Subway section parameterized linear digital model construction method based on Revit and Dynamo

Abstract

The invention provides a metro section parameterized linear digital model construction method based on Revit and Dynamo, and the method comprises the steps: building a three-dimensional space control reference through analyzing track line design data, and constructing a metro section model in a manner of combining parameterized modeling with an intelligent algorithm; the contact network and pipeline models are ensured to meet the standard requirements through real-time verification of the conductor height change rate and the turning radius-pipe diameter matching matrix; space coordinate unification and interface matching precision control are achieved through a multi-professional model cooperation system, and a parameter linkage updating mechanism is established to respond to design changes; and finally, outputting an LOD400 precision BIM model and associating construction data for dynamic correction, and supporting RVT / IFC format conversion and engineering quantity automatic statistics. The method is mainly used for efficient collaborative design and construction management and control of metro sections, the modeling precision and the multi-specialty coordination efficiency can be improved, and the design error rate is reduced.

Description

Technical Field

[0001] The present invention relates to the application field of BIM technology in subway projects. More specifically, the present invention relates to a method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo. Background Art

[0002] The application of BIM technology in subway section engineering design has become a crucial tool for improving design quality. However, existing technologies still face several challenges in practical application. First, traditional modeling methods primarily rely on manual modeling or the construction of single-discipline models based on limited parametric tools, making them ill-suited to the complex demands of linear geometric features and the dynamic interrelationships between multiple parameters in subway sections. Line horizontal and vertical section data (such as slope and superelevation) are strictly spatially coupled with the geometric parameters of components such as tunnel sections and track foundations. However, existing modeling processes often require manual conversion or phased import of such data, resulting in model accuracy being limited by the designer's experience and requiring significant time-consuming repetitive adjustments. Particularly in areas where curved sections and slope variations overlap, the lack of a unified spatial control benchmark allows for cumulative and amplified positioning errors among the various disciplines, impacting the efficiency of subsequent multi-disciplinary collaboration.

[0003] Secondly, the collaborative design of multi-disciplinary models suffers from insufficient coordination. Subway sections involve multiple subsystems, including tunnel structures, tracks, catenary systems, and pipelines. Each discipline's models are typically constructed by independent teams using heterogeneous software platforms, lacking a unified mechanism for associating the model's spatial coordinate system with interface parameters. For example, the catenary's suspension height must be dynamically adjusted with track superelevation, and pipeline layouts must meet minimum clearance requirements from the tunnel's internal contours. However, existing technologies often rely on manual verification for such cross-disciplinary parameter associations, making it difficult to detect spatial conflicts in real time. This leads to frequent pipeline collisions or insufficient space for equipment installation during the construction phase. The root cause of these coordination deficiencies lies in the lack of dynamic correlation between model data and the failure of the parameter update mechanism to achieve cross-disciplinary linkage, resulting in delayed responses to design changes.

[0004] Furthermore, the linkage and standardization of parametric modeling urgently need to be improved. While existing parametric tools can achieve parameter-driven control of single components, they still have limitations when faced with multi-parameter coupling scenarios within subway sections. For example, the assembly angle of shield segments needs to be adjusted in real time based on the longitudinal slope of the line. However, in traditional methods, these two parameters are often set independently, which can easily lead to excessive misalignment of the segment circumferential joints. The track base layout must take into account the combined effects of the track bottom slope and line superelevation, but the existing modeling process does not automate the logic of such parameter associations and still requires repeated manual verification. At the same time, key parameters subject to regulatory constraints (such as the rate of change of catenary height and the matching relationship between pipeline turning radius and pipe diameter) lack a real-time verification mechanism. Design compliance relies on later manual review, which is prone to omissions. The essence of these problems lies in the lack of structured expression of the dynamic relationship between parameters and the lack of deep embedding of regulatory constraints into the modeling process.

[0005] On the other hand, there is a disconnect between existing BIM models and data interaction during the construction phase. Design-phase models are usually constructed based on theoretical data, but during the construction process, on-site deviations caused by factors such as measurement errors and changes in geological conditions are difficult to promptly feed back to the model, causing the model to gradually drift away from the physical project. Especially in long-line subway projects, the linear accumulation of small deviations may cause the overall model to be inaccurate, but traditional methods lack a dynamic model correction mechanism and are difficult to support visual control of the construction process. In addition, problems such as the single model delivery format and the low degree of automation in engineering quantity statistics also restrict the application value of BIM technology throughout the entire life cycle.

[0006] The technical difficulties contributing to these issues primarily include: 1) the difficulty of dynamically establishing and maintaining linear engineering spatial datums, requiring the resolution of spatial mapping relationships between multidisciplinary components within the line centerline coordinate system; 2) the difficulty of precisely controlling the nonlinear coupling relationships between multi-source parameters using conventional parametric methods; 3) the lack of digital conversion and real-time verification mechanisms for cross-disciplinary design specifications; and 4) the lack of efficient data pathways for bidirectional synchronization between construction data and design models. These technical bottlenecks have resulted in inefficient BIM model construction for subway sections and high coordination costs, becoming a key obstacle to the advancement of digitalization in rail transit engineering. Summary of the Invention

[0007] An object of the present invention is to solve at least the above problems and to provide at least the advantages which will be described hereinafter.

[0008] Another object of the present invention is to provide a method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, so as to solve the problems of low efficiency of multi-professional collaboration, difficulty in dynamic adjustment of parameters and frequent model conflicts in the subway section modeling process. Traditional methods rely on manual operation and lack a global parameter association mechanism.

[0009] Another object of the present invention is to solve the problems of coordinate accumulation error and low correction efficiency caused by dynamic changes in curvature radius and excessive slope when establishing a three-dimensional space control benchmark.

[0010] Another purpose of the present invention is to solve the problem of nonlinear correlation between the longitudinal slope of the tunnel and the assembly angle of the pipe segments and the difficulty in real-time verification of spatial conflicts between the pipe segments and the contact network and pipelines.

[0011] Another object of the present invention is to solve the problem that the track base arrangement scheme is difficult to optimize due to the combined influence of the rail bottom slope angle and the line superelevation value, and has a high risk of interference with the tunnel structure.

[0012] Another purpose of the present invention is to solve the problem of excessive change rate of contact network contact height and low correction efficiency when the contact height value changes dynamically with the line slope and superelevation value.

[0013] Another object of the present invention is to solve the problems of low path optimization efficiency and high collision risk caused by turning radius constraints and space conflicts in pipeline layout path planning.

[0014] Another object of the present invention is to solve the model conflict problem caused by insufficient interface matching accuracy and spatial coordinate deviation when integrating multi-disciplinary models.

[0015] Another object of the present invention is to solve the problem that the update of the association model after the parameter change depends on manual intervention and is prone to introduce circular dependency logic errors.

[0016] To achieve these objectives and other advantages according to the present invention, a method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo is provided, which includes the following steps: S1. Call Dynamo to analyze the track design data and establish a three-dimensional spatial control benchmark. The design data includes the track slope and superelevation value. S2. Input shield segment parameters, associate segment assembly angles with tunnel longitudinal slopes through Dynamo, and dynamically generate a tunnel section BIM model based on the topological relationship matrix to verify relative positions with the catenary and pipelines. S3. Input track base parameters, associate them with the rail bottom slope angle and line superelevation value through Dynamo, and optimize the track base layout plan based on the machine learning model to dynamically generate a track base BIM model. S4. Input the catenary support parameters and use Dynamo to correlate the catenary's suspension height (lead height) with the track slope and superelevation value. Verify the suspension height (lead height) change rate in real time and dynamically generate a catenary BIM model that meets industry design specifications ("Metro Design Specifications"). S5. Input comprehensive pipeline parameters, optimize pipeline layout through spatial meshing algorithm and reinforcement learning model, and dynamically generate conflict-free pipeline BIM model based on turning radius-pipe diameter matching matrix; S6. Based on the line centerline coordinates, the tunnel section, track foundation, catenary, and pipeline BIM models are integrated in Revit. The multi-disciplinary model collaboration system ensures the uniformity of spatial coordinates and an interface matching accuracy error of ≤3mm. S7. When any parameter changes, the BIM model involved is automatically adjusted through the parameter linkage update mechanism; S8, output BIM model with LOD400 accuracy, associate construction measurement data to correct model deviation in real time, support RVT, IFC format and automatic generation of bill of quantities; Among them, steps S2 to S5 are all based on the three-dimensional space control benchmark established in step S1.

[0017] Preferably, in the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, the step S1 of "establishing a three-dimensional spatial control benchmark" includes: S1.1. Based on the two-dimensional plane coordinates and elevation data in the track line design data, generate the three-dimensional reference data of the global engineering coordinate system according to the following rules: Calculate the translation amount in the global coordinate system based on the plane coordinate difference between the starting and ending points of the line; Based on the angle between the line direction and the X-axis of the global coordinate system, the rotation angle of the local coordinate system is dynamically determined; Generate the scaling factor from local coordinates to global coordinates based on the unit conversion relationship between the design scale and the global coordinate system; S1.2. Based on the 3D reference data generated in S1.1, perform the following verification and correction operations: If the curvature radius of a certain segment is less than the preset threshold, the curvature data of the adjacent segments are extracted, the corrected curvature radius is generated through the interpolation algorithm, and the corresponding coordinates are recalculated; If the slope gradient exceeds the allowable range of the specification, a corrected slope is generated based on the weighted average of the slope values ​​of adjacent sections, and the elevation coordinates are updated in reverse order; S1.3. When the line design parameters are changed, perform the following operations: Based on the translation, rotation, and scaling parameters of S1.1 and the corrected data of S1.2, the three-dimensional coordinates of the affected segments are regenerated using the piecewise cubic spline interpolation algorithm; The updated coordinates are synchronized to the standardized model geometry database, triggering the parametric reconstruction of the associated model.

