Rail transit station digital twin virtual-real consistency mapping and operation and maintenance closed loop method

Abstract

The application discloses a rail transit station digital twin virtual-real consistency mapping and operation and maintenance closed loop method, relates to the field of rail transit intelligent operation and maintenance, and has the following steps: operation and maintenance oriented reconstruction is carried out on a construction period completion BIM model, a digital twin model matched with entity granularity is generated, an operation and maintenance exclusive six-level coding system is constructed, virtual-real component identity anchoring is completed through three-level topological benchmark verification, and a coding feature vector is generated; point clouds are differentially collected according to four functional partition differences, and geometric feature sets of entity and model components are extracted in layers; a ternary joint optimization objective function fusing geometry, coding and topological constraints is constructed, an optimal rigid body transformation matrix is solved through SVD iteration, global virtual-real mapping is completed, a ternary dimension deviation verification and hierarchical correction mechanism is established, and a full life cycle operation and maintenance closed loop is realized. The application provides a high-precision and high-reliability digital twin foundation for the field of rail transit intelligent operation and maintenance, and improves the station operation and maintenance safety and intelligent level.

Description

Technical Field

[0001] This invention belongs to the field of intelligent operation and maintenance technology for rail transit, specifically involving a digital twin mapping of rail transit stations to ensure consistency between virtual and real data and a closed-loop operation and maintenance method. Background Technology

[0002] Currently, BIM technology is widely used in the construction phase of urban rail stations to complete the entire process of design, construction and completion. The core prerequisite for ensuring the successful implementation of digital twin models in operation and maintenance is to smoothly transform the completed BIM model during the construction phase into the operation and maintenance phase, build a digital twin model that accurately matches the physical station, and achieve a highly reliable and consistent mapping between the physical entity and the virtual model.

[0003] Currently, most mainstream virtual-real consistency mapping technologies are based on three-dimensional geometric feature matching. By collecting three-dimensional laser point cloud data of the physical scene, the geometric features of the physical components and BIM model components are extracted to complete spatial registration, thereby achieving positional alignment between the virtual model and the physical entity. This technology has already been applied in engineering projects in static scenes in fields such as architecture and municipal engineering.

[0004] However, existing technologies still have the following core technical shortcomings and unresolved issues when applied to urban rail station operation and maintenance scenarios: Firstly, the existing BIM coding system in the urban rail transit industry is not adaptable enough to the virtual-real mapping scenario of operation and maintenance, and has not formed a customized coding support that can be deeply integrated into the entire virtual-real mapping process.

[0005] The existing BIM coding system provides a standardized foundation for the unified coding connection between the construction and operation and maintenance phases of urban rail transit stations. However, its classification logic is mainly built around the general management needs of the entire construction process. For the specific application scenarios of virtual-physical mapping in the operation and maintenance phase of urban rail transit stations, the existing BIM coding system lacks targeted customized design and adaptability optimization in core needs such as hierarchical control of operation and maintenance safety levels, accurate anchoring of component spatial locations, and full life cycle identity traceability and registration optimization linkage. It is difficult to directly transform into semantic features that can be deeply involved in registration optimization and deviation verification, and cannot provide exclusive underlying support for the accurate matching and identity continuation of virtual and physical components in the operation and maintenance phase.

[0006] Secondly, the identification of physical entities and digital models lacks automated and highly reliable means, making it impossible to establish a stable one-to-one correspondence.

[0007] The existing methods for anchoring the identities of virtual and physical components largely rely on manual binding or simple name matching, which is inefficient, error-prone, and cannot meet the large-scale operation and maintenance needs of urban rail stations with their massive number of components. For a large number of standardized components of the same model and name in the station (such as multiple platform screen doors, multiple fans / distribution cabinets of the same model, etc.), relying solely on names cannot achieve accurate differentiation, nor is a matching and verification mechanism built in conjunction with the station's professional topology relationship, which makes it very easy for cross-component mismatches to occur. At the same time, due to the insufficient adaptability of the existing coding system to the operation and maintenance scenario, after the component is replaced or moved, the identity correspondence between the entity and the model cannot be updated synchronously and automatically, ultimately leading to long-term confusion in the identity correspondence between virtual and physical components.

[0008] Third, purely geometry-driven spatial registration schemes have poor scene adaptability, weak anti-interference ability, insufficient stability under complex working conditions, and are prone to mismatch and global registration drift.

