such as beam-column node positioning accuracy and MEP pipeline layout paths in real time, providing an accurate digital foundation for subsequent process simulation.

[0162] Step 2: Deconstructing the Multi-Dimensional Process Logic Chain

[0163] Based on a spatial topology analysis engine using Building Information Modeling (BIM), the system intelligently identifies conflict points in equipment installation paths and process dependencies. A genetic algorithm optimizes the logical weights of the process network, constructing a multi-level process logic chain that includes a risk buffer period. Dynamic logic chain data is loaded into a digital twin environment to simulate the evolution of construction scenarios under extreme weather conditions, enabling pre-simulation of conflicts between tower crane trajectories and worker movement. A distributed IoT sensor cluster captures physical field data in real time, comparing it with the simulated progress curve at millisecond-level deviations to trigger a three-level early warning mechanism and pinpoint the root cause of the deviation.

[0164] Step 3: Intelligent Decision-Making and Adaptive Correction

[0165] When a deviation in process execution is detected, the semantic feature parsing engine extracts core feature entities such as conflict type and scope of impact, and performs spatiotemporal feature matching in a multi-dimensional expert knowledge base. A weighted voting mechanism is employed to integrate historical case solutions, generating an optimization strategy with priority markers. The parametric BIM-driven engine automatically adjusts the coordinates of equipment path control points and process sequence parameters based on the strategy data. Simultaneously, it modifies the impact assessment model to quantify the chain reaction on subsequent processes, forming a closed-loop feedback mechanism for decision-making.

[0166] Step 4: Immersive Visual Verification

[0167] In an interactive 3D environment, four key optimization areas are identified based on spatial topological relationships: high-risk conflict areas are marked with pulsed conflict indicators and associated with accident simulation animations; resource optimization areas are overlaid with dynamic breathing light effects and cost comparison charts; schedule compression areas are bound to flowing light trajectories to display time compression solutions; and newly added construction passage areas display passage parameters with semi-transparent highlighting. Construction personnel can deeply verify the feasibility of the solution through functions such as 360° scene rotation review, process timeline zoom control, and conflict area cross-sectional perspective, achieving lossless transfer from decision-making to execution.

[0168] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for process simulation and consulting service support based on BIM, characterized in that: The method includes the following steps: S1. Obtain the full-element BIM model data of the target project through BIM data acquisition equipment. The full-element BIM model data includes building structure parameters, electromechanical pipeline layout parameters, construction resource distribution parameters, and schedule parameters. S2. Based on the full-element BIM model data, perform intelligent decomposition processing of construction procedures to generate multi-level process logical relationship chain data, which includes key process nodes, process dependencies, and process timing constraint parameters. S3. Combine the construction environment simulation engine to perform dynamic process simulation and deduction on the multi-level process logical relationship chain data to generate dynamic simulation data of construction process. The dynamic simulation data of construction process includes process conflict early warning parameters, resource scheduling optimization parameters and schedule deviation prediction parameters. S3 includes the following key operations: S31. Create a 1:1 digital twin scene in the Unity3D engine and load multi-level process logic relationship chain data to drive the simulation of tower crane running trajectory; S32, via A The pathfinding algorithm calculates the optimal movement path for workers and detects equipment collision risks and violations of safety protection distances in real time. ； in The actual cost from the starting point to point n. Let n be the estimated cost from point n to the destination. For dynamic obstacle distance factor, For safety factor; S33. Establish a progress delay prediction model. When the deviation of concrete setting time is detected to exceed the threshold, a three-level early warning mechanism will be automatically triggered. S34. Calculate the impact of material loading on temporary support structures based on the physics engine, and generate dynamic simulation data of construction procedures including stress distribution cloud maps; S4. Real-time monitoring of the matching degree between construction physical field data and dynamic simulation data of construction procedures, generating process execution status deviation data; S4 includes the following monitoring mechanisms: S41. Deploy distributed IoT sensor clusters at key nodes on the construction site, including tower crane tilt sensors, concrete temperature and humidity sensors, and UWB worker positioning base stations. S42. Transmit physical field data to the central processing platform in real time through the 5G edge computing gateway, and use timestamp alignment technology to ensure the spatiotemporal synchronization of data acquisition and simulation. S43. Construct a sliding time window for the Dynamic Time Warping (DTW) algorithm and calculate the shape similarity between the actual progress curve and the simulated progress curve. S44. When the deviation value of three consecutive sampling cycles exceeds 5%, automatically generate process execution status deviation data with positioning coordinates. S5. Based on the process execution status deviation data and the preset expert knowledge base, perform intelligent matching analysis to generate process optimization consulting strategy data; S6. Dynamically correct the full-element BIM model data based on the process optimization consulting strategy data, and generate a visualized process optimization scheme report data.