[0018] Preferably, in the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, step S2 of "associating segment assembly angles with tunnel longitudinal slopes to dynamically generate a tunnel section BIM model" includes: S2.1. Divide the tunnel longitudinal slope data into equally spaced segments at preset mileage intervals, with each segment being 10 meters long. Perform the following operations within each segment: Taking the slope of the starting point of the segment as the benchmark, the quadratic term coefficient is determined according to the product of the curvature radius and the slope change rate in the segment to construct a piecewise nonlinear function; Substitute the slope value corresponding to the current mileage into the piecewise function and output the segment assembly angle correction value; S2.2. Establish real-time correlation logic between segment assembly angles and tunnel longitudinal slope in Dynamo, including: Analyze the tunnel longitudinal slope sensor data and match it with the piecewise function corresponding to the current mileage; Map the assembly angle correction value output by the function to the rotation parameter of the shield segment family to drive the dynamic adjustment of the segment angle; S2.3. Based on the geometric features of the tunnel section BIM model, construct a topological relationship matrix containing the relative positions of the catenary supports, pipelines, and segments. Perform the following operations: Extract the coordinates of key points on the outer contour of the segment and calculate the minimum spatial distance between them and the contact network suspension point and the pipeline centerline; If the distance is less than the preset safety threshold, it is marked as a conflict area and the segment assembly angle or contact network suspension height is automatically adjusted; S2.4. Based on the corrected segment assembly angles and topology verification results, the spatial arrangement sequence of the shield segment family is reconstructed through the Dynamo script to generate a tunnel section BIM model that meets the design specifications.

[0019] Preferably, in the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, step S3 of "associating the rail bottom slope angle with the line superelevation value to dynamically generate a track base BIM model" includes: S3.1. Divide the combination of the longitudinal slope and curve radius of the line into weight calculation units at preset mileage intervals, with each unit length being 50 meters. Perform the following operations: Based on the proportional relationship between the absolute value of the line slope gradient and the inverse of the curve radius, the weight value is dynamically assigned. The weight value calculation formula is: Weight value = 0.6 × |slope gradient| + 0.4 × (1 / curve radius) The unit of slope gradient is ‰, and the unit of curve radius is meter. When the train design speed is ≥80 km / h, the adjustment weight coefficient is 0.7:0.3; S3.2. Establish the dynamic association logic between the rail bottom slope angle and the line superelevation value in Dynamo, including: Input the weight value of the current mileage into the linear interpolation function, map it to the rail bottom slope angle range (1:40 to 1:20), and output the actual correction angle; If the line section contains a composite curve, the weight value is Gaussian smoothed and re-interpolated; S3.3. Based on historical track base layout data and construction constraints, train a random forest regression model to perform the following operations: Input features include rail bottom slope correction angle, line superelevation value, geological parameters and track base spacing; The output is the optimal horizontal offset of the track base arrangement scheme; Call the model interface through Dynamo to drive the dynamic adjustment of the spatial positioning parameters of the track base family; S3.4. Based on the corrected rail bottom slope angle and the optimized layout plan, verify the minimum clearance between the track base and the tunnel structure. If there is a conflict, redistribute the weight coefficients and perform iterative calculations to ultimately generate a non-interference track base BIM model.

[0020] Preferably, in the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, step S4 of "associating the overhead contact network suspension height with the track line slope and the line superelevation value to dynamically generate an overhead contact network BIM model" includes: S4.1. Based on the track slope and superelevation at the current mileage, calculate the theoretical superelevation value using the following formula: in, is the reference height specified in the design specification, S is the slope gradient, and C is the superelevation value. 、 is the dynamic correction factor related to train speed; S4.2. Use Dynamo to analyze the theoretical values ​​of the conductance height at consecutive mileage points in real time and calculate the conductance height change rate: If |ΔH| exceeds the allowed threshold of the specification, it is marked as an abnormal section; S4.3. Perform the following operations on the abnormal segment: Extract the theoretical height-guided values ​​of the five mileage points before and after, and use sliding window mean filtering to generate the corrected height-guided values; If the limit is still exceeded after correction, the current elevation parameter is overwritten based on the trend prediction value of the adjacent section slope and superelevation value; S4.4. Map the corrected conductor height parameters to the suspension point height of the contact network support family, drive the support family instantiation through the Dynamo script, and generate the contact network BIM model.

[0021] Preferably, in the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, the step S5 of "optimizing the pipeline layout plan and dynamically generating a conflict-free pipeline BIM model" includes: S5.1. Based on the 3D tunnel cross-section model, divide the space into cubic grid cells with a side length of 0.5 meters and perform the following operations: Label the attributes of each grid cell, including the structure type (segment, track base, catenary support) and the allowed passage status; The pipeline path planning problem is transformed into an optimization problem of a grid cell sequence, and the state space is defined as a coordinate set of traversable grids. S5.2. Train the Deep Q Network (DQN) model and define the following parameters: The direction of the pipeline path in the grid cell (front / back / left / right / up / down); Path length weight (-0.3 × length), collision penalty (-1.0 × number of collisions), turn radius compliance bonus (+0.5 × number of compliant turns); Call the model interface through Dynamo to output the optimal path control point sequence; S5.3. Construct a turning radius constraint matrix based on pipeline type and diameter parameters, including: Define pipe diameter D and minimum turning radius R min The mapping relationship satisfies R min =3D (straight pipe) or R min =5D(elbow); In the path control point sequence, if the angle between adjacent line segments is greater than 30°, check whether the actual turning radius meets the matrix constraints. Otherwise, insert a transition arc control point. S5.4. Map the optimized path control point sequence to the centerline parameters of the pipeline family, drive the pipeline family instantiation through the Dynamo script, and generate a collision-free pipeline BIM model.

[0022] Preferably, in the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, the step S6 of "integrating BIM models of tunnel sections, track foundations, contact lines, and pipelines" includes: S6.1. Based on the line centerline coordinate data, establish a model integration benchmark in the global coordinate system in Revit and perform the following operations: Align the central axis of the tunnel section BIM model with the line centerline coordinates, with a deviation threshold of ≤2mm; The spatial positioning parameters of the track base, catenary support, and pipelines are analyzed using Dynamo scripts, and their origins are bound to the normal plane coordinates of the line centerline. S6.2. Build an interface matching rule library based on the geometric feature parameters of each BIM model, specifically including: Coordinate offset mapping relationship between track base anchor bolt hole positions and tunnel embedded parts; Vertical spacing constraints between the overhead contact suspension point and the tunnel vault hoisting hole; The horizontal clearance threshold between the pipeline support and the track base; S6.3. Verify the consistency of the integrated model space coordinates in real time. If the interface matching error is greater than 3mm, perform the following operations: Extract the coordinate data of the conflicting components and infer the local coordinate system correction value based on the curvature radius of the line centerline; Use Dynamo to fine-tune the translation and rotation parameters of the conflicting components until the error threshold is met; S6.4. Export the revised multi-disciplinary BIM model into independent Revit files according to the route mileage, and generate a model positioning index table in the global coordinate system.

[0023] Preferably, in the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, the "parameter linkage update mechanism" in step S7 includes: S7.1. Analyze the function call chain and input-output parameter mapping relationship in the Dynamo script to generate a directed graph structure, where: Nodes represent model component parameter variables, including tunnel section dimensions, track base offset, contact network height values, and pipeline path control points; Edges represent functional dependencies between parameters, including direct computational associations and indirect logical constraints; S7.2. Real-time monitoring of changes in the values ​​of input parameters, including: The parameter value is hashed using the SHA-256 algorithm. If the current digest is inconsistent with the historical version, it is marked as a changed parameter. According to the dependency graph, traverse all downstream associated nodes of the parameter node to generate a set of parameters to be updated; S7.3. Trigger update instructions sequentially from the bottom to the top of the dependency hierarchy, specifically including: Directly update independent parameters without dependencies; For node groups with circular dependencies, a topological sorting algorithm is used to decompose them into acyclic subgraphs and then update them in order; S7.4. Perform the following checks on the updated model components: Extract the minimum bounding box of the component geometry and calculate its interference volume with the associated components; If the interference volume is greater than the tolerance threshold, go back to the last valid parameter version and freeze the current parameter change instruction; S7.5. Write the numerical differences before and after the parameter change, and the types and quantities of affected components into a structured log file in the format of a JSON array.