[0009] Urban rail stations contain a large number of standardized components of the same type and geometric features. Existing pure geometric registration methods rely solely on geometric features for matching, which cannot effectively distinguish components with highly similar features. Furthermore, they do not combine relevant features in BIM coding with the topological relationships between components to construct registration constraints, making it easy for cross-component mismatches to occur. At the same time, in complex scenarios such as obtaining sparse point clouds during track maintenance windows and severe occlusion from equipment rooms, pure geometric registration has insufficient anti-interference capabilities and is prone to global registration drift, failing to meet the high-precision and high-reliability registration requirements of core components related to train operation safety and passenger safety during the operation and maintenance phase.

[0010] Fourth, the lack of a closed-loop management mechanism for consistency between the virtual and physical systems throughout the entire operation and maintenance lifecycle makes it impossible to guarantee the long-term effectiveness of the digital twin model.

[0011] Existing virtual-physical mapping schemes are mostly one-time registrations during the construction and completion phases, lacking the ability to continuously and automatically verify the consistency between virtual and physical entities. They also fail to construct a closed-loop management mechanism covering deviation quantification, hierarchical verification, and adaptive correction for high-frequency dynamic changes such as component replacement, relocation, and modification during the operation and maintenance phase of urban rail stations. As a result, the digital twin model gradually becomes disconnected from the physical entity over time, turning into a "static dead model" that cannot adapt to dynamic operation and maintenance needs, and cannot provide a stable and accurate digital foundation for long-term intelligent operation and maintenance applications.

[0012] In summary, existing digital twin virtual-real mapping methods can no longer meet the actual needs of urban rail transit station operation and maintenance for consistent dynamic synchronization throughout the entire lifecycle. There is an urgent need to design a digital twin virtual-real consistency mapping method for rail transit stations that can adapt to the virtual-real mapping scenario of operation and maintenance, build a dedicated coding scheme, realize automated and highly reliable identity anchoring of virtual and real components throughout the entire lifecycle, integrate geometric-coding-topological ternary constraints to complete high-precision registration in complex scenarios, and build a closed-loop operation and maintenance system for virtual-real consistency covering the entire operation and maintenance cycle. Summary of the Invention

[0013] This invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a method for consistent mapping of digital twins of rail transit stations and closed-loop operation and maintenance.

[0014] The technical solution of this invention is: a method for mapping virtual and real consistency of digital twins for rail transit stations and a closed-loop operation and maintenance system. The virtual aspect refers to the digital twin model of the rail transit station and its various BIM model components, while the real aspect refers to the physical entities within the operation and maintenance scope of the rail transit station and their physical components. The method includes the following steps: S1. Undertake the construction phase of rail transit stations' completed BIM models, as-built data, and operation and maintenance ledgers, and complete the reconstruction of the station's digital twin model with an operation and maintenance orientation; S2. Based on the station's digital twin model and physical entities, construct a six-level coding system dedicated to operation and maintenance, build a three-level topology benchmark, complete the identification of virtual and physical components, and generate a coding feature vector that is consistent between virtual and physical components; S3. Construct a geometric feature set for the solid components, construct a geometric feature set for the BIM model components, and encapsulate the two to form a common geometric feature set; S4. Based on the geometric feature set of virtual and real components, calculate the initial transformation matrix by the centroid coordinate deviation and spatial orientation deviation of the feature points of virtual and real components; based on the geometric nearest neighbor matching rule, select the set of effective matching point pairs between the solid components and the model components; construct a ternary joint optimization objective function that integrates geometric feature constraints, coded feature constraints, and urban rail transit full-profession hierarchical topological constraints based on a three-level topological benchmark; with the initial transformation matrix as the starting point of the iteration and the ternary joint optimization objective function as the core, solve the optimal rigid body transformation matrix of the current iteration round through singular value decomposition to complete the accurate mapping between virtual and real components; S5. Based on the ternary constraints of the ternary joint optimization objective function, quantitatively calculate the consistency deviation of a single component in three dimensions: geometry, coding features, and topological relationships; set a threshold according to the security level corresponding to the component's security level code to perform single-dimensional verification; if the verification fails, trigger deviation classification; execute differentiated adaptive correction strategies for different deviation levels to complete the deviation closed-loop handling, and synchronously update the entity-model mapping relationship and operation and maintenance ledger.

