Rail construction period asset data management method driven by bim model dynamic updating

Abstract

The present application relates to the technical field of BIM modeling, and more particularly to a track construction period asset data management method driven by BIM model dynamic updating. The method comprises the following steps: acquiring track construction data and track terrain data stored in a GIS platform; performing component and terrain alignment processing of track construction based on the track construction data and the track terrain data to generate track component terrain alignment data; establishing a track construction dynamic BIM model through the track component terrain alignment data; performing construction-driven minimum maintenance unit node and attribute feature analysis based on the track construction dynamic BIM model to generate construction-driven minimum maintenance unit node-attribute feature data; and performing track construction period asset dynamic management analysis based on the construction-driven minimum maintenance unit node-attribute feature data to generate track construction period asset dynamic management data. The present application realizes BIM model establishment and intelligent dynamic management of assets during the track construction period.

Description

BIM Model Dynamic Update-Driven Asset Data Management Method for Rail Construction Phase Technical Field

[0001] This invention relates to the field of BIM modeling technology, and in particular to a method for managing asset data during the construction period of rail transit driven by dynamic updates of BIM models. Background Technology

[0002] With the continuous expansion of rail transit construction, the asset data during the construction phase is characterized by its massive quantity, complex types, and frequent dynamic changes, covering various data such as track components, terrain conditions, and construction status. Precise and dynamic management of this data has become crucial to ensuring the quality and efficiency of rail construction. As the level of intelligence in rail construction increases, BIM (Building Information Modeling) technology has been widely applied in the design and construction phases. However, existing rail construction asset data management technologies mostly construct static BIM models, making it difficult to dynamically update them according to the construction process. Furthermore, they cannot construct minimum maintenance units based on construction-driven needs and systematically analyze their link constraint states. Real-time asset management for each component involves a large amount of data, making it impossible to conduct targeted asset management for components requiring replacement during construction. This results in a disconnect between asset data and actual construction progress, insufficient management accuracy, and an inability to timely and accurately reflect the dynamic changes in assets during the rail construction phase, failing to meet the actual needs of dynamic asset management during rail construction. Summary of the Invention

[0003] Based on this, the present invention provides a method for managing asset data during the construction period of rail transit driven by dynamic updates of BIM models, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for managing railway construction phase asset data driven by dynamic updates of the BIM model includes the following steps:

[0005] Step S1: Obtain track construction data and track terrain data stored in the GIS platform; perform track construction component and terrain alignment processing based on track construction data and track terrain data to generate track component terrain alignment data;

[0006] Step S2: Establish a dynamic BIM model for track construction using terrain alignment data of track components;

[0007] Step S3: Based on the dynamic BIM model of the rail construction, perform construction-driven minimum maintenance unit node and attribute feature analysis to generate construction-driven minimum maintenance unit node-attribute feature data;

[0008] Step S4: Based on the construction-driven minimum maintenance unit node-attribute feature data, perform minimum maintenance unit link constraint state feature analysis to generate minimum maintenance unit link constraint state feature data;

[0009] Step S5: Based on the construction-driven minimum maintenance unit node-attribute characteristic data and the minimum maintenance unit link constraint state characteristic data, perform dynamic management analysis of track construction period assets to generate dynamic management data of track construction period assets.

[0010] The beneficial effects of this application are as follows: By systematically acquiring track construction data and track terrain data stored on a GIS platform, this invention constructs a complete multi-source data acquisition foundation, providing comprehensive and accurate data source support for subsequent asset data management, effectively solving the problems of fragmented data acquisition and disconnected multi-source data in existing technologies. Through spatial vector feature analysis, regional segmentation, and attribute labeling of track terrain data, it clearly outlines the spatial distribution and attribute characteristics of the track scene terrain. Simultaneously, it analyzes and performs hierarchical feature analysis on track construction components, clarifying the multi-dimensional hierarchical characteristics of track components, including their functions, structures, and engineering attributes, thus overcoming the limitations of independent component data and terrain data lacking correlation. By spatially aligning component hierarchical feature data with terrain scene feature data, it achieves precise matching between track components and terrain conditions, effectively solving the drawbacks of mismatched track component and terrain data and insufficient spatial correlation in existing technologies. Through staged analysis of track construction data, it achieves accurate simulation of the construction process of components at each construction stage, clearly presenting the construction status and evolution process of track components at different construction stages. By analyzing the dynamic process-related nodes and construction organization characteristics of components at each stage, this study uncovers the process connection patterns and organizational logic during component construction, providing crucial support for the analysis of component dynamic evolution characteristics. The constructed dynamic BIM model for rail construction can reflect the component status, terrain adaptation, and construction progress at each stage of rail construction in real time, achieving dynamic linkage between the BIM model and the entire construction process. This effectively solves the problem of existing static BIM models being disconnected from actual construction scenarios and unable to drive dynamic asset management, providing a reliable carrier for the dynamic tracking of asset data during the construction period. Based on the dynamic BIM model for rail construction, it focuses on construction-driven needs, addressing the shortcomings of not dividing scientific minimum maintenance units and failing to achieve deep binding of asset data with construction-driven needs. By conducting functional and dependency analysis on dynamic BIM models, this system constructs track construction-driven linkage units that align with actual construction practices. It clarifies the functions and relationships of each driving unit, providing a rational basis for the division of minimum maintenance units. By extracting features from maintainable and non-maintainable components and analyzing their relationships, it accurately identifies maintenance components requiring key management, avoiding blind asset management and reducing management costs. By analyzing the correlation characteristics of maintenance components and driving unit data, it clarifies the boundary characteristics of the minimum maintenance units, achieving a scientific division that ensures the division aligns with construction-driven and subsequent operation and maintenance needs. Through node and attribute feature analysis of the minimum maintenance units, it clearly outlines the attribute characteristics of each maintenance unit node and its construction-driven relationships, significantly improving the targeting and precision of asset data management. It also provides a clear unit carrier for construction-period maintenance planning and risk control, facilitating targeted management of key maintenance components and reducing waste of maintenance resources and asset control loopholes.Through a layered and progressive analytical logic, the construction status of the smallest maintenance unit node is first accurately captured, clarifying the real-time construction status of each node. Then, the constraint characteristics of the maintenance unit under construction-driven conditions are analyzed. Following this, the risk status of node construction is assessed based on the constraint characteristics. On this basis, supply adaptation evaluation and maintenance impact analysis are conducted, integrating multi-dimensional data to complete the link constraint status characteristic analysis. This process not only achieves full-dimensional control over maintenance units from nodes to links, from status to risk, and from supply to maintenance, accurately presenting the constraint relationships and impact logic between maintenance units, providing crucial link-level data support for subsequent dynamic asset management; but also, by predicting construction risks in advance and assessing supply adaptability and maintenance impact, it can effectively avoid problems such as construction stagnation, supply chain disruptions, and improper maintenance caused by link constraint imbalances, reducing asset losses and optimizing the allocation of construction and maintenance resources. Based on the node attribute characteristics of maintenance units, the basic supply requirements of track components are clearly defined. Combined with the link constraint status characteristic data of maintenance units, the supply requirements are dynamically adjusted, focusing on factors that may change assets during or after construction, such as components requiring repair or replacement. This achieves a precise match between supply requirements and actual construction, as well as link constraints, overcoming the limitations of static supply planning being disconnected from actual construction. By tracking dynamic supply requirements in real time, the entire process status of component supply is accurately grasped, ensuring that the supply process is synchronized with the construction progress. Dynamic asset management is carried out based on tracking data. This process achieves closed-loop control of assets from supply demand analysis, dynamic adjustment, real-time tracking to final management. It can timely and accurately reflect the dynamic changes in assets and precisely address the management needs of component replacement during construction. It provides the construction party with comprehensive and accurate asset management basis, facilitating optimized asset allocation, cost control, and risk mitigation. This ensures efficient and rational utilization of assets during the track construction period, fully meeting the actual needs of dynamic asset management during track construction and promoting the intelligent and refined upgrading of track construction asset management.

[0011] Therefore, the BIM model dynamic update-driven asset data management method for rail construction in this invention solves the limitation of static BIM models being unable to link with dynamic data throughout the construction process in real time by constructing a dynamic BIM model for rail construction. It achieves dynamic updates of the BIM model along with rail construction data and terrain data, effectively solving the problem of the BIM model being out of sync with the actual construction scenario and ensuring that model data remains synchronized with the actual construction progress. Furthermore, by scientifically dividing the smallest maintenance unit based on construction-driven needs and systematically analyzing the node attribute characteristics and link constraint status of the maintenance unit, it not only overcomes the shortcomings of existing management methods that lack scientific maintenance unit division and cannot conduct link constraint analysis, but also achieves deep binding of asset data with construction-driven needs, significantly improving asset data management. The system enhances the targeting and precision of asset management. Simultaneously, by integrating multi-source data from rail construction, a complete dynamic asset management process is established, effectively resolving issues such as data mismatch, omissions, fragmented management, and the inability to specifically manage replacement components. This reduces the management difficulty of massive asset data, enabling timely and accurate reflection of the dynamic changes in assets during the rail construction period. It provides precise data support for asset supply scheduling, risk prediction and control, and maintenance planning, effectively avoiding problems such as supply delays and insufficient risk prediction. Furthermore, it improves the spatial correlation of asset data, achieving full-process, dynamic, and precise management of assets during the rail construction period. This comprehensively meets the actual needs of high-quality and high-efficiency development in rail construction, providing reliable technical support for intelligent management of rail construction. Attached Figure Description

[0012] Figure 1 is a flowchart illustrating the steps of a BIM model-driven method for managing asset data during the construction period of a railway.

[0013] Figure 2 is a detailed flowchart of the implementation steps of step S3 in Figure 1;

[0014] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0016] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. Functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the associated items listed.

[0017] To achieve the above objectives, please refer to Figures 1 and 2. This invention provides a method for managing asset data during the rail construction period driven by dynamic updates of a BIM model. In an embodiment of this invention, please refer to Figure 1, which is a flowchart illustrating the steps of the method. The method includes the following steps:

[0018] To achieve the above objectives, a method for managing railway construction phase asset data driven by dynamic updates of the BIM model includes the following steps:

[0019] Step S1: Obtain track construction data and track terrain data stored in the GIS platform; perform track construction component and terrain alignment processing based on track construction data and track terrain data to generate track component terrain alignment data;

[0020] In this embodiment of the invention, track construction data and track topographic data stored on a GIS platform are acquired. The track construction data covers all relevant data at all stages of track construction, including component design data, construction procedure data, asset base data, and construction organization data. All data sources and collection times must be clearly defined, and the collection time must be synchronized with the actual construction progress. The data format is standardized to ensure that various types of data can be correlated. The track topographic data stored on the GIS platform covers topographic information along the entire track line, including topographic elevation data, topographic slope data, topographic geological data, and surrounding environmental data. The data collection range covers a fixed width area on both sides of the track centerline. The collection accuracy meets the requirements of track construction and component installation. The data coordinates are based on a unified national geodetic coordinate system to ensure the uniformity and accuracy of the topographic data. After acquiring both types of data, component and topographic alignment processing is performed. Coordinates are extracted from the component design data in the track construction data to clarify the spatial location parameters of each component. Simultaneously, the corresponding area coordinate parameters are extracted from the track topographic data on the GIS platform. A unified coordinate system is used for coordinate calibration to eliminate coordinate deviations between the two types of data. Subsequently, based on the spatial location and structural characteristics of the components, feature matching was performed with the corresponding terrain elevation, slope, and geological features to ensure that the designed location of the components closely matches the terrain conditions, and the degree of fit meets the track construction design standards. For any deviations that occur during the matching process, a fixed deviation correction method is used for adjustment. The entire correction process records deviation data and correction measures to ensure that the corrected component location is perfectly adapted to the terrain conditions, with no potential deviation risks. After alignment processing is completed, the calibrated component data, terrain data, and deviation correction records are integrated to generate track component terrain alignment data. This data achieves a precise association between track components and terrain data, clearly defining the terrain parameters and alignment basis for each component.