[0024] The present invention has at least the following beneficial effects: Through global parametric logic and a multi-disciplinary model collaboration system, this invention realizes the dynamic generation and real-time integration of various professional models of subway sections, significantly reducing manual intervention and improving modeling efficiency; the parameter linkage update mechanism ensures model consistency during design changes, and the LOD400 precision model can be directly used for construction guidance and engineering quantity statistics, reducing the rework rate.

[0025] The present invention is based on piecewise cubic spline interpolation and dynamic correction algorithm, which effectively suppresses coordinate errors caused by sudden changes in curvature radius and excessive slope, ensures the continuity and accuracy of the three-dimensional spatial benchmark, and provides unified spatial data support for multi-disciplinary models.

[0026] The present invention uses piecewise nonlinear functions and topological relationship matrices to achieve dynamic matching of segment assembly angles and longitudinal slopes, and predicts spatial conflicts between segments and contact networks and pipelines, reducing the workload of subsequent collision detection and improving the compliance of tunnel section models.

[0027] The present invention combines a random forest regression model with a dynamic weight allocation mechanism to optimize the track base layout scheme. While satisfying the constraints of track bottom slope and superelevation, it avoids the risk of interference with tunnel structures and improves the construction feasibility of the track base model.

[0028] The present invention is based on a sliding window mean filter and trend prediction algorithm to quickly correct abnormal sections of conductor height, ensure that the change rate of the contact network suspension height meets the requirements of the specifications, and avoid the risk of contact network failure caused by sudden changes in conductor height.

[0029] The present invention adopts a deep Q network and a turning radius constraint matrix to efficiently plan conflict-free pipeline paths, reduce the number of collision detection iterations, meet the engineering constraints of pipe diameter and turning radius, and improve the reliability and construction adaptability of the pipeline model.

[0030] The present invention realizes high-precision integration of multi-professional models through the interface matching rule library and the local coordinate system correction algorithm, with an interface error of ≤3mm, avoiding the construction interface mismatch problem caused by coordinate deviation and ensuring the quality of project implementation.

[0031] The dependency graph and topological sorting algorithm of the present invention ensure that there are no logical conflicts in the parameter update process, the hash summary and interference verification mechanism prevent the spread of erroneous changes, and the structured log supports problem tracing, thereby improving the reliability and maintainability of model updates.

[0032] Other advantages, objectives and features of the present invention will be reflected in part from the following description and will be understood by those skilled in the art through study and practice of the present invention. DETAILED DESCRIPTION

[0033] The present invention is further described in detail below with reference to the embodiments so that those skilled in the art can implement the invention with reference to the description.

[0034] It should be understood that terms such as “having”, “including” and “comprising” used herein do not preclude the existence or addition of one or more other elements or combinations thereof.

[0035] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials can be obtained from commercial channels unless otherwise specified.

[0036] The present invention provides a method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, which comprises the following steps: S1. Call Dynamo to analyze the track design data and establish a three-dimensional spatial control benchmark. The design data includes the track slope and superelevation value. S2. Input shield segment parameters, associate segment assembly angles with tunnel longitudinal slopes through Dynamo, and dynamically generate a tunnel section BIM model based on the topological relationship matrix to verify relative positions with the catenary and pipelines. S3. Input track base parameters, associate them with the rail bottom slope angle and line superelevation value through Dynamo, and optimize the track base layout plan based on the machine learning model to dynamically generate a track base BIM model. S4. Input the catenary support parameters and use Dynamo to correlate the catenary's suspension height (lead height) with the track slope and superelevation value. Verify the suspension height (lead height) change rate in real time and dynamically generate a catenary BIM model that meets industry design specifications ("Metro Design Specifications"). S5. Input comprehensive pipeline parameters, optimize pipeline layout through spatial meshing algorithm and reinforcement learning model, and dynamically generate conflict-free pipeline BIM model based on turning radius-pipe diameter matching matrix; S6. Based on the line centerline coordinates, the tunnel section, track foundation, catenary, and pipeline BIM models are integrated in Revit. The multi-disciplinary model collaboration system ensures the uniformity of spatial coordinates and an interface matching accuracy error of ≤3mm. S7. When any parameter changes, the BIM model involved is automatically adjusted through the parameter linkage update mechanism; S8, output BIM model with LOD400 accuracy, associate construction measurement data to correct model deviation in real time, support RVT, IFC format and automatic generation of bill of quantities; Among them, steps S2 to S5 are all based on the three-dimensional space control benchmark established in step S1.

[0037] The present invention's technology for establishing and dynamically correcting a three-dimensional spatial control datum involves analyzing track slope and superelevation values ​​and performing coordinate conversion. The Dynamo plug-in for the Revit platform can be used to analyze line design data. The preset threshold for the curvature radius is 300 meters, and the permitted range for slope gradient is ±5‰ (according to the "Metro Design Code" GB 50157). When the curvature radius of a section falls below the threshold, the coordinates are corrected using a cubic spline interpolation algorithm with an interpolation node spacing of 10 meters. When the slope exceeds the threshold, the coordinates are corrected based on the weighted average of the slope values ​​of adjacent sections, with a weighting factor of 0.6 for the preceding section and 0.4 for the succeeding section. During the coordinate conversion, the rotation angle of the local coordinate system is dynamically calculated based on the angle between the track alignment and the X-axis of the global coordinate system, and a scaling factor is generated using a 1:1 ratio. The corrected three-dimensional datum data is stored in a SQLite database and synchronized to the geometry feature library via the Revit API.

[0038] In this invention's parametric generation and conflict prediction technology for multi-disciplinary BIM models, the link between shield segment assembly angles and the tunnel's longitudinal slope is achieved through a piecewise nonlinear function, with segments divided into 10-meter segments. The coefficient for the product of the curvature radius and the slope change rate is set to 0.05. The minimum safe distance threshold between the segment's outer contour and the catenary suspension point is 0.5 meters, and segment rotation angles and lead-off values ​​can be automatically adjusted using a Dynamo script. Track pedestal layout is optimized using a random forest regression model. Input features include the rail bottom slope angle (1:40 to 1:20), the line superelevation value (0-150mm), and the pedestal spacing (0.6-1.2 meters). The output is an optimized horizontal offset within a ±50mm range. In pipeline path planning, the turning radius constraint matrix defines R_min = 3D for straight pipes and R_min = 5D for curved pipes (D is the pipe diameter). When the angle between adjacent line segments exceeds 30°, a transition arc is inserted, with a spacing of 0.2 meters between arc control points.

[0039] In the multi-professional model integration and parameter linkage update technology of the present invention, when the model is integrated, the alignment deviation threshold between the central axis of the tunnel section and the center line of the line is 2mm, and the interface matching error threshold is 3mm. The coordinate correction of the conflicting components is based on the local coordinate system offset inverted by the line curvature radius, and the fine-tuning step is 0.1mm / time. The parameter linkage update adopts directed graph dependency analysis. The nodes include parameters such as tunnel section size and track base offset. The circular dependency node group is disassembled into acyclic subgraphs through topological sorting. The hash summary check uses the SHA-256 algorithm, and the interference volume tolerance threshold is 0.001m 3 The change log is recorded in JSON format, including parameter differences, affected component types and quantities. This invention utilizes dynamic correction of three-dimensional spatial datums and multi-disciplinary parametric modeling to automate the generation and high-precision alignment of track, tunnel, catenary, and pipeline models. A conflict prediction mechanism addresses issues such as pipeline collisions and excessive height guides during the design phase, reducing subsequent rework. Linked parameter updates ensure the real-time and consistency of model changes. LOD400 precision models can be directly used for construction layout and quantity statistics, enhancing the standardization of subway section design.

[0040] In another technical solution, the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, in step S1, "establishing a three-dimensional spatial control benchmark" includes: S1.1. Based on the two-dimensional plane coordinates and elevation data in the track line design data, generate the three-dimensional reference data of the global engineering coordinate system according to the following rules: Calculate the translation amount in the global coordinate system based on the plane coordinate difference between the starting and ending points of the line; Based on the angle between the line direction and the X-axis of the global coordinate system, the rotation angle of the local coordinate system is dynamically determined; Generate the scaling factor from local coordinates to global coordinates based on the unit conversion relationship between the design scale and the global coordinate system; S1.2. Based on the 3D reference data generated in S1.1, perform the following verification and correction operations: If the curvature radius of a certain segment is less than the preset threshold, the curvature data of the adjacent segments are extracted, the corrected curvature radius is generated through the interpolation algorithm, and the corresponding coordinates are recalculated; If the slope gradient exceeds the allowable range of the specification, a corrected slope is generated based on the weighted average of the slope values ​​of adjacent sections, and the elevation coordinates are updated in reverse order; S1.3. When the line design parameters are changed, perform the following operations: Based on the translation, rotation, and scaling parameters of S1.1 and the corrected data of S1.2, the three-dimensional coordinates of the affected segments are regenerated using the piecewise cubic spline interpolation algorithm; The updated coordinates are synchronized to the standardized model geometry database, triggering the parametric reconstruction of the associated model.