2. The method for BIM-based process simulation and consulting service support according to claim 1, characterized in that: S1 includes the following specific steps: S11. Collect real-world point cloud data of the construction site with a resolution of 0.1mm using a high-precision ground laser scanning device, and at the same time, obtain parametric model data of building components by connecting to the API interface of BIM modeling software. S12. The ICP point cloud registration algorithm is used to align the real-time collected point cloud data with the BIM parametric model in spatial coordinates to generate an initial BIM structural model with sub-millimeter accuracy. S13. Synchronize the Gantt chart time node data of the construction progress management platform with the resource inventory list data of the material management platform through the ODBC database connector; S14. Establish a three-dimensional mapping relationship between time, space, and resources in the BIM data fusion engine, and dynamically associate the schedule parameters with building components through time axis binding technology to generate full-element BIM model data with time sequence attributes.

3. The method for BIM-based process simulation and consulting service support according to claim 1, characterized in that: S2 includes the following deep processing flow: S21. Based on the IFC standard format of the BIM model, the spatial topological relationship of building components is analyzed, and the graph neural network algorithm is used to identify the three-dimensional spatial constraint relationship of beam-column nodes and pipeline intersections. S22. When constructing the process logic network diagram, the installation path of critical equipment is inserted as a virtual node into the CPM critical path, and the process dependency weight coefficient is iteratively optimized through a genetic algorithm: ； in It is a time cost function. For resource conflict functions, For risk coefficient function, , , As a dynamic weighting factor, + + =1; S23. The Monte Carlo simulation method is used to conduct sensitivity analysis on the process sequence under extreme weather conditions, and multi-level process logical relationship chain data including risk buffer period is generated. S24. Push the process logic chain data to the field mobile terminal device in real time via the WebSocket protocol.

4. The method for BIM-based process simulation and consulting service support according to claim 1, characterized in that: The S5 executes the following intelligent decision-making process: S51. Construct a multi-dimensional expert knowledge base, including a historical engineering case library, an industry standard clause library, and a technical standard text library, with each library linked through a knowledge graph; S52. Use the BERT pre-trained model to analyze the semantic features of process deviation data and extract key entities including: conflict type, scope of impact, and urgency. S53. Perform multi-dimensional similarity retrieval in the expert knowledge base, including spatial feature similarity, temporal feature similarity, and resource type similarity; S54. By integrating the solutions of the top-3 similar cases through a weighted voting mechanism, process optimization consulting strategy data containing implementation priority markers is generated.

5. The method for BIM-based process simulation and consulting service support according to claim 1, characterized in that: S6 includes the following implementation details: S61. Based on process optimization consulting strategy data, automatically adjust the coordinates of equipment path control points and process time parameters through the BIM parametric drive engine; S62. Establish a modification impact assessment model to quantitatively analyze the chain effect of scheme adjustment on subsequent processes; S63. Generates a 3D interactive process optimization report using the WebGL visualization engine, supporting the following interactive functions: 360° scene rotation and viewing, process timeline zoom control, and cross-sectional perspective of conflict areas; S64. Use color enhancement technology to mark three types of key modification areas, and use differentiated dynamic identifiers for different areas.

6. The BIM-based process simulation and consulting service support method according to claim 5, characterized in that: The identification rules for key modification areas in S64 include: High-risk conflict zone: Uses red pulse conflict display signs, superimposed with safety warning icons, and automatically plays an accident simulation animation when the user focuses on it; Resource Optimization Area: Identified with a blue breathing light effect, clicking it displays a resource scheduling comparison chart and cost-saving data; Progress Compression Zone: Identified by a yellow flowing light trail, the time compression scheme and key technical measures are displayed when hovering over it; New green channel area: The newly added areas of construction access roads resulting from the optimization are marked with semi-transparent green high-brightness signs and linked to equipment traffic capacity parameters.

7. A BIM-based process simulation and consulting service support system, used to implement the BIM-based process simulation and consulting service support method according to any one of claims 1-6, characterized in that: The system includes: a BIM holographic data acquisition module, a process logic intelligent deconstruction module, a dynamic process simulation engine module, a physical field real-time monitoring module, an expert knowledge decision-making module, and a visualization solution generation module; The BIM holographic data acquisition module uses a laser scanner and BIM software to collaboratively construct a full-element BIM model with temporal attributes. The process logic intelligent deconstruction module generates a multi-level process logic relationship chain based on topology analysis and genetic algorithm. The dynamic process simulation engine module simulates construction conflicts and generates early warning parameters in the Unity3D environment; The physical field real-time monitoring module collects data through an IoT sensor cluster and calculates the deviation with the simulation results. The expert knowledge decision-making module combines the BERT model with the case library to output process optimization consultation strategies. The visualization scheme generation module drives the WebGL engine to generate a 3D interactive optimization scheme report.

8. The BIM-based process simulation and consulting service support system according to claim 7, characterized in that: The expert knowledge decision-making module includes a conflict resolution strategy library, a resource allocation template library, and a legal compliance standard library. Each sub-library is stored in a graph database and supports multi-dimensional case similarity retrieval.

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