[0021] Step S2: Establish a dynamic BIM model for track construction using terrain alignment data of track components;

[0022] In this embodiment of the invention, a model terrain foundation layer is constructed based on terrain data from the track component terrain alignment data. This layer fully recreates the terrain, geological conditions, and surrounding environment along the track. The construction accuracy of the terrain foundation layer is consistent with the terrain data acquisition accuracy, ensuring accurate representation of terrain features. Subsequently, a model component evolution layer is constructed based on component data from the track component terrain alignment data. Each component's 3D model is constructed sequentially according to its type and installation order. The structural features and dimensional parameters of the 3D component models are completely consistent with the component design data in the track construction data. Simultaneously, corresponding terrain parameters are associated to ensure precise alignment between the component model and the terrain foundation layer, achieving integrated representation of components and terrain. Next, construction process data from the track construction data is integrated to construct a model process association layer. This layer precisely associates construction process nodes with the component models and the terrain foundation layer, clarifying the components, construction periods, and construction requirements corresponding to each process, achieving dynamic linkage between process progress and component installation. Finally, asset base data from the track construction data is integrated to construct a model asset management layer. This layer associates the asset information corresponding to each component with the component model, clarifying asset ownership, asset value, asset consumption, and other relevant information, providing model support for subsequent dynamic asset management. Finally, by combining construction organization data, a model construction organization layer is constructed, integrating relevant information such as construction personnel, construction equipment, and construction materials. This layer is precisely linked with the process association layer and component evolution layer to clarify the organizational arrangements for each construction stage, forming a dynamic BIM model for rail construction. This model can achieve subsequent dynamic updates based on construction-driven processes, supporting full-process asset data management.

[0023] Step S3: Based on the dynamic BIM model of the rail construction, perform construction-driven minimum maintenance unit node and attribute feature analysis to generate construction-driven minimum maintenance unit node-attribute feature data;

[0024] In this embodiment of the invention, the analysis and establishment of minimum maintenance units are carried out. Based on component data from the BIM model's component evolution layer, process data from the process association layer, and asset data from the asset management layer, and combined with construction-driven requirements, the boundaries of minimum maintenance units are defined, components are collected, and parameters are improved. The boundary range, component composition, asset association, and other relevant information of each minimum maintenance unit are clarified, and standardized minimum maintenance units are established. Each minimum maintenance unit is assigned a unique identifier code to ensure traceability. After establishment, each minimum maintenance unit undergoes comprehensive verification. Once verified, the data is integrated to form track minimum maintenance unit data. Subsequently, using this data as the core basis, combined with track construction-related driving data, component association data, and asset and process data, focusing on construction-driven needs, a comprehensive analysis of the nodes and attribute characteristics of minimum maintenance units is conducted. First, the structural nodes of the minimum maintenance units in the construction-driven branches are established. Based on construction-driven logic and dependencies, and combined with the component composition and association characteristics of the minimum maintenance units, structural nodes are established for each minimum maintenance unit. Node types are classified according to a fixed system, and each node is assigned a unique code and clearly defined functional positioning, clarifying the node's spatial location, timestamp, associated components, and other relevant information. Next, attribute feature analysis of the minimum maintenance unit is conducted. Based on the component composition, maintenance parameters, drive affiliation, asset information, and other relevant content of the minimum maintenance unit, various attribute features are extracted. Each attribute feature has a clear numerical value or identifier to ensure its completeness and accuracy. Finally, attribute feature mapping processing is carried out for each structural node. The extracted attribute features are accurately mapped to the corresponding nodes according to their functional positioning, eliminating data redundancy and conflicts. This ensures that the attribute features of each node are accurately matched with its function, generating construction-driven minimum maintenance unit node-attribute feature data. This data is used in track construction asset management to perform targeted asset management for maintenance units composed of maintenance components as the main component and associated with other related components.

[0025] Step S4: Based on the construction-driven minimum maintenance unit node-attribute feature data, perform minimum maintenance unit link constraint state feature analysis to generate minimum maintenance unit link constraint state feature data;

[0026] In this embodiment of the invention, a construction status characteristic analysis of the minimum maintenance unit (MPU) nodes is conducted. Based on node-attribute characteristic data, combined with relevant BIM model data and construction phase data, the operational status and construction progress of each node are monitored, construction status characteristic parameters are extracted, and node construction status characteristic data is generated. Subsequently, a construction-driven minimum maintenance unit constraint characteristic analysis is conducted. Based on node-attribute characteristic data and construction-driven related data, various constraint requirements and constraint standards imposed on the minimum maintenance unit by construction are clarified, constraint characteristic parameters are extracted, and constraint characteristic data is generated. Next, based on the constraint characteristic data and node construction status characteristic data, a construction-constrained minimum maintenance unit risk status analysis is conducted. Construction status deviations are checked against constraint standards, risk levels and mitigation measures are determined, and risk status data is generated. Finally, based on the risk status data, combined with minimum maintenance unit related data and component association data, a risk-constrained minimum maintenance unit supply adaptation assessment is conducted. Assessment dimensions and standards are clarified, adaptation-related parameters are calculated, and supply adaptation assessment data is generated. Subsequently, based on supply fit assessment data, combined with risk status data and node-attribute characteristic data, a minimum maintenance unit (MINU) maintenance impact analysis was conducted to analyze the various impacts of fit issues on maintenance work, extract impact characteristic parameters, and generate maintenance impact data. Finally, by integrating the above four types of data and focusing on the correlation logic between them, a minimum maintenance unit link constraint state characteristic analysis was conducted. Core characteristic parameters such as link coordination, stability, and fit were extracted, weak links and their impact scope were identified, and all relevant data were integrated to generate minimum maintenance unit link constraint state characteristic data. This data comprehensively presents the overall state of minimum maintenance unit link constraints, supporting subsequent dynamic asset management analysis.

[0027] Step S5: Based on the construction-driven minimum maintenance unit node-attribute characteristic data and the minimum maintenance unit link constraint state characteristic data, perform dynamic management analysis of track construction period assets to generate dynamic management data of track construction period assets.

[0028] In this embodiment of the invention, the supply demand analysis for track component foundations is conducted. Based on the construction-driven minimum maintenance unit node-attribute characteristic data, combined with minimum maintenance unit related data and component association data, the focus is on the supply demand for track component foundations. Supply-related demand parameters are calculated, and requirements such as supply quantity, specifications, and cycle are clarified, generating track component foundation supply demand data. Subsequently, based on the minimum maintenance unit link constraint state characteristic data, combined with foundation supply demand data and risk state related data, dynamic change demand adjustment processing for maintenance components is carried out. According to the link constraint state and risk level, supply demand parameters are adjusted according to fixed rules to achieve precise matching between supply demand and link constraints and risk states, generating dynamic supply demand data for track components. Next, dynamic supply demand data for track construction is processed, covering the entire supply process. Real-time tracking of each stage of supply is performed, recording tracking data and anomalies, establishing an anomaly handling mechanism to ensure the supply process is traceable and controllable, generating dynamic supply tracking data for track construction. Finally, based on this tracking data, and combined with node-attribute characteristic data, link constraint status characteristic data, and dynamic supply and demand data, dynamic asset management during the rail construction period is carried out. This covers four core aspects: asset accounting, asset monitoring, asset optimization, and asset archiving. It enables refined control over the entire asset lifecycle, accounting for asset consumption, monitoring asset status, optimizing asset configuration, and standardizing asset archiving. Data consistency is verified throughout the process, and redundant and conflicting data is eliminated. Ultimately, all asset-related data is integrated to generate dynamic asset management data for the rail construction period. This provides accurate data support for asset management, subsequent operation and maintenance, and decision-making during the rail construction period, achieving refined asset management driven by dynamic updates of the BIM model.

[0029] Furthermore, step S1 includes the following steps:

[0030] Step S11: Obtain track construction data and track terrain data stored on the GIS platform;

[0031] In this embodiment of the invention, the track construction data encompasses design parameter data, construction process data, and component foundation data throughout the entire track construction process. The design parameter data includes the track line's plane coordinates, longitudinal profile elevation, track gauge standard, curve radius, and transition curve length. The construction process data includes component processing accuracy data, on-site installation deviation data, and construction procedure completion node data. The component foundation data includes material parameters, dimensional specifications, production batches, and quality inspection data for various components such as rails, sleepers, track beds, bridges, and tunnels. The track topographic data stored on the GIS platform includes topographic elevation data, topographic features, geological stratification data, and surrounding structure distribution data within a certain range along the track line (50 meters on each side of the track centerline). The topographic elevation data has a sampling accuracy of 10 centimeters, the geological stratification data specifies the thickness, bearing capacity, and shear strength parameters of each soil layer, and the surrounding structure distribution data specifies the boundary coordinates and structural types of the structures. All track construction data is acquired through a data acquisition terminal covering the entire track construction process. The acquisition frequency is synchronized with the construction procedures, and relevant data for each completed construction procedure is collected immediately to ensure data timeliness and support dynamic updates to the BIM model. The track terrain data stored on the GIS platform is acquired through a combination of on-site surveys and satellite remote sensing. On-site surveys use total stations to accurately measure the coordinates and elevations of key terrain control points along the track, while satellite remote sensing data resolution is controlled within 1 meter. The fusion of these two data sets forms a complete track terrain data set, providing data support for the precise alignment of subsequent track components with the terrain and ensuring consistency between asset data and actual terrain and construction scenarios during the track construction period.

[0032] Step S12: Perform track scene terrain feature analysis on the track terrain data to generate track scene terrain feature data;

[0033] In this embodiment of the invention, a comprehensive analysis of the terrain features of the track scene is performed on the track terrain data obtained from the GIS platform. Spatial coordinate information and elevation information are extracted from the track terrain data, and a three-dimensional spatial model of the terrain along the track is constructed. Based on this three-dimensional spatial model, spatial vector feature analysis of the track terrain is carried out, and core vector features such as terrain slope, aspect, terrain undulation, and terrain fragmentation are extracted. The terrain slope is divided into four levels: 0-5°, 5-15°, 15-30°, and above 30°. The terrain undulation is divided into four levels: less than 5 meters, 5-10 meters, 10-20 meters, and above 20 meters. The terrain fragmentation is divided into four levels: complete area, slightly fragmented, moderately fragmented, and severely fragmented. Based on this, track terrain spatial vector feature data is generated, which clearly defines the coordinates, elevation, slope, aspect, undulation, and fragmentation parameters of each terrain control point. Subsequently, the spatial vector feature data of the track terrain was segmented into spatial vector regions. The segmentation was based on the direction of the track centerline and abrupt changes in terrain feature parameters. Using the track centerline as a reference, each 100-meter segmentation unit was used. When the difference in terrain slope between two adjacent basic segmentation units exceeded 10°, the difference in terrain undulation exceeded 8 meters, or the geological stratification type changed, additional segmentation nodes were added, dividing the terrain along the track into several independent terrain vector segmentation regions. Each segmentation region formed a set of track terrain spatial vector segmentation data. At the same time, the terrain regional attribute feature data of each segmentation region were marked, including the geological type (silty clay, sand, rock, etc.), groundwater depth, standard value of foundation bearing capacity, and terrain use type (farmland, forest, construction land, etc.) within the region, ensuring that the terrain attribute features of each segmentation region were uniform and clear. Finally, all track terrain spatial vector segmentation data and corresponding terrain regional attribute feature data were integrated. Combined with the track construction line design requirements, the impact of each terrain segmentation region on track construction was analyzed, the construction adaptability of each region's terrain was clarified, and finally, the track scene terrain feature data was generated.