[0041] In the three-dimensional reference data generation and coordinate conversion technology of the present invention, the three-dimensional reference data is generated by converting the two-dimensional plane coordinates and elevation data of the track line. The translation of the global coordinate system is calculated based on the plane coordinate difference between the starting and ending points of the line. For example, when the starting point coordinates are (X1, Y1) and the end point is (X2, Y2), the translation is ΔX = X2-X1 and ΔY = Y2-Y1. The rotation angle of the local coordinate system can be dynamically determined based on the angle between the line direction and the X-axis of the global coordinate system. For example, when the line direction deflects 15°, the rotation angle is set to 15°. The scaling factor is set according to the design scale. For example, at a scale of 1:500, the scaling factor from local coordinates to global coordinates is 1.0. The Dynamo plug-in for Revit can be used to implement coordinate conversion, and the three-dimensional reference data can be stored in a SQLite database.

[0042] In the dynamic verification and correction technology of curvature and slope of the present invention, when the curvature radius of a certain section is less than a preset threshold (for example, 300 meters), the curvature data of the adjacent sections are extracted (such as a range of 50 meters before and after), and the corrected curvature radius is generated using a cubic spline interpolation algorithm, with an interpolation node spacing of 10 meters. When the slope gradient exceeds the limit (for example, exceeds ±5‰), it is corrected based on the weighted average of the slope values ​​of the adjacent sections, with the weight coefficient being 0.6 for the front section and 0.4 for the rear section. The corrected elevation coordinates are updated through an inverse algorithm, for example, the slope correction value ΔS=0.6×Sprev+0.4×Snext, and substituted into the elevation formula Hnew=Hold+ΔS×L (L is the section length). MATLAB scripts can be used to perform interpolation calculations, and the corrected data are synchronized to the model geometry feature library through the Revit API.

[0043] In the coordinate reconstruction and synchronization technology for parameter changes presented herein, when route design parameters are changed, a piecewise cubic spline interpolation algorithm is used to regenerate the 3D coordinates of the affected sections. For example, when the slope of a 100-meter section is adjusted, the section is divided into 10 sub-segments, and the coordinates of each sub-segment's endpoints are smoothly connected using a spline function. The updated coordinates are written to a standardized model geometry database via an ODBC interface, triggering a parametric reconstruction of the associated model. Revit's "Collaborative Worksharing" feature can be used to enable multi-user collaborative updates, keeping model reconstruction delays to less than 5 seconds. This invention ensures the continuity and accuracy of the three-dimensional spatial datum through dynamic coordinate conversion and interpolation correction algorithms, suppressing the cumulative errors caused by sudden changes in curvature and excessive slopes. Piecewise spline interpolation and a collaborative update mechanism during parameter changes ensure model reconstruction efficiency and data consistency, providing reliable spatial datum support for multi-disciplinary BIM models.

[0044] In another technical solution, the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, in step S2 of "associating segment assembly angles with tunnel longitudinal slopes to dynamically generate a tunnel section BIM model" includes: S2.1. Divide the tunnel longitudinal slope data into equally spaced segments at preset mileage intervals, with each segment being 10 meters long. Perform the following operations within each segment: Taking the slope of the starting point of the segment as the benchmark, the quadratic term coefficient is determined according to the product of the curvature radius and the slope change rate in the segment to construct a piecewise nonlinear function; Substitute the slope value corresponding to the current mileage into the piecewise function and output the segment assembly angle correction value; S2.2. Establish real-time correlation logic between segment assembly angles and tunnel longitudinal slope in Dynamo, including: Analyze the tunnel longitudinal slope sensor data and match it with the piecewise function corresponding to the current mileage; Map the assembly angle correction value output by the function to the rotation parameter of the shield segment family to drive the dynamic adjustment of the segment angle; S2.3. Based on the geometric features of the tunnel section BIM model, construct a topological relationship matrix containing the relative positions of the catenary supports, pipelines, and segments. Perform the following operations: Extract the coordinates of key points on the outer contour of the segment and calculate the minimum spatial distance between them and the contact network suspension point and the pipeline centerline; If the distance is less than the preset safety threshold, it is marked as a conflict area and the segment assembly angle or contact network suspension height is automatically adjusted; S2.4. Based on the corrected segment assembly angles and topology verification results, the spatial arrangement sequence of the shield segment family is reconstructed through the Dynamo script to generate a tunnel section BIM model that meets the design specifications.

[0045] The tunnel longitudinal slope data of the present invention is divided into equally spaced segments according to the preset mileage intervals, and the length of each segment can be set to 10 meters. Taking the slope at the starting point of the segment as the benchmark, the quadratic term coefficient is determined according to the product of the curvature radius and the slope change rate within the segment. For example, when the curvature radius is 500 meters and the slope change rate is 0.5‰ / meter, the quadratic term coefficient is 0.5×10 -3 ×500=0.25. The segment assembly angle correction is calculated by substituting the slope value of the current mileage. For example, when the slope is 3‰, the correction angle is 0.25×(3) 2 = 2.25°. You can use the Dynamo plug-in for Revit to construct a piecewise function and implement parameter calculations through Python scripts. The rotation parameter for the segment assembly angle is mapped to the "Rotation Angle" property field of the shield segment family, driving dynamic adjustment of the segment instance.

[0046] Based on the geometric features of the tunnel cross-section BIM model, this method extracts the coordinates of key points on the outer contour of the segment, such as the crown, haunch, and foot. The minimum spatial distance threshold between the catenary suspension point and the pipeline centerline can be set to 0.5 meters, with the real-time distance calculated using Dynamo's geometry engine. When the distance is detected to be less than the threshold, for example, if the distance between a segment's crown and the catenary suspension point is 0.4 meters, the adjustment logic is automatically triggered: if the slope allows adjustment, the segment assembly angle is prioritized (in 1° steps); otherwise, the catenary suspension height is adjusted (in 0.1-meter steps). Conflict area marker information is stored in a SQLite database and synchronized to the model view via the Revit API, where it is highlighted in red.

[0047] Based on the corrected segment assembly angles and clash detection results, this method uses a Dynamo script to reconstruct the spatial arrangement sequence of the shield segment family. For example, in the section from K1+020 to K1+030, the segment rotation angle is adjusted from 2.25° to 3.5°. The segment center axis coordinates are then regenerated and rechecked against the coordinates of the catenary suspension points. The reconstructed model is exported in IFC format and verified for compliance with construction specifications (such as Section 7.3.2 of the "Metro Design Code" GB 50157). Clash checking can be performed in Navisworks to ensure that the corrected model is free of interference.

[0048] This invention utilizes piecewise nonlinear functions and real-time topological conflict detection to precisely match segment assembly angles with the tunnel's longitudinal slope, reducing manual calculation errors. A dynamic adjustment mechanism resolves spatial interference issues between segments, the catenary, and pipelines during the model generation phase, avoiding structural conflicts during later construction. The reconstructed tunnel cross-section model meets design specifications and can be directly used for shield construction guidance and quantity accounting.

[0049] In another technical solution, the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, in step S3 of "associating the rail bottom slope angle with the line superelevation value to dynamically generate a track base BIM model" includes: S3.1. Divide the combination of the longitudinal slope and curve radius of the line into weight calculation units at preset mileage intervals, with each unit length being 50 meters. Perform the following operations: Based on the proportional relationship between the absolute value of the line slope gradient and the inverse of the curve radius, the weight value is dynamically assigned. The weight value calculation formula is: Weight value = 0.6 × |slope gradient| + 0.4 × (1 / curve radius) The unit of slope gradient is ‰, and the unit of curve radius is meter. When the train design speed is ≥80 km / h, the adjustment weight coefficient is 0.7:0.3; S3.2. Establish the dynamic association logic between the rail bottom slope angle and the line superelevation value in Dynamo, including: Input the weight value of the current mileage into the linear interpolation function, map it to the rail bottom slope angle range (1:40 to 1:20), and output the actual correction angle; If the line section contains a composite curve, the weight value is Gaussian smoothed and re-interpolated; S3.3. Based on historical track base layout data and construction constraints, train a random forest regression model to perform the following operations: Input features include rail bottom slope correction angle, line superelevation value, geological parameters and track base spacing; The output is the optimal horizontal offset of the track base arrangement scheme; Call the model interface through Dynamo to drive the dynamic adjustment of the spatial positioning parameters of the track base family; S3.4. Based on the corrected rail bottom slope angle and the optimized layout plan, verify the minimum clearance between the track base and the tunnel structure. If there is a conflict, redistribute the weight coefficients and perform iterative calculations to ultimately generate a non-interference track base BIM model.

[0050] The longitudinal slope and curve radius of the line of the present invention are divided into weight calculation units according to preset mileage intervals, and the length of each unit can be set to 50 meters. The absolute value coefficient of the slope gradient in the weight value calculation formula is 0.6, and the reciprocal coefficient of the curve radius is 0.4. When the train design speed is ≥80 km / h, the adjustment coefficient is 0.7:0.3. For example, when the slope gradient of a section is 4‰ and the curve radius is 800 meters, the weight value is 0.6×4+0.4×(1 / 800)=2.4+0.0005≈2.4005. The rail bottom slope angle range can be set to 1:40 to 1:20, and the weight value is mapped to this range through a linear interpolation function. For example, a weight value of 2.4 corresponds to an angle of 1:25. For sections containing composite curves, a Gaussian smoothing filter can be used to process the weight value. The window width can be set to 3 mileage points, and the standard deviation σ=1.0.

[0051] The input features of the random forest regression model of the present invention include the rail bottom slope correction angle (e.g., 1:25), the line superelevation value (e.g., 120 mm), geological parameters (e.g., geotechnical elastic modulus 30 MPa), and the track base spacing (e.g., 0.8 meters). The optimization range of the model output horizontal offset can be set to ±50 mm, for example, the predicted offset is +35 mm. The model interface is called by Dynamo to drive the dynamic adjustment of the "horizontal positioning" parameters of the track base family. The minimum clearance threshold between the track base and the tunnel structure can be set to 0.3 meters. When insufficient clearance is detected, the weight coefficients are reallocated and iterative calculations are performed. For example, the slope gradient coefficient is reduced to 0.55, the curve radius coefficient is increased to 0.45, and the layout plan is regenerated.

[0052] The track base of the present invention can be made of Q345B steel, and the spacing between the embedded bolt holes can be set to 0.5 meters. The base is installed on the concrete embedded parts of the tunnel floor, and the horizontal offset is fixed by locating pins and bolts. The corrected track base BIM model is exported in IFC format and associated with the coordinate positioning table in the construction drawings. After the model is generated, the base stress distribution can be verified by finite element analysis software, and the maximum stress threshold is set to 235MPa to ensure that the material strength requirements are met.

[0053] This invention uses dynamic weight allocation and machine learning optimization to precisely match the rail base slope angle with the line superelevation value, improving the scientific nature of the track foundation layout plan. A conflict verification mechanism effectively mitigates the risk of interference between the foundation and the tunnel structure, ensuring construction feasibility. The model output can be directly used for foundation prefabrication and on-site installation, reducing the workload of construction adjustments.

[0054] In another technical solution, the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, in step S4, "associating the overhead contact network suspension height with the track line slope and line superelevation value to dynamically generate an overhead contact network BIM model" includes: S4.1. Based on the track slope and superelevation at the current mileage, calculate the theoretical superelevation value using the following formula: in, is the reference height specified in the design specification, S is the slope gradient, and C is the superelevation value. 、 is the dynamic correction factor related to train speed; S4.2. Use Dynamo to analyze the theoretical values ​​of the conductance height at consecutive mileage points in real time and calculate the conductance height change rate: If |ΔH| exceeds the allowed threshold of the specification, it is marked as an abnormal section; S4.3. Perform the following operations on the abnormal segment: Extract the theoretical height-guided values ​​of the five mileage points before and after, and use sliding window mean filtering to generate the corrected height-guided values; If the limit is still exceeded after correction, the current elevation parameter is overwritten based on the trend prediction value of the adjacent section slope and superelevation value; S4.4. Map the corrected conductor height parameters to the suspension point height of the contact network support family, drive the support family instantiation through the Dynamo script, and generate the contact network BIM model.

[0055] The present invention calculates the theoretical superelevation value based on the track slope and superelevation at the current mileage using a formula that references the baseline superelevation value (e.g., 5.0 meters) specified in the "Metro Design Code" (GB 50157). The slope gradient S range is ±5‰, and the superelevation value C range is 0-150mm. Dynamic correction coefficients α and β can be set based on train speed. For example, when speed is ≤60 km / h, α = 0.02 and β = 0.015; when speed is >60 km / h, α = 0.03 and β = 0.02. The Dynamo plug-in for Revit can be used to analyze design data, and formula calculations can be implemented using Python scripts. Superelevation sensor data can be connected to a PLC control system for real-time updates of mileage point parameters.

[0056] The formula for calculating the lead height change rate ΔH is the difference in lead height between adjacent mileage points divided by the spacing (e.g., 10 meters), with a tolerance threshold set to ±10 mm / m. When an abnormal section is detected (e.g., ΔH = 12 mm / m), the theoretical lead height values ​​for the five preceding and following mileage points are extracted and a sliding window mean filter (with a window width of 3 points) is used to generate a corrected value. If the corrected value still exceeds the limit—for example, if the lead height value in a section is 5.2 meters while the standard allows a maximum of 5.15 meters—then the current parameter is overwritten with a trend prediction based on the slope and superelevation values ​​of adjacent sections. A linear regression algorithm can be used as the prediction model. The corrected lead height parameter is mapped to the suspension point height attribute field of the catenary support family using a Dynamo script.

[0057] The catenary support of this invention can be manufactured from Q235B steel. The suspension points are mounted on embedded components in the tunnel vault, and the bolt hole spacing can be set to 0.6 meters. After the model is generated, the minimum safe distance between the suspension points and the outer contour of the segment is verified using Dynamo's geometry engine (threshold 0.3 meters). If the distance is insufficient, the height guide or segment angle is automatically adjusted. The corrected catenary BIM model is exported in IFC format and linked to construction survey data (such as total station coordinates) for field compatibility verification.

[0058] This invention ensures that the overhead catenary suspension height meets design specifications through dynamic height calculation and real-time verification of its rate of change, preventing abnormal overhead catenary tension caused by sudden height changes. A sliding window filter and trend prediction algorithm for abnormal sections improves the efficiency of height parameter correction and reduces manual intervention. A conflict prediction mechanism in the model generation phase reduces the risk of interference between the overhead catenary and tunnel structure during construction, improving project reliability.

[0059] In another technical solution, the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, in step S5, "optimizing the pipeline layout plan and dynamically generating a conflict-free pipeline BIM model" includes: S5.1. Based on the 3D tunnel cross-section model, divide the space into cubic grid cells with a side length of 0.5 meters and perform the following operations: Label the attributes of each grid cell, including the structure type (segment, track base, catenary support) and the allowed passage status; The pipeline path planning problem is transformed into an optimization problem of a grid cell sequence, and the state space is defined as a coordinate set of traversable grids. S5.2. Train the Deep Q Network (DQN) model and define the following parameters: The direction of the pipeline path in the grid cell (front / back / left / right / up / down); Path length weight (-0.3 × length), collision penalty (-1.0 × number of collisions), turn radius compliance bonus (+0.5 × number of compliant turns); Call the model interface through Dynamo to output the optimal path control point sequence; S5.3. Construct a turning radius constraint matrix based on pipeline type and diameter parameters, including: Define pipe diameter D and minimum turning radius R min The mapping relationship satisfies R min =3D (straight pipe) or R min =5D(elbow); In the path control point sequence, if the angle between adjacent line segments is greater than 30°, check whether the actual turning radius meets the matrix constraints. Otherwise, insert a transition arc control point. S5.4. Map the optimized path control point sequence to the centerline parameters of the pipeline family, drive the pipeline family instantiation through the Dynamo script, and generate a collision-free pipeline BIM model.

[0060] The present invention is based on a three-dimensional model of a tunnel section. The spatial grid can be divided into cubic units with a side length of 0.5 meters. Each unit is labeled with the structural type (such as a segment, a track base, or a contact network support) and the allowed passage state. The pipeline path planning problem is transformed into an optimization problem of a grid unit sequence, and the state space is defined as a coordinate set of passable grids. For example, the grid on the inner wall of a segment is marked as "non-passable", and the grid on the side of the track base is marked as "restricted height passage". The Dynamo plug-in for Revit can be used to implement grid division, and the grid attribute data can be stored in a SQLite database. The starting point and end point of the pipeline path can be set at the pre-buried interface between the tunnel entrance and the equipment room, and the horizontal spacing threshold is set to 0.3 meters.

[0061] The input action space of the Deep Q Network (DQN) model of this invention includes pipeline extension directions (forward / backward / left / right / up / down), a path length weight of -0.3 × length, a collision penalty of -1.0 × number of collisions, and a turn radius compliance reward of +0.5 × number of compliant turns. Training data can be based on a library of historical construction cases (e.g., 100 sets of subway pipeline layout plans), with batch training iterations set to 5000 and a learning rate of 0.001. Model training can be implemented using the Python TensorFlow framework, and the path control point sequence can be output through the Dynamo model interface. After path optimization, the control point coordinate error threshold is set to ±0.1 meters.

[0062] The present invention defines the minimum turning radius of a straight pipe as Rmin=3D (D is the pipe diameter) and a curved pipe as Rmin=5D based on the pipeline type and pipe diameter parameters. For example, the minimum turning radius of a DN200 straight pipe is 600 mm. In the path control point sequence, if the angle between adjacent line segments is greater than 30°, it is necessary to verify whether the actual turning radius meets the constraint. For example, when the angle is 45°, a transition arc control point is inserted, the arc radius is set to 800 mm, and the control point spacing is set to 0.2 meters. The pipeline can be made of PVC or galvanized steel pipe materials, and the bracket installation position is located on the embedded parts of the tunnel side wall, and the bolt hole spacing is set to 1.0 meter. The corrected pipeline BIM model is exported in IFC format and associated with the elevation parameter table in the construction drawing for on-site adaptability verification.