[0034] Step S13: Perform track construction component parsing processing based on track construction data to generate track construction component data;

[0035] In this embodiment of the invention, track construction data is analyzed and processed to identify track construction components. The data is categorized and filtered to remove redundant data irrelevant to the components, retaining all core data related to component design, processing, and installation. The filtered track construction data is then broken down and analyzed according to the type of track component. These components are functionally categorized into main track components, connecting components, and auxiliary components. Main track components include rails, sleepers, and track bed; connecting components include clamps, bolts, and fasteners; and auxiliary components include drainage ditches, guardrails, and signs. The relevant data for each type of component is analyzed one by one to extract core parameters. For rails, the model, length, cross-sectional dimensions, material strength, tensile strength, and wear resistance parameters are extracted. For sleepers, the type, length, width, height, concrete strength grade, and reinforcement parameters are extracted. For track bed, the material type, particle size distribution, thickness, and compaction parameters are extracted. For connecting components, the model, dimensions, material, and load-bearing capacity parameters are extracted. For auxiliary components, the dimensions, material, installation location, and functional parameters are extracted. Simultaneously, the connection relationships between various components are analyzed, clarifying the connection methods and parameters between rails and sleepers, sleepers and track bed, and rails and rail clamps, and determining the installation sequence and accuracy requirements for each component. Through the above comprehensive analysis and processing, the scattered track construction data is transformed into structured track construction component data.

[0036] Step S14: Perform track component hierarchical feature analysis on the track construction component data to generate track component hierarchical feature data;

[0037] In this embodiment of the invention, based on the data of track construction components, hierarchical feature analysis of track components is carried out to comprehensively explore various hierarchical features of track components and generate complete hierarchical feature data of track components. This data covers six major categories: functional hierarchical feature data of track components, structural hierarchical feature data of track components, engineering attribute hierarchical feature data of track components, construction status hierarchical feature data of track components, spatial hierarchical feature data of track components, and asset hierarchical feature data of track components. The various hierarchical feature data are interconnected and completely cover the key information of the entire life cycle of the components. The functional hierarchy feature data is divided into levels according to the core functions of the components. The first level is the track load-bearing components (rails, sleepers, ballast), the second level is the connecting and fixing components (clamps, bolts, fasteners), and the third level is the protective auxiliary components (drainage ditches, guardrails, signs), clearly defining the functional positioning and priority of each level of components. The structural hierarchy feature data is divided into levels according to the structural complexity and load-bearing capacity of the components. The first level is the main load-bearing components (rails, ballast, main bridge components), the second level is the secondary load-bearing components (sleepers, connecting clamps), and the third level is the non-load-bearing components (fasteners, guardrails, signs), clearly defining the structural parameters and load-bearing standards of each level of components. The engineering attribute hierarchy feature data clearly defines the construction technology, acceptance standards, quality grade, and installation accuracy requirements of the components. Among them, the construction technology specifies the component processing flow and on-site installation steps, and the acceptance standards specify the components. The pass / fail thresholds for various parameters are defined, and the quality level is divided into two grades: excellent and qualified, based on the test results. The installation accuracy requirements specify the thresholds for the positional deviation and elevation deviation of the component installation. The construction status hierarchical characteristic data specifies the current construction stage of the component, divided into five states: unprocessed, processing, processed and awaiting installation, installation, and installed and accepted. Each state corresponds to a specific time node and status identifier. The spatial hierarchical characteristic data specifies the spatial coordinates, elevation, and spatial distribution relationship of the component. Based on the track line design coordinates, the specific spatial location of each component is determined, and the spatial distance and spatial orientation relationship between components are specified. The asset hierarchical characteristic data specifies the component's label (unique QR code), supply information, service life, maintenance cycle, replacement cost, and value depreciation standard, ensuring the comprehensiveness and accuracy of asset data during the track construction period, and providing hierarchical component characteristic support for subsequent minimum maintenance unit division and dynamic asset management.

[0038] Step S15: Map the track component hierarchical feature data to the track scene terrain feature data to perform track component and terrain spatial alignment processing to generate track component terrain alignment data.

[0039] In this embodiment of the invention, using the terrain feature data of the track scene as a spatial reference, the hierarchical feature data of the track components is spatially mapped, and spatial alignment processing of the track components and the terrain is carried out to ensure that the track components are accurately matched with the actual terrain in spatial position, providing accurate spatial alignment data for the subsequent establishment of a dynamic BIM model. First, the spatial hierarchical feature data in the hierarchical feature data of the track components is extracted to clarify the spatial coordinates, elevation, and spatial distribution relationship of each track component. At the same time, the track terrain spatial vector segmentation data and terrain area attribute feature data in the terrain feature data of the track scene are extracted to clarify the spatial range, elevation parameters, and terrain attributes of each terrain segmentation area. Subsequently, a unified mapping relationship between the spatial coordinates of the track components and the spatial coordinates of the terrain was established. Using the coordinate system of the track centerline as a reference, the spatial coordinates of each track component were mapped to the corresponding terrain segmentation area. Based on the elevation parameters and slope characteristics of the terrain segmentation area, the spatial elevation and installation angle of the components were adjusted to ensure that the installation position of the components was compatible with the terrain conditions. For example, in terrain areas with a slope of 5-15°, the installation angle of the sleepers was adjusted to match the terrain slope, ensuring the bottom of the sleepers was in contact with the terrain surface, with elevation deviation controlled within ±5 mm. In areas with significant terrain undulation, the track bed thickness was adjusted according to changes in terrain elevation to ensure that the elevation of the top surface of the rails met design requirements, with a deviation not exceeding ±3 mm. Simultaneously, the compatibility of the component materials and installation methods with the terrain conditions was verified by combining terrain area attribute data. For example, in terrain areas with high groundwater levels, the track bed was ensured to have good drainage performance, and the installation position of the drainage ditches in the auxiliary components was consistent with the terrain drainage direction. Through the above spatial mapping and adjustment processing, the spatial alignment of track components with the terrain is completed, generating track component terrain alignment data. This data includes the spatial coordinates, elevation, installation angle of each track component, the corresponding terrain segmentation area information, the alignment deviation data between the component and the terrain, and the compatibility verification results. This achieves the organic integration of track component data and terrain data, providing accurate data support for the subsequent construction of dynamic BIM models, ensuring that the BIM model can truly reflect the actual scenario of track construction, and providing a reliable spatial foundation for the dynamic management of asset data during the track construction period.

[0040] Furthermore, step S12 includes the following steps:

[0041] Step S121: Perform spatial vector feature analysis of the track terrain based on the track terrain data to generate spatial vector feature data of the track terrain;

[0042] In this embodiment of the invention, the track topography data stored on the GIS platform is used as the sole basis for conducting spatial vector feature analysis of the track topography, ensuring that the analysis results fully match the actual terrain of the track construction. Spatial coordinate information and elevation information are comprehensively extracted from the track topography data. The spatial coordinate information adopts the National Geodetic Coordinate System, accurate to the millimeter level, and the elevation information adopts the 1985 National Elevation Datum. The sampling interval is consistent with the track topography data acquisition interval, i.e., one elevation control point data is collected every 10 meters. Based on the extracted coordinate and elevation information, a three-dimensional spatial model of the terrain along the track is constructed. The model can fully present all terrain details within 50 meters on both sides of the track centerline, including various terrain features such as protrusions, depressions, and gullies. Based on this three-dimensional spatial model, the system conducts spatial vector feature mining and analysis of the track terrain, focusing on extracting four core vector features: terrain slope, aspect, terrain undulation, and terrain fragmentation. Terrain slope is divided into four fixed levels: 0-5°, 5-15°, 15-30°, and above 30°, each corresponding to a specific slope threshold range. Terrain undulation is divided into four levels: less than 5 meters, 5-10 meters, 10-20 meters, and above 20 meters. The difference between the maximum and minimum elevations within a 50-meter radius of each terrain control point is used as the standard for undulation calculation. Terrain fragmentation is divided into four levels: intact area, slightly fragmented, moderately fragmented, and severely fragmented. The classification is based on the number of terrain slope abrupt change points and the integrity of terrain units within the area. A density of more than 3 abrupt change points per 100 meters indicates slightly fragmented terrain, more than 5 indicates moderately fragmented terrain, and more than 8 indicates severely fragmented terrain. The extracted vector feature data are standardized to eliminate differences in data units and ensure the comparability of various feature parameters. Finally, the spatial vector feature data of the track terrain is generated. This data includes the coordinates, elevation, slope, aspect, undulation and fragmentation parameters of each terrain control point. Each parameter has a clear numerical value or level label, which can accurately reflect the spatial vector characteristics of the terrain along the track.

[0043] Step S122: Perform spatial vector region segmentation on the track terrain spatial vector feature data to obtain track terrain spatial vector segmentation data, and label the terrain region attribute feature data of the track terrain spatial vector segmentation data;

[0044] In this embodiment of the invention, spatial vector feature data of the track terrain is used as the core to carry out spatial vector region segmentation. Simultaneously, the attribute feature data of the terrain regions is labeled to ensure that the vector features of each segmented terrain region are uniform and the attributes are clear. Spatial vector region segmentation is based on the direction of the track centerline and abrupt changes in terrain feature parameters. First, the size of the basic segmentation unit is determined. Using the track centerline as a reference, a fixed basic segmentation unit is formed every 100 meters. Each basic segmentation unit is 100 meters long along the track centerline and 50 meters wide on each side of the track centerline, forming a rectangular segmentation area. During the segmentation process, the differences in terrain feature parameters between adjacent basic segmentation units are strictly judged. When the difference in terrain slope between two adjacent units is greater than 10°, the difference in terrain undulation is greater than 8 meters, or the geological stratification type changes, a segmentation node is immediately added, splitting the original basic segmentation unit into two independent segmentation regions. This ensures that there are no significant abrupt changes in the terrain vector features within each segmentation region, and that the vector characteristics remain consistent. After segmentation, several independent terrain vector segmentation regions are formed. Each segmentation region corresponds to a set of track terrain spatial vector segmentation data, which clearly defines the boundary coordinates, spatial range, and terrain spatial vector characteristic parameters of each segmentation region. Simultaneously, terrain regional attribute characteristic data are labeled for each segmentation region. The labeling content includes the geological type (silty clay, sandy soil, rock, pebble soil), groundwater level depth (accurate to 0.1 meters), standard value of foundation bearing capacity (accurate to 1 kPa), and terrain use type (farmland, forest land, building land, idle land). Each labeled attribute characteristic data is derived from the geological layer data and surrounding environmental data in the GIS track terrain data, ensuring that the attribute labeling is true, accurate, and completely consistent with the actual terrain conditions. Finally, the track terrain spatial vector segmentation data and the corresponding terrain regional attribute characteristic data are obtained.

[0045] Step S123: Perform terrain feature analysis on the track scene based on the track terrain spatial vector segmentation data and the corresponding terrain area attribute feature data to generate track scene terrain feature data.

[0046] In this embodiment of the invention, track terrain spatial vector segmentation data and corresponding terrain area attribute feature data are integrated. Combined with the asset data management requirements during the track construction period driven by dynamic updates of the BIM model, and the line design requirements in the track construction data, a comprehensive analysis of the terrain features of the track scene is conducted. All track terrain spatial vector segmentation data are classified and organized, with each segmented region arranged according to the track line direction. The positional correspondence between each segmented region and the track line is clarified, and the corresponding track line segment is determined, ensuring accurate matching between the segmented region and the track construction segment. Subsequently, the terrain area attribute feature data of each segmented region is correlated and fused with the corresponding spatial vector segmentation data to clarify the correspondence between the vector features and attribute features of each segmented region. For example, for a segmented region with a slope of 5-15° and a geological type of silty clay, the compatibility relationship between its foundation bearing capacity standard value, groundwater level depth, and other attribute features and vector features is clarified. Based on the design requirements of the railway construction line, this study analyzes the impact of each terrain segmentation area on railway construction. It focuses on the influence of terrain slope, geological type, and groundwater depth on the installation accuracy of track components, selection of construction techniques, and asset management. The study clarifies the construction adaptability of each terrain area. For example, areas with rocky geology and slopes less than 5° have high adaptability and can directly carry out the installation of the main track components. Areas with groundwater depths less than 1 meter and sandy soil have low adaptability and require foundation treatment before construction. Through this comprehensive analysis, the scattered segmentation and attribute data are transformed into structured and systematic terrain feature data for the railway scene. This data fully includes the spatial range, vector characteristics, attribute characteristics, construction adaptability level, and impact analysis results of each terrain segmentation area, accurately reflecting the terrain scene characteristics of different sections along the railway line.

[0047] Furthermore, the track component hierarchical feature data mentioned in step S14 includes track component functional feature data, track component structural feature data, track component engineering attribute feature data, track component construction status feature data, track component spatial feature data, and track component asset feature data.

[0048] Furthermore, step S2 includes the following steps:

[0049] Step S21: Perform track construction phase analysis on the track construction data to generate track construction phase data;

[0050] In this embodiment of the invention, track construction data is analyzed in stages. The track construction stages are strictly divided into five fixed phases: survey and design, foundation construction, main structure construction, ancillary facilities construction, and final acceptance. The survey and design phase involves completing track route design, component parameter design, and terrain adaptation design, corresponding to the design parameter data in the track construction data. The foundation construction phase involves completing track bed foundation treatment, bridge foundation pouring, and tunnel excavation, corresponding to the foundation construction process data in the track construction data. The main structure construction phase involves completing the installation and laying of rails, sleepers, and track bed, as well as the main structure construction of bridges and tunnels, corresponding to the main component processing and installation data in the track construction data. The ancillary facilities construction phase involves completing the installation of ancillary components such as drainage ditches, guardrails, and signs, corresponding to the ancillary component construction data in the track construction data. The final acceptance phase involves completing the quality inspection of all component installations and asset verification, corresponding to the quality inspection data and asset accounting data in the track construction data. During the analysis, the track construction data corresponding to each stage is extracted, and the key nodes of each stage are marked to obtain the generated track construction stage data.

[0051] Step S22: Based on the track construction phase data, perform construction phase component construction simulation processing on the track component terrain alignment data to generate construction phase component construction simulation data;

[0052] In this embodiment of the invention, the construction simulation of track components is performed on the terrain alignment data of track components based on the track construction stage data. The simulation process is carried out step by step in strict accordance with the track construction stage sequence. The simulation of each stage is based on the track construction stage data of the corresponding stage, combined with the construction tasks of that stage and the spatial position of the components and terrain adaptation parameters in the track component terrain alignment data, to restore the real process of component construction. The simulation of the survey and design stage focuses on the adaptation simulation of component design parameters and terrain conditions, clarifying the matching degree between design parameters and terrain features, and marking the content in the design parameters that needs to be adjusted; the simulation of the foundation construction stage focuses on the construction process of track bed foundation treatment and bridge foundation pouring, simulating the foundation excavation depth, pouring strength and conformity with the terrain, ensuring that the foundation construction meets the terrain attribute requirements. For example, in areas with sandy soil geology, the simulation of foundation replacement treatment process is performed to clarify the replacement material and thickness; the simulation of the main structure construction stage focuses on the installation and laying process of rails, sleepers and track bed, simulating the installation sequence, installation accuracy and conformity with the terrain. The spatial alignment adjustment process strictly follows the spatial coordinates and elevation parameters in the terrain alignment data of the track components, simulating the installation position of each component. Installation deviations are controlled to be consistent with actual construction requirements, with horizontal deviations for rail installation controlled within ±2 mm and spacing deviations for sleeper installation controlled within ±3 mm. The simulation during the ancillary facility construction phase focuses on the installation adaptability of ancillary components, simulating the compatibility of drainage ditches with the terrain drainage direction and the compatibility of guardrail installation height with the terrain slope. The simulation during the final acceptance phase focuses on the component installation quality inspection simulation, comparing the quality of each component against quality inspection standards. Each stage of the simulation records the component's construction time, installation parameters, terrain adaptation adjustment records, quality inspection results, and asset consumption data. All simulation parameters are consistent with actual construction standards, and the simulation error is controlled within 5%, ultimately generating construction simulation data for the components during the construction phase.

[0053] Step S23: Analyze the dynamic process association nodes of components at each stage based on the construction simulation data of the components in the construction phase, generate dynamic process association node data of the components in each stage, and extract the characteristic data of the dynamic process association nodes of the components in each stage.

[0054] In this embodiment of the invention, dynamic process association nodes of components at each stage are analyzed based on the component construction simulation data during the construction phase. The component construction simulation process is broken down into five construction phases, and key association nodes for each process are extracted. Node types are divided into three categories: process start nodes, process connection nodes, and process completion nodes. Each node corresponds to a specific construction process, timestamp, and associated components. The process start node is the start time of each construction process, corresponding to the component construction preparation stage, marking the component preparation status and initial asset input data at this node. The process connection node is the connection time between two adjacent processes, corresponding to the transition stage from the completion of the previous process to the start of the next process, marking the process connection accuracy, component status, and cumulative asset consumption data at this node. The process completion node is the completion time of each construction process, corresponding to the component construction completion stage, marking the component installation quality, total asset consumption, and process completion acceptance results at this node. For example, in the main structure construction stage, the start node of the rail installation process corresponds to the rail transportation arrival time, the connection node corresponds to the start time of the rail-sleeper connection process, and the completion node corresponds to the rail installation acceptance time. Each node is associated with specific components such as rails and sleepers, and corresponding asset data. Analyze the relationships between each node, clarify the time intervals between adjacent nodes, the requirements for process connection, and the component association logic, mark the dependencies between nodes, and ensure that the process-related nodes can fully reflect the dynamic process of component construction, generating dynamic process-related node data for stage components. Based on this, extract the characteristic data of the dynamic process-related nodes for stage components from the dynamic process-related node data for stage components. The extracted features include node time features (node ​​timestamp, time interval between adjacent nodes), node association features (number of associated components, number of associated processes, node dependencies), node quality features (quality compliance rate of the process corresponding to the node), and node asset features (asset input and consumption data corresponding to the node), generating characteristic data of dynamic process-related nodes for stage components.

[0055] Step S24: Analyze the correlation characteristics of component construction organization at each stage based on the component construction simulation data during the construction phase, and generate stage component construction organization correlation characteristic data;

[0056] In this embodiment of the invention, the component construction organization correlation characteristics analysis is performed at each stage based on the component construction simulation data. The construction organization correlation characteristic analysis revolves around four core elements: personnel allocation, equipment scheduling, material supply, and construction team division of labor. The analysis focus for each construction stage is determined in conjunction with the construction tasks and terrain conditions of that stage, ensuring that the analysis results align with actual construction organization needs. The survey and design stage focuses on analyzing the characteristics of design personnel allocation and design equipment scheduling, clarifying the division of labor, working hours, and usage frequency and parameter settings of design personnel and design equipment, and marking the material consumption (design drawings, survey equipment consumables) and corresponding asset data for the design stage. The foundation construction stage focuses on analyzing the number of construction personnel, types of construction equipment, and material supply rhythm, combined with the terrain regional attributes (geological type, groundwater level), clarifying the number of construction teams, construction equipment models, and material supply cycles for different terrain regions, and marking equipment usage time, material consumption quantity, and corresponding asset consumption data. The main structure construction stage focuses on analyzing the characteristics of construction team division of labor, equipment collaborative scheduling, and precise material supply, clarifying the characteristics of the rail installation team. The division of labor among sleeper installation teams, the time nodes for equipment coordination and scheduling, and the matching degree between material supply and construction progress are all determined to ensure that component installation is synchronized with equipment and material supply. The work efficiency, equipment coordination error, and material loss rate of each team are marked. During the construction phase of ancillary facilities, the focus is on analyzing the degree of personnel optimization and the scheduling characteristics of small equipment. The division of labor among personnel for the installation of ancillary components and the frequency of use of small equipment are clarified, and equipment loss and material consumption data are marked. During the final acceptance phase, the focus is on analyzing the configuration of acceptance personnel and the scheduling characteristics of testing equipment. The division of labor among acceptance personnel and the parameter settings and usage procedures of testing equipment are clarified. The usage cost of testing equipment and the number of asset verifications during the acceptance process are marked, and the associated characteristic data of component construction organization for each phase are generated.

[0057] Step S25: Perform dynamic evolution feature analysis on the track component terrain alignment data by using the dynamic process association node feature data of stage components and the construction organization association feature data of stage components to generate dynamic evolution feature terrain data of track components.

[0058] In this embodiment of the invention, each stage combines the process-related node feature data and construction organization-related feature data of the corresponding stage to conduct dynamic evolution analysis of the component parameters, spatial position, and terrain adaptation relationship in the track component terrain alignment data, clarifying the state changes, terrain adaptation adjustments, and asset consumption evolution patterns of the components at different stages. Based on the node time characteristics and correlation characteristics in the process-related node feature data, the state changes of the components at different process nodes are analyzed, including the installation progress, quality status, and spatial position adjustment of the components. For example, from the start node to the completion node of rail installation, the adjustment process of the rail spatial position and elevation parameters is analyzed to clarify the rail status and corresponding asset consumption changes at each node. Secondly, combined with the personnel, equipment, and material characteristics in the construction organization-related feature data, the impact of construction organization elements on component evolution is analyzed to clarify the impact of personnel efficiency, equipment accuracy, and material quality on the component installation status and evolution speed. For example, the analysis of construction equipment scheduling is conducted. The impact of errors on component installation progress and evolution rhythm is investigated, and the causes of asset loss during component evolution are identified. Finally, by combining the terrain adaptation parameters in the track component terrain alignment data, the adaptation evolution relationship between the component and the terrain at different stages is analyzed. The constraints of terrain conditions (slope, geological type) on component evolution and the adjustments made by the component to adapt to the terrain (installation angle, size adjustment) are clarified. For example, from the foundation construction stage to the main structure construction stage, the evolution process of track bed thickness with terrain elevation is analyzed, the dynamic adjustment parameters of track bed and terrain adaptation and the corresponding changes in asset input are clarified, and dynamic evolution characteristic terrain data of track components are generated.

[0059] Step S26: Establish a dynamic BIM model for track construction based on terrain data showing the dynamic evolution characteristics of track components.

[0060] In this embodiment of the invention, a dynamic BIM model for track construction is established based on terrain data showing the dynamic evolution of track components. The terrain foundation layer, based on terrain feature data of the track scene, constructs a 3D model of the terrain along the track, accurately presenting the spatial extent, vector features, and attribute features of each terrain segmentation area. Elevation accuracy is controlled within ±10 mm, and spatial coordinates are consistent with the national geodetic coordinate system, providing a precise terrain foundation for the entire model. The component evolution layer, centered on the terrain data showing the dynamic evolution of track components, loads 3D models of all track components. Each component model corresponds to specific hierarchical feature parameters, dynamically presenting the status changes, spatial position adjustments, and terrain adaptation processes of components at different construction stages. The parameters of the component models are completely consistent with the hierarchical feature data and dynamic evolution data of the track components, and the component status update frequency is synchronized with the construction procedures. The process association layer, based on the dynamic process association node feature data of stage components, links the component evolution process with the construction process, marking the component status, time node, and asset data corresponding to each process association node, achieving synchronous linkage between process and component evolution; the construction organization layer, based on the construction organization association feature data of stage components, loads relevant information on construction personnel, equipment, and materials, presenting the scheduling process of construction organization elements and their relationship with component evolution, and marking the asset consumption data corresponding to each construction organization element; the asset management layer integrates asset-related data at all levels, clarifying the asset input, consumption, and loss data for each component and each construction stage, achieving real-time statistics, dynamic updates, and precise management of asset data during the track construction period. During model construction, it ensures accurate data connection at each level, eliminates data deviations, and the model's dynamic update mechanism is synchronized with the track construction stage. After each construction stage is completed, the component status, process progress, construction organization status, and asset data are automatically updated to ensure that the model can truly and accurately reflect the actual scenario and asset status of track construction.