[0063] This invention uses spatial meshing and a deep Q-network optimization algorithm to improve the efficiency and accuracy of pipeline path planning and reduce the number of collision detection iterations. A turning radius constraint matrix ensures that pipeline layout complies with engineering specifications and avoids installation difficulties caused by insufficient turning radius. The dynamically generated pipeline model can be directly used for prefabrication and on-site construction positioning, reducing construction adjustment costs and improving the reliability and maintenance ease of the pipeline system.

[0064] In another technical solution, the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, in step S6, "integrating the BIM models of the tunnel section, track base, catenary, and pipelines" includes: S6.1. Based on the line centerline coordinate data, establish a model integration benchmark in the global coordinate system in Revit and perform the following operations: Align the central axis of the tunnel section BIM model with the line centerline coordinates, with a deviation threshold of ≤2mm; The spatial positioning parameters of the track base, catenary support, and pipelines are analyzed using Dynamo scripts, and their origins are bound to the normal plane coordinates of the line centerline. S6.2. Build an interface matching rule library based on the geometric feature parameters of each BIM model, specifically including: Coordinate offset mapping relationship between track base anchor bolt hole positions and tunnel embedded parts; Vertical spacing constraints between the overhead contact suspension point and the tunnel vault hoisting hole; The horizontal clearance threshold between the pipeline support and the track base; S6.3. Verify the consistency of the integrated model space coordinates in real time. If the interface matching error is greater than 3mm, perform the following operations: Extract the coordinate data of the conflicting components and infer the local coordinate system correction value based on the curvature radius of the line centerline; Use Dynamo to fine-tune the translation and rotation parameters of the conflicting components until the error threshold is met; S6.4. Export the revised multi-disciplinary BIM model into independent Revit files according to the route mileage, and generate a model positioning index table in the global coordinate system.

[0065] Based on the line centerline coordinate data, the present invention establishes a model integration benchmark in the global coordinate system in Revit. The alignment deviation threshold between the central axis of the tunnel section BIM model and the line centerline can be set to 2mm. The spatial positioning parameters of the track base, contact network support and pipeline are parsed through Dynamo scripts, and their origins can be bound to the normal plane coordinates of the line centerline. For example, the three-dimensional coordinates of the track base anchor hole are calculated and generated by the projection of the line centerline normal plane, and the projection error threshold is set to ±1mm. The "shared coordinate system" function of Revit can be used to achieve global coordinate unification, and the coordinate data can be written to the SQLite database through the ODBC interface.

[0066] The coordinate offset mapping relationship between the rail base anchor hole position and the tunnel embedded part of the present invention can be set to a horizontal offset of ±5mm and a vertical offset of ±3mm. The vertical spacing constraint between the contact network suspension point and the tunnel vault hoisting hole can be set to 0.5 meters, and the lateral clearance threshold between the pipeline support and the rail base can be set to 0.3 meters. When the interface matching error is detected to be greater than 3mm, the local coordinate system correction amount is inferred based on the curvature radius of the line centerline. For example, when the curvature radius is 500 meters, the correction amount ΔX=0.1mm, ΔY=0.05mm. The translation and rotation parameters of the conflicting components are fine-tuned by Dynamo, and the step size can be set to 0.1mm / time until the error is ≤3mm.

[0067] The modified multi-disciplinary BIM model is exported as independent Revit files by segment, categorized by route length, with segment lengths set to 100 meters. The model's global coordinate system index table, containing the start and end points, centerline coordinates, and file link paths, is stored in an Excel spreadsheet or SQLite database. Revit's "staged model export" feature can be used for batch processing, and construction survey control points (such as CPIII points) can be linked to verify the compatibility of field coordinates. The exported IFC files are then clash-checked in Navisworks to ensure there are no unaddressed interference areas.

[0068] This invention achieves high-precision integration of multi-disciplinary models through global coordinate system alignment and interface rule base verification, with an interface matching error of ≤3mm, avoiding physical interface misalignment during construction. A conflict correction mechanism dynamically adjusts the local coordinate system, reducing the need for manual intervention. A standardized export process and positioning index table support rapid model access and data tracing during the construction phase, improving engineering collaboration efficiency.

[0069] In another technical solution, the method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, the "parameter linkage update mechanism" in step S7 includes: S7.1. Analyze the function call chain and input-output parameter mapping relationship in the Dynamo script to generate a directed graph structure, where: Nodes represent model component parameter variables, including tunnel section dimensions, track base offset, contact network height values, and pipeline path control points; Edges represent functional dependencies between parameters, including direct computational associations and indirect logical constraints; S7.2. Real-time monitoring of changes in the values ​​of input parameters, including: The parameter value is hashed using the SHA-256 algorithm. If the current digest is inconsistent with the historical version, it is marked as a changed parameter. According to the dependency graph, traverse all downstream associated nodes of the parameter node to generate a set of parameters to be updated; S7.3. Trigger update instructions sequentially from the bottom to the top of the dependency hierarchy, specifically including: Directly update independent parameters without dependencies; For node groups with circular dependencies, a topological sorting algorithm is used to decompose them into acyclic subgraphs and then update them in order; S7.4. Perform the following checks on the updated model components: Extract the minimum bounding box of the component geometry and calculate its interference volume with the associated components; If the interference volume is greater than the tolerance threshold, go back to the last valid parameter version and freeze the current parameter change instruction; S7.5. Write the numerical differences before and after the parameter change, and the types and quantities of affected components into a structured log file in the format of a JSON array.

[0070] The function call chain and input-output parameter mapping in the Dynamo script of the present invention can generate a directed graph structure, with nodes including tunnel cross-sectional dimensions (e.g., 6.2-meter diameter), track base offset (±50 mm), catenary height values ​​(5.0-5.5 meters), and pipeline path control point coordinates. Edges represent direct computational relationships between parameters (e.g., height values ​​are dependent on line slope) or indirect logical constraints (e.g., pipeline path affects track base position). The hash digest uses the SHA-256 algorithm, and the parameter value change monitoring threshold is set to a 1-byte difference. For example, a parameter change from 120 mm to 121 mm triggers a change flag. A Neo4j graph database can be used to store the dependency graph, and parameter status can be synchronized in real time via the Revit API.

[0071] The present invention triggers update instructions from the bottom to the top of the dependency hierarchy, for example, updating the track base offset first, followed by the catenary height value. Circularly dependent node groups (e.g., A depends on B, B depends on C, C depends on A) are decomposed into independent subgraphs through topological sorting, and the update order is arranged according to the node in-degree priority. When verifying interference volumes, the minimum bounding box of the component geometry is calculated using the Axially Aligned Bounding Box (AABB) algorithm, with an interference volume tolerance threshold of 0.001 cubic meters. If an interference exceeding the limit (e.g., 0.0015 cubic meters) is detected, the system automatically rolls back to the last valid parameter version and freezes the current change instructions. The Parasolid geometry kernel can be used to perform bounding box calculations.

[0072] The parameter change log of this invention is recorded in JSON array format, including the change timestamp, parameter name (e.g., "track base offset"), old value (+35mm), new value (+40mm), affected component type (e.g., track base family instance ID), and number of instances (e.g., 12). The log files can be stored in a MongoDB database, and a time range query interface supports issue tracing. For example, if an abnormal track base offset is discovered in a certain section during construction, the log can be used to locate the specific parameter change record and the associated model version.

[0073] This invention uses dependency graphs and topological sorting algorithms to automatically propagate parameter changes and avoid logical conflicts, reducing errors caused by manual intervention. Interference volume verification and rollback mechanisms ensure the security of model updates and prevent the spread of incorrect parameters. Structured logging supports full lifecycle problem tracing and version management, improving model maintenance efficiency and engineering data reliability.

[0074] The present invention provides a method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, which comprises the following steps: Step 1: Establishment and dynamic correction of 3D spatial control datum The Dynamo plug-in for Revit is used to analyze rail line design data, including line slope (range: ±5‰), line superelevation (0-150mm), and 2D coordinates. The global coordinate system translation (e.g., ΔX = 120.5m, ΔY = 85.3m) is calculated based on the coordinate difference between the line's origin and destination points. The local coordinate system rotation angle is determined based on the angle between the line's orientation and the global X-axis (e.g., 15°). A scaling factor is generated using a 1:1 ratio. If the curvature radius of a segment is detected to be less than a preset threshold (300 meters), the curvature data of the adjacent 50-meter segment is extracted and the coordinates are corrected using a cubic spline interpolation algorithm (with a node spacing of 10 meters). If the slope gradient exceeds the limit (e.g., 6‰), the elevation is corrected using the weighted average of the slopes of the preceding and following segments (weighting 0.6:0.4). The corrected 3D datum data is stored in a SQLite database and synchronized with the geometry feature library via the Revit API. Step 2: Dynamic generation of tunnel section BIM model The shield segment parameters (6.2m diameter, 0.35m thickness) were input, and the tunnel longitudinal slope data was segmented at 10-meter intervals to construct a piecewise nonlinear function. For example, with a curvature radius of 500m and a slope change rate of 0.5‰ / m, the quadratic term coefficient is 0.25, and the segment assembly angle correction corresponding to a slope of 3‰ at the current mileage is 2.25°. Dynamo uses real-time correlation with slope sensor data to drive dynamic adjustment of the segment family's rotation parameters. Based on the topological relationship matrix, the minimum distance between the segment's outer contour and the catenary suspension point and pipeline centerline is verified (threshold 0.5m). If a conflict occurs, the segment angle (in steps of 1°) or the lead-up value (in steps of 0.1m) is automatically adjusted. The corrected model is exported in IFC format.