[0061] Furthermore, as an embodiment of the present invention, referring to FIG2, which is a detailed implementation flowchart of step S3 in FIG1, step S3 in this embodiment includes:

[0062] Step S31: Design the linkage unit driven by the track construction based on the dynamic BIM model of track construction, and generate track construction driving unit data;

[0063] In this embodiment of the invention, the linkage unit design strictly adheres to the layered architecture of the BIM model, focusing on the data from the component evolution layer, process association layer, and asset management layer. Guided by the driving needs of the entire rail construction process, it integrates components, processes, and asset elements with collaborative driving relationships into independent linkage units. The division of linkage units must meet three requirements: single driving function, centralized related elements, and convenient asset management. Each linkage unit corresponds to a clear driving function and asset management scope. During the design process, the evolution characteristics, process associations, and asset attributes of all components in the dynamic BIM model of rail construction are first sorted out to clarify the driving dependencies between components, that is, the impact logic of changes in the construction status or asset consumption of one component on other related components. For example, the installation progress of rail components will directly drive the construction progress of connecting components such as sleepers and fasteners, and their asset consumption will affect the asset management data of the entire main structure construction stage. Based on the driving dependency relationship, the linkage units are divided into four categories according to the driving function type: basic driving units, main driving units, auxiliary driving units, and acceptance driving units. The basic driving unit corresponds to the foundation construction stage and consists of components such as track bed foundation and bridge foundation, as well as corresponding construction procedures and asset elements. Its core driving function is to support the subsequent construction of main components. The main driving unit corresponds to the main structure construction stage and consists of main components such as rails, sleepers, and track bed, as well as corresponding construction procedures and asset elements. Its core driving function is to ensure the formation of the main track structure. The auxiliary driving unit corresponds to the auxiliary facility construction stage and consists of auxiliary components such as drainage ditches and guardrails, as well as corresponding construction procedures and asset elements. Its core driving function is to improve the track auxiliary protection system. The acceptance driving unit corresponds to the completion and acceptance stage and consists of all installed components and related elements for quality inspection and asset verification. Its core driving function is to complete the quality acceptance of track construction and asset accounting. Each linkage unit clearly defines its boundary scope, component list, associated processes, driving logic, asset control nodes, and responsibility identifiers. The boundary scope is based on the spatial location of the components and the process relationship. The component list specifies the model, quantity, and parameters of all components within the unit. The driving logic specifies the collaborative driving mechanism of each element within the unit. The asset control nodes specify the statistical nodes for asset input and consumption within the unit. The responsibility identifiers specify the construction and control responsibility scope corresponding to the unit, generating track construction driving unit data.

[0064] Step S32: Extract the track maintenance component feature data and the track maintenance-free component feature data from the track component hierarchical feature data respectively, and perform track maintenance component association feature analysis based on the track maintenance component feature data and the track maintenance-free component feature data to generate track maintenance component association feature data;

[0065] In this embodiment of the invention, during the extraction process, the engineering attribute hierarchical feature data, asset hierarchical feature data, and construction status hierarchical feature data of the components are strictly followed to clarify the classification criteria for maintenance-free components. The classification criteria are fixed and have no ambiguous intervals: maintenance-free components are those that are marked as installed and accepted in the construction status hierarchical feature data, have a service life of more than 50 years in the asset hierarchical feature data, and have a quality grade of excellent and no easily damaged parts in the engineering attribute hierarchical feature data. Specifically, these include reinforced concrete track bed foundations, bridge main load-bearing structures, etc. All hierarchical feature data of such components are extracted and integrated to form track maintenance-free component feature data. This data clarifies the spatial location, parameters, service life, quality grade, and asset value of each maintenance-free component. Repairable components refer to all components that do not meet the criteria for exemption from repair, specifically including rails, sleepers, rail clamps, bolts, fasteners, drainage ditches, guardrails, etc. All hierarchical feature data of such components are extracted, with a focus on retaining core data such as maintenance process and acceptance standards in the engineering attributes, maintenance cycle, replacement cost, and value depreciation standards in the asset level, spatial location and relationships in the spatial level, and loss status in the construction state. These are integrated to form track repairable component feature data, which clearly defines the maintenance-related parameters and asset information of each repairable component. After extraction, a correlation feature analysis of track maintenance components is conducted. The core analysis dimensions are the spatial location, functional positioning, construction sequence relationships, and asset consumption logic of the maintenance components. The correlation types and strengths between maintenance components are clarified. Correlation types are categorized into four types: spatial correlation, functional correlation, procedural correlation, and asset correlation. Spatial correlation refers to components being spatially adjacent and whose maintenance operations can be carried out simultaneously, such as rails and sleepers in the same section. Functional correlation refers to components having complementary functions and mutually influencing maintenance needs, such as steel plates and bolts. Procedural correlation refers to components having adjacent construction sequences and dependent maintenance timing, such as rail installation and rail grinding maintenance. Asset correlation refers to the interconnected asset consumption during component maintenance, such as fastener replacement and bolt wear. Correlation strength is categorized into three levels based on the tightness of the correlation: strong correlation (over 90% synchronization rate), medium correlation (60%-90% synchronization rate), and weak correlation (below 60% synchronization rate). Correlation strength is calculated using maintenance synchronization records and asset consumption linkage data from component construction simulation data to generate track maintenance component correlation feature data.

[0066] Step S33: Perform minimum maintenance unit boundary feature analysis based on the track maintenance component association feature data and track construction driving unit data to generate track minimum maintenance unit boundary feature data;

[0067] In this embodiment of the invention, based on the boundary range of the driving unit, and combined with the association characteristics of maintenance components, three core criteria for the boundary division of the minimum maintenance unit are defined: driving unit boundary constraints, maintenance component association strength constraints, and spatial location concentration constraints. All three constraints must be satisfied simultaneously to determine the boundary range. First, based on the boundary range of the linkage unit in the track construction driving unit data, each driving unit is used as the basic area for the division of the minimum maintenance unit, ensuring that the boundary of the minimum maintenance unit does not exceed the boundary of the corresponding driving unit, avoiding conflicts in the driving logic and asset management scope between the maintenance unit and the driving unit. For example, maintenance components within a basic driving unit are only divided into minimum maintenance units within the boundary of that driving unit. Second, based on the association strength in the track maintenance component association characteristic data, strongly and moderately associated maintenance components are included in the same boundary range, while weakly associated maintenance components are divided separately or merged according to spatial location. This ensures that the linkage maintenance efficiency of maintenance components within the same minimum maintenance unit is maximized, reducing overlapping maintenance operations and asset waste. For example, strongly associated rails and sleepers must be included in the same boundary range, while weakly associated guardrails can have their boundaries divided separately. Finally, by combining the spatial hierarchy feature data of the maintenance components, it is ensured that the spatial locations of maintenance components within the same minimum maintenance unit are concentrated, with the boundary range being a rectangular area. The side length of the rectangle is determined according to the spatial distribution range of the components, with a length not exceeding 100 meters and a width not exceeding 10 meters. The boundary coordinates are based on the national geodetic coordinate system, accurate to the millimeter level, ensuring that the boundary range can accurately cover all maintenance components within the area, and that there is no overlap or gap with the boundaries of adjacent minimum maintenance units, with gap errors controlled within ±5 millimeters. During the analysis, feature parameters of each potential boundary range are extracted simultaneously, including boundary coordinates, boundary area, list of maintenance components within the area, distribution of association strength, corresponding drive unit identifier, asset management nodes, etc. Adaptability verification is performed on each potential boundary range, including the adaptability of the boundary range to the drive unit, the rationality of the association of maintenance components, the concentration of spatial distribution, and the convenience of asset management. Boundary ranges that fail verification are readjusted in terms of boundary coordinates and component composition until all requirements are met, generating boundary feature data for the minimum maintenance unit of the track.

[0068] Step S34: Establish the minimum maintenance unit of the track based on the boundary feature data of the minimum maintenance unit of the track, and generate the minimum maintenance unit data of the track;

[0069] In this embodiment of the invention, the establishment process strictly follows the constraints of boundary feature data, constructing minimum maintenance units one by one according to the boundary range. The establishment of each minimum maintenance unit is divided into four fixed steps: boundary positioning, component collection, unit identification, and parameter improvement, ensuring that the established maintenance units are standardized, uniform, and traceable. First, boundary positioning is carried out. Based on the boundary coordinates in the boundary feature data, the boundary range of each minimum maintenance unit is accurately located in the component evolution layer and terrain foundation layer of the dynamic BIM model of track construction, and the boundary range is visualized. At the same time, the coordinates and other parameters in the boundary feature data are associated to achieve precise control of the boundary range. Second, component collection is carried out. According to the list of maintenance components in the area in the boundary feature data, all data of the corresponding components in the track maintenance component feature data are accurately collected into the corresponding minimum maintenance unit. At the same time, components exempt from maintenance and maintenance components outside the boundary range are excluded to ensure that the component list in each maintenance unit is completely consistent with the boundary feature data. During the collection process, the collection time, corresponding boundary identification, and asset ownership information of the components are recorded to avoid component collection errors or omissions. Next, unit identification is carried out, assigning a unique identification code to each smallest maintenance unit. Parameters such as the correlation strength distribution, corresponding drive unit identifier, and asset control node in the boundary feature data, parameters such as maintenance process, maintenance cycle, and asset value in the maintenance component feature data, and parameters such as drive logic and responsibility identifier in the drive unit data are all incorporated into the corresponding smallest maintenance unit. This clarifies the maintenance needs, asset consumption standards, drive dependencies, and control responsibilities of each maintenance unit, generating track smallest maintenance unit data.

[0070] Step S35: Perform construction-driven minimum maintenance unit node and attribute feature analysis on the track minimum maintenance unit data to generate construction-driven minimum maintenance unit node-attribute feature data.

[0071] In this embodiment of the invention, the process is carried out step by step for each minimum maintenance unit, consisting of three fixed stages: node establishment, attribute feature extraction, and node-attribute mapping. This ensures accurate node division, complete attribute features, and clear mapping relationships. First, the structural nodes of the minimum maintenance units in the construction-driven branch are established. Taking the driving logic and driving dependencies in the track construction-driven unit data as the core, and combining the component composition and maintenance association characteristics of the minimum maintenance unit, structural nodes are established for each minimum maintenance unit. The node types are divided into three categories: driving nodes, maintenance nodes, and asset nodes. Each node corresponds to a unique node code and a clear functional positioning. The driving nodes correspond to the driving functions of the driving units, marking the driving source, driving parameters, and driving sequence of the smallest maintenance unit. They are precisely linked with the driving nodes of the driving units to ensure that the node driving logic is consistent with the driving unit. The maintenance nodes correspond to the maintenance needs of the smallest maintenance unit, marking the start node, core node, and completion node of the maintenance operation. Each maintenance node corresponds to specific maintenance procedures, maintenance parameters, and maintenance asset consumption standards, and is precisely matched with the association characteristics of the maintenance components. The asset nodes correspond to the asset management needs of the smallest maintenance unit, marking asset input nodes, consumption nodes, and accounting nodes. Each asset node corresponds to specific asset types, asset values, consumption quantities, and depreciation status, and is precisely connected with the asset management layer data. Each node clearly defines its spatial location, timestamp, associated components, associated parameters, and responsibility identifier. The node coding rules are associated with the smallest maintenance unit identifier code to ensure node traceability. Secondly, we conduct attribute feature analysis of the smallest maintenance unit. Based on the component composition, maintenance parameters, drive affiliation, and asset information in the smallest maintenance unit data, we extract five major categories of attribute features: component attribute features (model, quantity, parameters, and quality status of components within the unit), maintenance attribute features (maintenance cycle, maintenance process, maintenance difficulty, and replacement cost), drive attribute features (drive type, drive dependency, and drive intensity), spatial attribute features (unit boundary coordinates, spatial range, and terrain adaptation parameters), and asset attribute features (total assets, asset consumption rate, asset depreciation standard, and asset accounting cycle). Each attribute feature has a clear value or identifier. For example, the maintenance cycle is accurate to the day, the maintenance difficulty is divided into three levels according to the complexity of the maintenance procedures, and the asset consumption rate is calculated based on the daily consumption amount. Finally, attribute feature mapping processing is carried out for each structural node. The extracted five categories of attribute feature data are accurately mapped to the corresponding nodes according to the node function. The driving node is mapped to the driving attribute features and the associated asset node attribute features. The maintenance node is mapped to the maintenance attribute features, component attribute features and the associated asset consumption node attribute features. The asset node is mapped to the complete asset attribute features and the associated driving and maintenance node attribute features. Data redundancy and conflicts are eliminated in the mapping process to ensure that the attribute features of each node are complete, accurate and match the node function, and to generate the construction-driven minimum maintenance unit node-attribute feature data.

[0072] Furthermore, step S31 includes the following steps:

[0073] Step S311: Perform track-driven function analysis on the dynamic BIM model of track construction and generate track-driven function data;

[0074] In this embodiment of the invention, relying on the layered architecture of the BIM model, the focus is on the component evolution layer, the process association layer, and the asset management layer, while simultaneously integrating relevant data from the terrain foundation layer and the construction organization layer to ensure coverage of the entire process and all elements of rail construction. First, the evolutionary characteristics, installation process, and asset attributes of all components in the BIM model are analyzed. Combining the core tasks of the five construction phases—survey and design, foundation construction, etc.—the fundamental driving direction for each component is determined: rails correspond to the main body forming drive, track bed foundations correspond to the foundation bearing drive, drainage ditches correspond to the protective auxiliary drive, and acceptance-related elements correspond to the quality and asset acceptance drive. Second, according to the requirements of quantifiable, verifiable, and controllable single functions, the specific driving functions of each component are broken down, clarifying their specific content, achievement goals, triggering conditions, and output results. Each function has clear parameters and acceptance standards. For example, the rail driving function is broken down into main body forming, installation accuracy, and asset consumption drives, with clearly defined corresponding goals, triggering conditions, and output data. Simultaneously, process nodes and construction organization-related data are linked to clarify the process support and organizational guarantees required for each driving function, and the impact of equipment accuracy and material quality on the driving function is analyzed. Finally, each drive function is verified for compatibility, checking its matching degree with the construction phase, component evolution, and asset management. If it is not qualified, it is readjusted. After verification, the information is integrated by type to generate track drive function data.

[0075] Step S312: Perform track drive dependency analysis based on track drive function data to generate track drive dependency data;

[0076] In this embodiment of the invention, the analysis is conducted according to the type of driving function. First, the distribution and corresponding elements of four types of driving functions—basic, main, auxiliary, and acceptance—are analyzed. Then, based on the fixed standard that "a driving function directly affecting the normal operation of another driving function indicates a dependency," the mutual influence logic is analyzed, distinguishing between dependency sources and dependency targets. Basic driving functions are prerequisites for subsequent functions and form strong dependencies with the other three types of driving functions. For example, the output result of the track bed foundation bearing capacity drive is the trigger condition for the rail installation accuracy drive; if the standard is not met, the latter cannot start, and the asset consumption data of both are linked. The main driving function forms strong dependencies internally and medium dependencies with auxiliary driving functions; the auxiliary function's preliminary preparations can be initiated once the main function is basically completed. The acceptance driving function is strongly dependent on all three types; it can only start if all prerequisite functions meet the standards, and its asset accounting data needs to integrate the asset consumption data of the first three types of functions. The analysis clarifies the dependency source, dependency target, triggering condition, and scope of influence for each type of dependency. Dependency intensity is divided into strong and medium levels, and asset association logic is marked. After the analysis is completed, all information is integrated to generate track driving dependency relationship data.

[0077] Step S313: Establish the linkage unit for track construction drive through track drive function data and track drive dependency data, and generate track construction drive unit data.

[0078] In this embodiment of the invention, track construction-driven linkage units are established using track-driven function data and track-driven dependency data. The establishment process strictly adheres to four principles: coordinated driving functions, close dependencies, convenient asset management, and clear boundaries. The process proceeds step-by-step according to the type of driving function, with each unit undergoing five fixed steps: dependency aggregation, element integration, boundary division, parameter refinement, and verification confirmation. Based on dependency data, strong and medium-dependency driving functions are grouped into collaborative function groups, with strong dependencies at the core and medium dependencies as supplements. Next, the corresponding components, processes, and asset elements of the function groups are extracted and associated with relevant BIM model data to form a preliminary unit framework. Boundary division is based on the spatial location of components, process associations, and driving range, combined with terrain segmentation data, using rectangular boundaries with a length not exceeding 200 meters and a width of 50 meters on each side of the track, with coordinates accurate to the millimeter level to ensure no overlap or gaps. Subsequently, a unique code is assigned to each unit, clarifying parameters such as core driving functions, component elements, and asset management nodes, generating track construction-driven unit data.

[0079] Furthermore, step S35 includes the following steps:

[0080] Step S351: Perform minimum maintenance unit attribute feature analysis on the minimum maintenance unit data of the track component hierarchy feature data to generate minimum maintenance unit attribute feature data;

[0081] In this embodiment of the invention, the process proceeds step by step, starting with each smallest maintenance unit. It relies on six categories of hierarchical features in the track component hierarchical feature data, combined with core information such as the component composition and boundary range of the smallest maintenance unit, to ensure comprehensive coverage of all elements without omissions. First, a list of components for each smallest maintenance unit is compiled, clarifying the component type, model, and quantity, and associating it with the six categories of hierarchical feature data for the corresponding components to ensure accurate data correlation and no gaps. Second, attribute features are extracted according to five fixed categories, with specific parameters defined for each category: component attribute features extract component model, quantity, material, size, and quality status, and statistically analyze the overall integrity rate of the components; maintenance attribute features extract maintenance processes, acceptance standards, maintenance cycles, replacement costs, and depreciation standards; drive attribute features extract the corresponding drive unit type, core drive functions, and dependencies; spatial attribute features extract boundary coordinates, spatial range, and terrain adaptation parameters, with coordinates accurate to the millimeter level; asset attribute features extract supply information, wear status, etc., generating attribute feature data for the smallest maintenance unit.

[0082] Step S352: Based on the track drive function data and track drive dependency data corresponding to the track construction drive unit data, establish the minimum maintenance unit structure node of the construction drive branch of the track minimum maintenance unit data, and generate the construction drive minimum maintenance unit structure node data.

[0083] In this embodiment of the invention, the driving unit corresponding to each minimum maintenance unit is clearly defined, and the core driving functions and driving dependencies in the driving unit data are associated. Simultaneously, the establishment benchmark for the structural nodes of the unit is determined by combining the component composition and maintenance association characteristics of the minimum maintenance unit. The benchmark must be consistent with the driving branches of the driving unit. For example, for a maintenance unit under the main driving unit, the establishment of structural nodes must revolve around driving functions such as main body forming and installation accuracy. Secondly, minimum maintenance unit structural nodes are established according to three types of fixed nodes: driving nodes, maintenance nodes, and asset nodes. Each node is assigned a unique node code. The driving node corresponds to the driving function of the driving unit, marking the driving source, driving parameters, driving sequence, and driving dependencies of the minimum maintenance unit. It is associated with the corresponding driving function parameters in the track driving function data and the dependency strength in the track driving dependency data, clarifying the triggering conditions and output results of the driving node. For example, the driving node of the rail-sleeper maintenance unit needs to mark the parameters and driving sequence of the main body forming drive, as well as the strong dependency relationship with the track bed foundation driving node. Maintenance nodes correspond to the maintenance needs of the smallest maintenance unit, marking the start, core, and completion nodes of maintenance operations. Each maintenance node corresponds to specific maintenance procedures, parameters, and asset consumption standards, and is linked to the correlation type and strength in the track maintenance component correlation feature data, clarifying the temporal relationship of maintenance nodes. Asset nodes correspond to the asset management needs of the smallest maintenance unit, marking asset input, consumption, and accounting nodes. Each asset node corresponds to specific asset type, value, quantity consumed, and depreciation, and is linked to the asset hierarchy features in the track component hierarchy feature data, ensuring accurate matching between asset nodes and asset management. Each node clearly defines its spatial location, timestamp, associated components, parameters, and responsibility identifier. The spatial location is precisely correlated with the boundary coordinates of the smallest maintenance unit, and the timestamp is consistent with the construction procedures and maintenance sequence, generating construction-driven structural node data for the smallest maintenance unit.

[0084] Step S353: Transmit the attribute feature data of the minimum maintenance unit to the construction-driven minimum maintenance unit structure node data for attribute feature mapping processing of each structure node, and generate construction-driven minimum maintenance unit node-attribute feature data.

[0085] In this embodiment of the invention, the attribute feature data of the minimum maintenance unit is transmitted to the structural node data of the construction-driven minimum maintenance unit for attribute feature mapping processing of each structural node. The mapping process proceeds step-by-step for each minimum maintenance unit. First, the five major categories of attribute features of the unit are identified and their specific parameters are categorized. Simultaneously, the type and functional positioning of each node are identified to ensure accurate matching between attributes and node functions. Then, targeted mapping is performed according to node type: driving nodes are mapped to driving, spatial, and associated asset attributes; maintenance nodes are mapped to maintenance, component, and associated asset consumption attributes; and asset nodes are mapped to complete asset attributes and associated driving and maintenance node attributes. During the mapping process, the matching degree between attributes and nodes is checked to eliminate data redundancy and conflicts. Conflicting data is corrected according to the principle of "node function priority, data accuracy priority." Finally, the mapping results of all nodes in each unit are integrated, clarifying the attribute parameters, data sources, and verification results corresponding to the nodes. These are then stored according to unit identifier codes to generate construction-driven minimum maintenance unit node-attribute feature data, fully presenting the node structure and attribute features, providing accurate support for subsequent steps.

[0086] Furthermore, step S4 includes the following steps:

[0087] Step S41: Perform construction status feature analysis on the minimum maintenance unit node based on the construction-driven minimum maintenance unit node attribute feature data, and generate minimum maintenance unit node construction status feature data;

[0088] In this embodiment of the invention, the process proceeds step by step according to each smallest maintenance unit. Relying on the node type, attribute parameters, and relationships in the node-attribute feature data, combined with the corresponding components, construction procedures, and driving requirements, it ensures coverage of all nodes without omissions. First, a list of driving nodes, maintenance nodes, and asset nodes for each unit is compiled, clarifying the code and function of each node. Fixed, quantifiable monitoring indicators are set according to node type: Driving nodes monitor drive startup status, parameter compliance rate, timing deviation, and dependency satisfaction. Startup status is only divided into "started" and "not started," compliance rate is accurate to 1%, timing deviation is accurate to the hour, and dependency satisfaction is recorded as 100% or 0%. Maintenance nodes monitor procedure progress, accuracy compliance rate, asset consumption deviation, and completion status. Progress is calculated based on the proportion of procedures, deviation is accurate to the yuan, and completion status is divided into "not started," "in progress," and "completed." Asset nodes monitor input progress, consumption rate, calculated compliance rate, and value deviation. Rate is accurate to yuan / day, and other indicators are accurate to 1% or yuan. Verify the monitoring indicators of each node, confirm their authenticity by combining construction phase data and component evolution data, eliminate abnormal data, mark the reasons and adjustment measures for nodes with deviations exceeding 5%, verify the consistency by linking the BIM model construction simulation data, and the construction status characteristic data of the smallest maintenance unit node.