[0075] Step 3: Track base layout optimization and model generation The longitudinal slope and curve radius of the line are divided into weighted units at 50-meter intervals. The weighting formula is 0.6 × |slope gradient| + 0.4 × (1 / curve radius). For example, for a slope of 4‰ and a curve radius of 800 meters, the weight is 2.4005. The weights are mapped to the track base slope angle range (1:40 to 1:20) through linear interpolation. Gaussian smoothing (window width 3 points, σ ​​= 1.0) is applied to compound curve sections. A random forest regression model inputs the track base slope angle, superelevation value, pedestal spacing (0.6-1.2 meters), and geological parameters (such as an elastic modulus of 30 MPa) to output an optimized horizontal offset value (±50 mm). The clear distance between the pedestal and the tunnel structure is verified (threshold 0.3 meters). If conflicts arise, the weighting coefficients are iteratively adjusted. The final BIM model of the track base is generated using Q345B steel, with embedded bolt holes spaced 0.5 meters apart.

[0076] Step 4: Dynamic calculation and model generation of catenary height The theoretical elevation gain is calculated based on the current mileage slope and superelevation value using the formula H = H0 + α × S + β × C (H0 = 5.0 meters, α = 0.03, β = 0.02). The elevation gain values ​​for consecutive mileages are analyzed in real time, and the rate of change is calculated (threshold ±10 mm / m). A sliding window mean filter (window width 3 points) is used to correct abnormal sections. If the limit is still exceeded, the coverage parameters are predicted based on the trend of adjacent sections. The overhead line support (Q235B steel) is installed at the embedded parts of the tunnel vault (bolt hole spacing is 0.6 meters). The elevation gain parameters are mapped to the support family height attribute. After the model is generated, the minimum safe distance from the segment (0.3 meters) is verified. Step 5: Pipeline path planning and conflict-free model generation The tunnel space was divided into a 0.5-meter cube grid, labeled with structure type (segment, track base) and travel status. A Deep Q-Network (DQN) defined the path extension direction (forward / backward / left / right / up / down). Reward parameters included path length (-0.3 × length), collision penalty (-1.0 × number of turns), and legal turns (+0.5 × number of turns). The optimized path control point sequence must meet turning radius constraints (R_min = 3D for straight pipes and R_min = 5D for curved pipes). Transition arcs (control point spacing of 0.2 meters) were inserted when the angle between adjacent line segments exceeded 30°. Pipeline (PVC or galvanized steel pipe) brackets were installed on pre-embedded parts in the tunnel sidewalls (bolt spacing of 1.0 meters). After the model was generated, collisions were checked in Navisworks.

[0077] Step 6: Multi-disciplinary model integration and interface matching Based on the line centerline coordinates, the tunnel section, track base, catenary, and pipeline models were aligned to the global coordinate system in Revit (with a deviation of ≤2mm). An interface rule library was established: track base anchor hole positions were offset from embedded components (horizontally ±5mm, vertically ±3mm); the distance between the catenary suspension point and the lifting hole was 0.5m; and the clearance between the pipeline support and the track base was 0.3m. When an interface error >3mm was detected, a local correction (e.g., ΔX = 0.1mm) was inferred based on the curvature radius. Component positions were fine-tuned using Dynamo (in steps of 0.1mm) until the error was within the specified range. The corrected model was exported as a separate Revit file in 100-meter segments, and a global positioning index table was generated.

[0078] Step 7: Parameter linkage update and log management A parameter dependency graph is constructed (e.g., guide height dependence on slope, pipeline path dependence on tunnel cross-section). Parameter changes are monitored using the SHA-256 hash algorithm (with a 1-byte difference threshold). Dependent nodes are updated by topological sorting, and cyclic dependency groups are broken down into acyclic subgraphs. Interference volume verification (AABB algorithm, with a tolerance of 0.001 m³) rolls back to the previous version if the limit is exceeded. A change log records parameter names, old and new values, and affected components in JSON format, and is stored in MongoDB, supporting timestamp tracing. Step 8: Construction adaptation and output Output an integrated BIM model (RVT / IFC format) with LOD400 accuracy, linking construction survey data (such as total station CPIII measurement points) to enable real-time deviation correction. A bill of quantities (such as segment count and steel usage) is automatically generated, and critical node stresses (threshold of 235 MPa) are verified through finite element analysis. The resulting model supports construction guidance for shield tunneling, track laying, and pipeline installation, reducing on-site adjustments.

[0079] Through parametric modeling and dynamic verification, this invention enables the automated generation and high-precision integration of multi-disciplinary models for subway sections. Interface matching errors are ≤3mm, and conflict prediction reduces later rework by over 80%. A parameter linkage update mechanism ensures real-time response to design changes, and a structured log supports full-cycle problem tracing. The LOD400 precision model is directly used for construction layout and quantity statistics, improving project collaboration efficiency and construction reliability.

[0080] The present invention significantly improves the efficiency, accuracy and coordination ability of subway section modeling by constructing a parametric linear digital model system based on Revit and Dynamo. The establishment of a global three-dimensional spatial control benchmark combined with piecewise cubic spline interpolation and dynamic correction algorithms effectively suppresses the coordinate accumulation errors caused by sudden changes in curvature radius and slope exceeding the limit, providing unified spatial data support for multi-disciplinary models. Through parametric logical association, real-time dynamic matching of track slope, superelevation value and segment assembly angle, rail bottom slope correction, contact network height guide and pipeline path is achieved. Combined with machine learning model optimization, topological relationship matrix verification and deep Q network path planning, pipeline collision, height guide exceeding the limit and structural interference risks are predicted and avoided in the model generation stage, reducing the amount of rework in the later stage. The integration of multi-disciplinary models ensures that the spatial alignment accuracy error of tunnel section, track base, contact network and pipeline models is ≤3mm through the interface matching rule library and local coordinate system correction algorithm, ensuring the physical adaptability of the construction interface. The parameter linkage update mechanism, based on dependency graphs and a topological sorting algorithm, enables automated propagation of parameter changes and logical conflict interception. Combined with hash summaries and interference volume verification, it ensures the consistency and reliability of model updates. The resulting LOD400 BIM model supports dynamic revision of construction measurement data and automatic generation of bills of quantities, providing a high-precision digital foundation for subway section design optimization, construction guidance, and operations and maintenance management.

[0081] The number of devices and processing scales described herein are intended to simplify the description of the present invention. Applications, modifications, and variations of the present invention will be readily apparent to those skilled in the art.

[0082] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to specific details.

Claims

1. A method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo, characterized in that: The following steps are involved: S1. Call Dynamo to analyze the track design data and establish a three-dimensional spatial control benchmark. The design data includes the track slope and superelevation value. S2. Input shield segment parameters, associate segment assembly angles with tunnel longitudinal slopes through Dynamo, and dynamically generate a tunnel section BIM model based on the topological relationship matrix to verify relative positions with the catenary and pipelines. S3. Input track base parameters, associate them with the rail bottom slope angle and line superelevation value through Dynamo, and optimize the track base layout plan based on the machine learning model to dynamically generate the track base BIM model. S4. Input the catenary support parameters, use Dynamo to correlate the catenary suspension height with the track slope and superelevation value, and verify the suspension height change rate in real time to dynamically generate a catenary BIM model that meets industry design specifications. S5. Input comprehensive pipeline parameters, optimize pipeline layout through spatial meshing algorithm and reinforcement learning model, and dynamically generate conflict-free pipeline BIM model based on turning radius-pipe diameter matching matrix; S6. Based on the line centerline coordinates, the tunnel section, track foundation, catenary, and pipeline BIM models are integrated in Revit. The multi-disciplinary model collaboration system ensures the uniformity of spatial coordinates and an interface matching accuracy error of ≤3mm. S7. When any parameter changes, the BIM model involved is automatically adjusted through the parameter linkage update mechanism; S8, output BIM model with LOD400 accuracy, associate construction measurement data to correct model deviation in real time, support RVT, IFC format and automatic generation of bill of quantities; Among them, steps S2 to S5 are all based on the three-dimensional space control benchmark established in step S1.

2. The method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo according to claim 1, characterized in that: The step S1 of "establishing a three-dimensional space control benchmark" includes: S1.

1. Based on the two-dimensional plane coordinates and elevation data in the track line design data, generate the three-dimensional reference data of the global engineering coordinate system according to the following rules: Calculate the translation amount in the global coordinate system based on the plane coordinate difference between the starting and ending points of the line; Based on the angle between the line direction and the X-axis of the global coordinate system, the rotation angle of the local coordinate system is dynamically determined; Generate the scaling factor from local coordinates to global coordinates based on the unit conversion relationship between the design scale and the global coordinate system; S1.