[0089] Step S42: Perform construction-driven minimum maintenance unit constraint feature analysis based on the construction-driven minimum maintenance unit node-attribute feature data to generate construction-driven minimum maintenance unit constraint feature data;

[0090] In this embodiment of the invention, constraint feature analysis of the minimum maintenance unit driven by construction is performed based on the node-attribute feature data of the construction-driven minimum maintenance unit. Following the rules in the driving function, boundary, and driving dependency relationship data of the driving unit data, and combined with the unit node structure and attribute features, the constraint analysis is ensured to be comprehensive and the parameters are clear. Four types of fixed constraints are clearly defined, each with specific standards and parameters: Driving constraints correspond to the driving node function, with a constraint driving parameter compliance rate of no less than 95%, a timing deviation of no more than 2 hours, and a dependency satisfaction rate of 100%; if these standards are not met, node operation is stopped. Construction constraints correspond to maintenance nodes and component construction, with a constraint maintenance accuracy compliance rate of no less than 98%, a process progress deviation of no more than 1 day, and conformity to component evolution characteristics and terrain adaptation requirements. Asset constraints correspond to asset node management, with a constraint asset consumption deviation of no more than ±500 yuan and an accounting compliance rate of no less than 99%, with consumption and accounting standards set according to the unit asset attribute characteristics. Spatial constraints correspond to unit boundaries and terrain adaptation, with a constraint unit boundary deviation of no more than ±5 millimeters, and component installation and terrain adaptation parameters conforming to the node-attribute feature data requirements. The constraint strength and scope of influence of each constraint are analyzed. The strength is divided into two levels: strong constraint and medium constraint. The driving and spatial constraints are strong constraints, and the construction and asset constraints are medium constraints. The specific impact of each constraint on node operation, component construction and asset management is clarified, and the constraint characteristic data of the construction-driven minimum maintenance unit are generated.

[0091] Step S43: Based on the construction-driven minimum maintenance unit constraint characteristic data, perform a construction-constrained minimum maintenance unit risk status analysis on the construction status characteristic data of the minimum maintenance unit node, and generate construction-constrained minimum maintenance unit risk status data;

[0092] In this embodiment of the invention, risk status analysis of the minimum maintenance unit (MPU) nodes is performed based on the constraint characteristic data of the construction-driven minimum maintenance unit. The deviations between the construction status indicators of each node and the constraint standards are checked, and risk judgment rules, levels, and handling requirements are clearly defined without ambiguity. First, the construction status indicators of the corresponding nodes are checked one by one against each type of constraint parameter, and the deviation value and deviation percentage are calculated. The deviation percentage is accurate to 1%. For example, if the compliance rate of the driving parameter is 88%, the deviation percentage from the constraint standard of 95% is 7.4%. The risk judgment rules are clearly defined: 0% deviation percentage is no risk; 1%-3% is low risk; 4%-6% is medium risk; and 7% and above is high risk. Risk levels are only divided into these four categories. For different constraint types and risk levels, fixed handling measures are clearly defined: for low risk, only the deviation is recorded and checked daily; for medium risk, abnormal nodes are marked, and construction parameters are adjusted within 24 hours; for high risk, the operation of the corresponding node is immediately suspended, and a re-check is performed after rectification. During the analysis, the component attributes and driving attributes in the associated node-attribute feature data are used to clarify the causes of risks. For example, if the driving timing deviation exceeds the constraint, the cause is determined to be that the driving dependent node does not meet the standard. At the same time, the component evolution layer data of the BIM model is associated to verify the authenticity of the risk status, ensuring that the risk analysis fits the actual construction scenario, and generating risk status data of the minimum maintenance unit under construction constraints.

[0093] Step S44: Based on the risk status data of the minimum maintenance unit under construction constraints, conduct a supply adaptation assessment of the minimum maintenance unit under risk constraints, and generate supply adaptation assessment data of the minimum maintenance unit under risk constraints.

[0094] In this embodiment of the invention, a supply fit assessment of the minimum maintenance unit (MBU) under risk constraints is conducted based on the risk status data of the MBU. This involves compiling a component list and supply requirements for each unit, including component model, quantity, and supply cycle. Related component information from the associated maintenance component characteristic data is also included to clarify supply priorities, with core maintenance components having higher supply priority than ordinary maintenance components. Three assessment dimensions are defined, each with fixed assessment standards: Supply quantity fit (assessing the match between the actual supplyable quantity and the required quantity, with a 100% match rate); Supply cycle fit (assessing the deviation between the actual supply cycle and the required cycle, with a deviation not exceeding 1 day); and Supply quality fit (assessing the match between the quality of the supplied components and the quality standards in the unit component attribute characteristic data, with a match rate of 98% or higher). The assessment standards are adjusted based on the risk level: low risk maintains the original standard; medium risk has a supply cycle deviation of no more than 0.5 days; high risk has a supply quality fit rate of no less than 99% and no supply cycle deviation. The fit rate for each assessment dimension is calculated, accurate to 1%, and the overall fit rate is calculated as the average of the fit rates for the three dimensions. The adaptation levels are clearly defined: a comprehensive adaptation rate of 98% or higher is considered fully adapted; 90%-97% is considered basically adapted; 80%-89% is considered insufficiently adapted; and 79% or lower is considered completely incompatible. Only these four categories are used. During the analysis, the reasons for insufficient adaptation and complete incompatibility are clarified. For example, if the supply cycle deviation exceeds the constraint, the cause is determined to be that rectification at high-risk nodes leads to earlier supply demand. Finally, the evaluation dimensions, adaptation rates, adaptation levels, deviation data, causes, and optimization suggestions of each unit are integrated, categorized and stored by unit code, to generate supply adaptation evaluation data for the maintenance unit with the lowest risk constraint.

[0095] Step S45: Based on the risk-constrained minimum maintenance unit supply adaptation assessment data, perform an adaptation-constrained minimum maintenance unit maintenance impact analysis to generate adaptation-constrained minimum maintenance unit maintenance impact data;

[0096] In this embodiment of the invention, the process proceeds step by step, starting with each smallest maintenance unit. By combining the adaptation level, risk level, and maintenance attributes of each unit, the impact dimensions, degree of impact, and countermeasures are clearly defined to ensure clear analysis logic and explicit parameters. The impact dimensions are clearly defined to align with the entire maintenance workflow: maintenance progress impact, assessing the delay time caused by adaptation issues to maintenance procedures, accurate to the hour; maintenance cost impact, assessing the increased amount of maintenance asset consumption caused by adaptation issues; maintenance accuracy impact, assessing the reduction in maintenance accuracy compliance rate caused by adaptation issues; and maintenance efficiency impact, assessing the increase in maintenance man-hours caused by adaptation issues, accurate to the hour. Combining the adaptation level and risk level, the maintenance process and maintenance cycle of maintenance nodes are linked to clarify the core logic of the impact, such as insufficient adaptation leading to frequent replacement of maintenance components, increasing maintenance costs and man-hours. Simultaneously, BIM model maintenance simulation data is used to verify the rationality of the impact analysis results, ensuring consistency with actual maintenance scenarios and generating maintenance impact data for the smallest maintenance unit with adaptation constraints.

[0097] Step S46: Based on the minimum maintenance unit node construction status characteristic data, construction constraint minimum maintenance unit risk status data, risk constraint minimum maintenance unit supply adaptation assessment data, and adaptation constraint minimum maintenance unit maintenance impact data, perform minimum maintenance unit link constraint status characteristic analysis to generate minimum maintenance unit link constraint status characteristic data.

[0098] In this embodiment of the invention, the related nodes of the data are sorted out. Construction status data is the foundation, risk status data is the constraint risk support, supply adaptation data is the supply-side support, and maintenance impact data is the maintenance-side support. The four are accurately linked through the smallest maintenance unit identification code to form a complete link constraint system. The three core characteristics of the link constraint status are defined: link coordination, which assesses the coordination and matching degree of construction status, risk control, supply adaptation, and maintenance work, accurate to 1%, and the coordination and matching degree is calculated based on the average compliance rate of the four types of data; link stability, which assesses the stability of the operation of each node in the link, constraint execution, supply adaptation, and maintenance, with no abnormalities indicating stability, 1-2 minor abnormalities indicating basic stability, 3-4 abnormalities indicating instability, and 5 or more abnormalities indicating extreme instability; and link adaptability, which assesses the adaptability of the entire link with construction-driven constraints, BIM model dynamic updates, and asset management requirements, with an adaptability of 95% or above indicating full adaptability, 85%-94% indicating basic adaptability, 75%-84% indicating insufficient adaptability, and 74% or below indicating complete incompatibility. During the analysis, abnormal data of each link node is checked, weak links in the link constraint status are identified, related driver dependency data are correlated, the impact range of the weak links on the entire link is determined, and the link constraint status characteristic data of the smallest maintenance unit is generated.

[0099] Furthermore, step S5 includes the following steps:

[0100] Step S51: Analyze the supply demand of track component foundations based on the construction-driven minimum maintenance unit node-attribute characteristic data, and generate track component foundation supply demand data;

[0101] In this embodiment of the invention, a component list for each minimum maintenance unit is compiled, specifying the model, quantity, material, and quality standards of each component. Related component information is linked to the component association feature data, distinguishing between core and ordinary maintenance components, with core maintenance components receiving priority in demand calculation. Combining the maintenance cycle, replacement cost, and asset consumption rate in the node-attribute feature data, the basic supply quantity for each component is calculated. The supply quantity is calculated based on the total consumption within the maintenance cycle plus a 10% reserve, with the reserve ratio remaining constant. Supply specifications are clearly defined, strictly matching the size and material parameters in the component attribute features. Each component installed during track construction has corresponding supply information and a supply code. A basic supply cycle is determined: the supply cycle for core maintenance components is fixed at once every 7 days, and the supply cycle for ordinary maintenance components is fixed at once every 15 days, accurate to the day. Basic supply costs are calculated by multiplying the unit price in the component asset attribute features by the supply quantity, accurate to the yuan. Cost accounting standards and allocation methods are also clearly defined, with cost allocation calculated separately for each minimum maintenance unit. Verify the consistency between supply demand and component attributes and maintenance cycles to ensure that supply information, specifications, and cycles align with maintenance needs and asset management requirements, and eliminate unreasonable demand parameters. Integrate the component supply demands of each smallest maintenance unit, classify and store them according to component type and unit code, and generate basic supply demand data for track components.

[0102] Step S52: Based on the minimum maintenance unit link constraint state characteristic data, perform dynamic change demand adjustment processing on the track component basic supply demand data to generate dynamic supply demand data for track components.

[0103] In this embodiment of the invention, the link constraint status of each minimum repair unit is analyzed, focusing on link stability and adaptability levels. Simultaneously, the risk level in the risk status data is used to determine the adjustment priority: units with extremely unstable links and high risks are adjusted first, while units with stable links and no risk are not adjusted. Adjustment rules are clearly defined, setting fixed adjustment ranges according to link constraint status and risk level: For extremely unstable links and high risks, the supply quantity of core repair components increases by 20%, and the supply cycle is shortened to once every 5 days; the supply quantity of ordinary repair components increases by 15%, and the supply cycle is shortened to once every 12 days. For unstable links and medium risks, the supply quantity of core repair components increases by 10%, and the supply cycle is shortened to once every 6 days; the supply quantity of ordinary repair components increases by 8%, and the supply cycle is shortened to once every 14 days. For basically stable links and low risks, the supply quantity of core repair components increases by 5%, the supply quantity of ordinary repair components remains unchanged, and the supply cycle maintains the basic standard. For stable links and no risk, the supply demand is not adjusted. During the adjustment process, the supply cost is adjusted simultaneously, calculated synchronously according to the adjustment ratio of the supply quantity, accurate to the yuan, and the reasons for cost adjustments and recording standards are clearly defined. To address supply compatibility issues caused by insufficient link adaptation, supply specifications are adjusted synchronously to ensure precise matching between supplied components and link constraints and component attributes. For example, if insufficient link adaptation leads to deviations in component installation accuracy, the deviation in supplied component specifications is adjusted to within ±1 mm. After adjustment, the compatibility of the adjusted supply demand with the link constraint status and risk status is verified to ensure that the adjustment range is reasonable and there is no over-adjustment or under-adjustment. Finally, the adjusted supply demand data of each unit is integrated, and parameters such as supply quantity, cycle, specifications, and cost are updated. The data is then categorized and stored according to unit code and component type to generate dynamic supply demand data for track components.