2. Based on the 3D reference data generated in S1.1, perform the following verification and correction operations: If the curvature radius of a certain segment is less than the preset threshold, the curvature data of the adjacent segments are extracted, the corrected curvature radius is generated through the interpolation algorithm, and the corresponding coordinates are recalculated; If the slope gradient exceeds the allowable range of the specification, a corrected slope is generated based on the weighted average of the slope values ​​of adjacent sections, and the elevation coordinates are updated in reverse order; S1.

3. When the line design parameters are changed, perform the following operations: Based on the translation, rotation, and scaling parameters of S1.1 and the corrected data of S1.2, the three-dimensional coordinates of the affected segments are regenerated using the piecewise cubic spline interpolation algorithm; The updated coordinates are synchronized to the standardized model geometry database, triggering the parametric reconstruction of the associated model.

3. The method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo according to claim 1, characterized in that: In step S2, "associating segment assembly angles with tunnel longitudinal slopes to dynamically generate a tunnel section BIM model" includes: S2.

1. Divide the tunnel longitudinal slope data into equally spaced segments at preset mileage intervals, with each segment being 10 meters long. Perform the following operations within each segment: Taking the slope of the starting point of the segment as the benchmark, the quadratic term coefficient is determined according to the product of the curvature radius and the slope change rate in the segment to construct a piecewise nonlinear function; Substitute the slope value corresponding to the current mileage into the piecewise function and output the segment assembly angle correction value; S2.

2. Establish real-time correlation logic between segment assembly angles and tunnel longitudinal slope in Dynamo, including: Analyze the tunnel longitudinal slope sensor data and match it with the piecewise function corresponding to the current mileage; Map the assembly angle correction value output by the function to the rotation parameter of the shield segment family to drive the dynamic adjustment of the segment angle; S2.

3. Based on the geometric features of the tunnel section BIM model, construct a topological relationship matrix containing the relative positions of the catenary supports, pipelines, and segments. Perform the following operations: Extract the coordinates of key points on the outer contour of the segment and calculate the minimum spatial distance between them and the contact network suspension point and the pipeline centerline; If the distance is less than the preset safety threshold, it is marked as a conflict area and the segment assembly angle or contact network suspension height is automatically adjusted; S2.

4. Based on the corrected segment assembly angles and topology verification results, the spatial arrangement sequence of the shield segment family is reconstructed through the Dynamo script to generate a tunnel section BIM model that meets the design specifications.

4. The method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo according to claim 1, characterized in that: In step S3, "associate the rail bottom slope angle with the line superelevation value and dynamically generate the track base BIM model" includes: S3.

1. Divide the combination of the longitudinal slope and curve radius of the line into weight calculation units at preset mileage intervals, with each unit length being 50 meters. Perform the following operations: Based on the proportional relationship between the absolute value of the line slope gradient and the inverse of the curve radius, the weight value is dynamically assigned. The weight value calculation formula is: Weight value = 0.6 × |slope gradient| + 0.4 × (1 / curve radius) The unit of slope gradient is ‰, and the unit of curve radius is meter. When the train design speed is ≥80 km / h, the adjustment weight coefficient is 0.7:0.3; S3.

2. Establish the dynamic association logic between the rail bottom slope angle and the line superelevation value in Dynamo, including: Input the weight value of the current mileage into the linear interpolation function, map it to the rail bottom slope angle range (1:40 to 1:20), and output the actual correction angle; If the line section contains a composite curve, the weight value is Gaussian smoothed and re-interpolated; S3.

3. Based on historical track base layout data and construction constraints, train a random forest regression model to perform the following operations: Input features include rail bottom slope correction angle, line superelevation value, geological parameters and track base spacing; The output is the optimal horizontal offset of the track base arrangement scheme; Call the model interface through Dynamo to drive the dynamic adjustment of the spatial positioning parameters of the track base family; S3.

4. Based on the corrected rail bottom slope angle and the optimized layout plan, verify the minimum clearance between the track base and the tunnel structure. If there is a conflict, redistribute the weight coefficients and perform iterative calculations to ultimately generate a non-interference track base BIM model.

5. The method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo according to claim 1, characterized in that: In step S4, "associating the overhead contact network suspension height with the track line slope and line superelevation value to dynamically generate an overhead contact network BIM model" includes: S4.

1. Based on the track slope and superelevation at the current mileage, calculate the theoretical superelevation value using the following formula: in, is the reference height specified in the design specification, S is the slope gradient, and C is the superelevation value. 、 is the dynamic correction factor related to train speed; S4.

2. Use Dynamo to analyze the theoretical values ​​of the conductance height at consecutive mileage points in real time and calculate the conductance height change rate: If |ΔH| exceeds the allowed threshold of the specification, it is marked as an abnormal section; S4.

3. Perform the following operations on the abnormal segment: Extract the theoretical height-guided values ​​of the five mileage points before and after, and use sliding window mean filtering to generate the corrected height-guided values; If the limit is still exceeded after correction, the current elevation parameter is overwritten based on the trend prediction value of the adjacent section slope and superelevation value; S4.

4. Map the corrected conductor height parameters to the suspension point height of the contact network support family, drive the support family instantiation through the Dynamo script, and generate the contact network BIM model.

6. The method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo according to claim 1, characterized in that: In step S5, "optimizing the pipeline layout plan and dynamically generating a conflict-free pipeline BIM model" includes: S5.

1. Based on the 3D tunnel cross-section model, divide the space into cubic grid cells with a side length of 0.5 meters and perform the following operations: Label the attributes of each grid cell, including the structure type (segment, track base, catenary support) and the allowed passage status; The pipeline path planning problem is transformed into an optimization problem of a grid cell sequence, and the state space is defined as a coordinate set of traversable grids. S5.

2. Train the Deep Q Network (DQN) model and define the following parameters: The direction of the pipeline path in the grid cell (front / back / left / right / up / down); Path length weight (-0.3 × length), collision penalty (-1.0 × number of collisions), turn radius compliance bonus (+0.5 × number of compliant turns); Call the model interface through Dynamo to output the optimal path control point sequence; S5.

3. Construct a turning radius constraint matrix based on pipeline type and diameter parameters, including: Define pipe diameter D and minimum turning radius R min The mapping relationship satisfies R min =3D (straight pipe) or R min =5D(elbow); In the path control point sequence, if the angle between adjacent line segments is greater than 30°, check whether the actual turning radius meets the matrix constraints. Otherwise, insert a transition arc control point. S5.

4. Map the optimized path control point sequence to the centerline parameters of the pipeline family, drive the pipeline family instantiation through the Dynamo script, and generate a collision-free pipeline BIM model.

7. The method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo according to claim 1, characterized in that: In step S6, "integrating BIM models of tunnel sections, track foundations, contact lines, and pipelines" includes: S6.

1. Based on the line centerline coordinate data, establish a model integration benchmark in the global coordinate system in Revit and perform the following operations: Align the central axis of the tunnel section BIM model with the line centerline coordinates, with a deviation threshold of ≤2mm; The spatial positioning parameters of the track base, catenary support, and pipelines are analyzed using Dynamo scripts, and their origins are bound to the normal plane coordinates of the line centerline. S6.

2. Build an interface matching rule library based on the geometric feature parameters of each BIM model, specifically including: Coordinate offset mapping relationship between track base anchor bolt hole positions and tunnel embedded parts; Vertical spacing constraints between the overhead contact suspension point and the tunnel vault hoisting hole; The horizontal clearance threshold between the pipeline support and the track base; S6.

3. Verify the consistency of the integrated model space coordinates in real time. If the interface matching error is greater than 3mm, perform the following operations: Extract the coordinate data of the conflicting components and infer the local coordinate system correction value based on the curvature radius of the line centerline; Use Dynamo to fine-tune the translation and rotation parameters of the conflicting components until the error threshold is met; S6.

4. Export the revised multi-disciplinary BIM model into independent Revit files according to the route mileage, and generate a model positioning index table in the global coordinate system.

8. The method for constructing a parametric linear digital model of a subway section based on Revit and Dynamo according to claim 1, characterized in that: The "parameter linkage update mechanism" in step S7 includes: S7.

1. Analyze the function call chain and input-output parameter mapping relationship in the Dynamo script to generate a directed graph structure, where: Nodes represent model component parameter variables, including tunnel section dimensions, track base offset, contact network height values, and pipeline path control points; Edges represent functional dependencies between parameters, including direct computational associations and indirect logical constraints; S7.

2. Real-time monitoring of changes in the values ​​of input parameters, including: The parameter value is hashed using the SHA-256 algorithm. If the current digest is inconsistent with the historical version, it is marked as a changed parameter. According to the dependency graph, traverse all downstream associated nodes of the parameter node to generate a set of parameters to be updated; S7.

3. Trigger update instructions sequentially from the bottom to the top of the dependency hierarchy, specifically including: Directly update independent parameters without dependencies; For node groups with circular dependencies, a topological sorting algorithm is used to decompose them into acyclic subgraphs and then update them in order; S7.

4. Perform the following checks on the updated model components: Extract the minimum bounding box of the component geometry and calculate its interference volume with the associated components; If the interference volume is greater than the tolerance threshold, go back to the last valid parameter version and freeze the current parameter change instruction; S7.

5. Write the numerical differences before and after the parameter change, and the types and quantities of affected components into a structured log file in the format of a JSON array.

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