[0104] Step S53: Perform dynamic supply tracking processing on the dynamic supply demand data of track components to generate dynamic supply tracking data for track construction;

[0105] In this embodiment of the invention, tracking indicators for each stage are clearly defined, and fixed monitoring standards are set for each stage: In the supply plan issuance stage, the issuance time and completeness of the issuance content are tracked, with the issuance time accurate to the hour and the content including all parameters such as supply quantity, specifications, and cycle; In the component transportation stage, transportation time, transportation loss rate, and transportation location are tracked, with the transportation time accurate to the hour and the loss rate not exceeding 0.5%, and the transportation location tracked and recorded in real time; In the on-site receiving stage, the receiving time, received quantity, and quality inspection results are tracked, with the receiving time accurate to the hour and the received quantity deviating from the supply plan not exceeding ±1%, and the quality inspection meeting the component attribute standards; In the warehousing stage, storage time, storage loss, and inventory quantity are tracked, with the storage time not exceeding 50% of the supply cycle, storage loss not exceeding 0.3%, and inventory quantity checked daily; In the requisition and consumption stage, the requisition time, requisition quantity, and consumption destination are tracked, with the requisition quantity deviating from maintenance requirements not exceeding ±2%, and the consumption destination clearly corresponding to the smallest maintenance unit and specific maintenance node. The tracking frequency is determined: core repair components are tracked every 2 hours for transportation and inventory status, with full-process tracking data summarized daily; ordinary repair components are tracked every 4 hours for transportation and inventory status, with full-process tracking data summarized every 2 days. An anomaly handling mechanism is established: if transportation losses exceed standards, supply is immediately replenished and loss costs are calculated; if quality inspection fails, the components are returned on the spot and redistributed; if inventory is insufficient, an emergency supply process is initiated, with the emergency supply cycle shortened to once every 3 days. During the tracking process, the BIM model asset management layer data is linked, and supply tracking information is updated synchronously to ensure that the tracking data is dynamically updated in sync with the BIM model, generating dynamic supply tracking data for rail construction. This data includes full-process tracking indicators, anomaly information, handling measures, and data summary results, ensuring that the supply process is traceable and controllable.

[0106] Step S54: Perform dynamic asset management processing based on the dynamic supply tracking data of rail construction to generate dynamic asset management data for the rail construction period.

[0107] In this embodiment of the invention, dynamic asset management during the track construction period is performed based on dynamic supply tracking data. The management process proceeds step-by-step for each minimum maintenance unit, covering four key stages: asset accounting, asset monitoring, asset optimization, and asset archiving. Clear management standards, parameters, and implementation procedures are defined for each stage to ensure precise and logically rigorous management. In the asset accounting stage, accounting is performed separately for each minimum maintenance unit. Combining supply costs and loss costs from the supply tracking data with depreciation standards from the associated component asset attribute characteristics, the cumulative asset consumption, residual asset value, and asset utilization rate are calculated for each unit and each type of component. The asset utilization rate is calculated as the ratio of consumption quantity to supply quantity, accurate to 1%. Asset values ​​are accurate to the yuan. The accounting cycle is fixed at once per month, and an asset accounting report is generated simultaneously, clearly defining the accounting basis and results. In the asset monitoring phase, asset consumption rate, supply cost deviation, and asset utilization rate are monitored in real time, with monitoring thresholds set: asset consumption rate deviation should not exceed ±8%, supply cost deviation should not exceed ±1000 yuan, and asset utilization rate should not be lower than 85%. If any threshold is exceeded, the cause is immediately investigated, and supply demand and asset control measures are adjusted. Simultaneously, the BIM model is dynamically updated, and asset monitoring data is synchronized in real time to ensure asset status visibility. In the asset optimization phase, supply demand and asset configuration are optimized by combining link constraint status, supply tracking data, and asset monitoring results. For example, for components with excessively low asset utilization, the supply quantity is reduced by 10%; for components with excessively fast consumption rates, the supply cycle and inventory reserves are optimized. At the same time, the asset cost allocation method is optimized to reduce management costs. In the asset archiving phase, asset accounting data, monitoring data, optimization records, and supply-related data for each smallest maintenance unit are organized and archived according to unit code and construction stage. Archiving standards and retention requirements are clearly defined, and archived data must include relevant information throughout the asset's entire lifecycle to ensure traceability. During management, the consistency between asset data and supply tracking data, as well as node-attribute characteristic data, is verified, and redundant and conflicting data is eliminated to ensure the accuracy of asset data. By integrating the asset accounting, monitoring, optimization, and archiving data of each smallest maintenance unit, and storing them according to construction stage and asset type, dynamic asset management data during the rail construction period is generated. This data includes the full life cycle parameters, management records, optimization measures, and archiving information of each unit's assets, providing accurate data support for asset control, subsequent operation and maintenance, and decision-making during the rail construction period, and realizing refined asset management driven by dynamic updates of the BIM model.

[0108] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0109] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for managing asset data during the construction phase of rail transit driven by dynamic updates of BIM models, characterized in that, Includes the following steps: Step S1: Obtain track construction data and track terrain data stored on the GIS platform; Based on track construction data and track terrain data, the components and terrain of the track construction are aligned to generate track component terrain alignment data; Step S2: Establish a dynamic BIM model for track construction using terrain alignment data of track components; Step S2 includes the following steps: Step S21: Perform track construction stage analysis on the track construction data to generate track construction stage data; Step S22: Perform construction stage component construction simulation processing on the terrain alignment data of track components based on the track construction stage data to generate construction stage component construction simulation data; Step S23: Perform dynamic process association node analysis on components at each stage based on the construction stage component construction simulation data to generate stage component dynamic process association node data, and extract stage component dynamic process association node feature data from the stage component dynamic process association node data; Step S24: Perform dynamic process association node analysis on components at each stage based on the construction stage data. Step S25: Analyze the correlation characteristics of component construction organization at each stage using simulation data, generating stage component construction organization correlation characteristic data; Step S26: Establish a dynamic BIM model for track construction based on the dynamic evolution characteristic terrain data of track components; Step S3: Analyze the construction-driven minimum maintenance unit node and attribute characteristics based on the dynamic BIM model for track construction, generating construction-driven minimum maintenance unit node-attribute characteristic data; Step S3 includes the following steps: Step S3 Step S31: Perform track-driven function analysis on the dynamic BIM model of track construction to generate track-driven function data; perform track-driven dependency analysis based on the track-driven function data to generate track-driven dependency data; establish linkage units for track construction driven by the track-driven function data and track-driven dependency data to generate track construction driven unit data; Step S32: Extract track maintenance component feature data and track maintenance-free component feature data from the track component hierarchical feature data respectively, and perform track maintenance component association feature analysis based on the track maintenance component feature data and track maintenance-free component feature data to generate track maintenance component association feature data; Step S33: Based on the track maintenance component association feature data... Step S34: Based on the track construction drive unit data, establish the track minimum maintenance unit associated with maintenance components and generate track minimum maintenance unit data; Step S35: Based on the track component hierarchical feature data, perform minimum maintenance unit attribute feature analysis on the track minimum maintenance unit data and generate minimum maintenance unit attribute feature data; Based on the track drive function data and track drive dependency data corresponding to the track construction drive unit data, establish the minimum maintenance unit structure nodes of the track minimum maintenance unit data for the construction drive branch and generate construction drive minimum maintenance unit structure node data;Step S4: Transmit the attribute feature data of the minimum maintenance unit to the construction-driven minimum maintenance unit structural node data for attribute feature mapping processing of each structural node, generating construction-driven minimum maintenance unit node-attribute feature data; Step S5: Perform minimum maintenance unit link constraint state feature analysis based on the construction-driven minimum maintenance unit node-attribute feature data, generating minimum maintenance unit link constraint state feature data; Step S4 includes the following steps: Step S41: Perform minimum maintenance unit node construction state feature analysis based on the construction-driven minimum maintenance unit node-attribute feature data, generating minimum maintenance unit node construction state feature data; Step S42: Perform construction-driven minimum maintenance unit constraint feature analysis based on the construction-driven minimum maintenance unit node-attribute feature data, generating construction-driven minimum maintenance unit constraint feature data; Step S43: Perform construction-constrained minimum maintenance unit risk state analysis on the minimum maintenance unit node construction state feature data based on the construction-driven minimum maintenance unit constraint feature data. Step S44: Generate risk status data for the minimum maintenance unit under construction constraints; Step S45: Based on the risk status data of the minimum maintenance unit under construction constraints, conduct a supply adaptation assessment of the minimum maintenance unit under risk constraints, and generate supply adaptation assessment data for the minimum maintenance unit under risk constraints; Step S46: Based on the supply adaptation assessment data of the minimum maintenance unit under risk constraints, conduct maintenance impact analysis of the minimum maintenance unit under adaptation constraints, and generate maintenance impact data for the minimum maintenance unit under adaptation constraints; Step S5: Based on the construction status characteristic data of the minimum maintenance unit node, the risk status data of the minimum maintenance unit under construction constraints, the supply adaptation assessment data of the minimum maintenance unit under risk constraints, and the maintenance impact data of the minimum maintenance unit under adaptation constraints, conduct link constraint status characteristic analysis of the minimum maintenance unit, and generate link constraint status characteristic data of the minimum maintenance unit; Step S6: Based on the construction-driven minimum maintenance unit node-attribute characteristic data and the link constraint status characteristic data of the minimum maintenance unit, conduct dynamic management analysis of assets during the track construction period, and generate dynamic management data of assets during the track construction period.

2. The method for managing railway construction phase asset data driven by dynamic BIM model updates according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain track construction data and track terrain data stored on the GIS platform; Step S12: Perform track scene terrain feature analysis on the track terrain data to generate track scene terrain feature data; Step S13: Perform track construction component parsing processing on the track construction data to generate track construction component data; Step S14: Perform track component hierarchical feature analysis on the track construction component data to generate track component hierarchical feature data; Step S15: Map the track component hierarchical feature data to the track scene terrain feature data to perform track component and terrain spatial alignment processing to generate track component terrain alignment data.

3. The method for managing railway construction phase asset data driven by dynamic BIM model updates according to claim 2, characterized in that, Step S12 includes the following steps: Step S121: Perform spatial vector feature analysis on the track terrain data to generate spatial vector feature data on the track terrain; Step S122: Perform spatial vector region segmentation on the spatial vector feature data on the track terrain to obtain spatial vector segmentation data on the track terrain, and label the terrain region attribute feature data of the spatial vector segmentation data on the track terrain; Step S123: Perform terrain feature analysis on the track scene based on the spatial vector segmentation data on the track terrain and the corresponding terrain region attribute feature data to generate terrain feature data on the track scene.

4. The method for managing railway construction phase asset data driven by dynamic BIM model updates according to claim 2, characterized in that, The track component hierarchical feature data mentioned in step S14 includes track component functional feature data, track component structural feature data, track component engineering attribute feature data, track component construction status feature data, track component spatial feature data, and track component asset feature data.

5. The method for managing railway construction phase asset data driven by dynamic BIM model updates according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Analyze the supply demand of track component foundations based on the construction-driven minimum maintenance unit node-attribute characteristic data to generate track component foundation supply demand data; Step S52: Adjust the supply demand of maintenance components based on the minimum maintenance unit link constraint state characteristic data to generate track component dynamic supply demand data; Step S53: Track construction dynamic supply tracking data is generated based on the track component dynamic supply demand data; Step S54: Track construction period asset dynamic management data is generated based on the track construction dynamic supply tracking data.

Images