[0015] Furthermore, step S1 involves taking over the completed BIM model, as-built data, and operation and maintenance records of the rail transit station during the construction phase, and completing the reconstruction of the station's digital twin model with an operation and maintenance orientation. The specific process is as follows: S11. Remove redundant features from the completed BIM model during the construction period that are irrelevant to operation and maintenance; S12. To address the mismatch between the granularity of the completed BIM model during the construction phase and the control requirements during the operation and maintenance phase, the physical components obtained by disassembling the station's physical entities according to the boundary of operation and maintenance functions are used as the sole benchmark. Standardized granularity adaptation processing is performed on the completed BIM model during the construction phase to achieve complete granularity matching between the BIM model components and the physical components, resulting in a matched BIM model. S13. Supplement the operation and maintenance phase of newly added components not included in the as-built BIM model during the construction period; S14. Based on the matching BIM model and newly added components during the operation and maintenance period, reconstruct the model to generate an operation and maintenance-oriented digital twin model of the station.

[0016] Furthermore, the standard form of the six-level coding system specific to operations and maintenance in step S2 is as follows: Project type code - Professional system code - Spatial location code - Security level code - Installation time code - Instance number code.

[0017] Furthermore, the three-level topology benchmark in step S2 is described in detail below: First, the primary topology is the core component, including the main structural columns, side walls, and track foundation of the station. The primary topology serves as the spatial positioning benchmark for the entire station. Then, the secondary topology is the backbone component, including the main fan cabinet in the ventilation room, the power distribution cabinet in the power supply room, the signal cabinet, and the fire control panel. The secondary topology is the positioning reference for the backbone. Finally, the three-level topology consists of end components, including sensors, lighting, access control, and end valves.

[0018] Furthermore, the identification of virtual and real components in step S2 is anchored in the following specific process: First, based on the names of each BIM model component in the station's digital twin model, the professional system code and spatial location code in the operation and maintenance-specific six-level code, we initially screened physical entities with similar names and locations to form initial corresponding candidate pairs. Then, the initial corresponding candidate pairs are checked again by combining the three-level topology benchmark to eliminate false matches and verify that the topology neighbor benchmark components of the entity components and the BIM model components are completely consistent. After the verification is passed, the identity belonging of the entity components and the BIM model components is established in a one-to-one correspondence anchoring relationship. The entity components and BIM model components with the anchored identity correspondence relationship are added to the station anchoring component set. Finally, the bound operation and maintenance exclusive level 6 code serves as a unique identifier for the entire operation and maintenance lifecycle of the component. After the component is replaced or moved, the operation and maintenance exclusive level 6 code and the corresponding identity relationship are updated synchronously.

[0019] Furthermore, in step S2, a consistent virtual and real encoded feature vector is generated. The specific process is as follows: First, core coding attributes are extracted. For components in the station anchoring component set, based on the component operation and maintenance exclusive six-level code, the project type code, professional system code, spatial location code, safety level code, and installation time code are extracted as the core coding attributes of the component. The instance number code is removed to avoid the unique identifier from disrupting the matching logic of components of the same model.

[0020] Then, the encoded feature vector is generated by vectorizing the core encoded attributes of the component through one-hot encoding, and generating the component encoded feature vector through weighted concatenation. C The coded feature vectors of the physical components are completely consistent with those of the corresponding BIM model components. When a component is replaced or moved, the coding is updated, and the coded feature vector is also updated to reflect the corresponding changes in the core coding attributes. C The calculation formula is as follows:

[0021] in, This is a vector concatenation operator that concatenates five weighted vectors in a fixed order into a single vector; one-hot(m) is the one-hot encoded vector of the m-th core encoded attribute of the component; oh m The weight coefficient of the m-th core coded attribute is determined using the Analytic Hierarchy Process (AHP) in the urban rail transit operation and maintenance field, satisfying the following conditions: ; Finally, the coded feature vectors are bound to the entity components and BIM model components in the station anchored component set that have been anchored to the identity correspondence.

[0022] Furthermore, in step S4, based on the geometric feature set of the virtual and real components, the initial transformation matrix is ​​calculated through the centroid coordinate deviation and spatial orientation deviation of the feature points of the virtual and real components. The specific process is as follows: First, calculate the centroids of all feature points in the geometric feature set of the solid component and the geometric feature set of the BIM model component, respectively. : , in, These represent the number of feature points in the geometric feature sets of solid components and BIM model components, respectively. These are the geometric feature sets of solid components and BIM model components, respectively. The, the The coordinates of the feature points.

[0023] Then, calculate the initial translation vector. : ; in, The translation amount along the X-axis (station mileage direction) of the right-hand Cartesian coordinate system for the unified station project. This represents the translation along the Y-axis (the horizontal width direction of the station). This represents the translation along the Z-axis (elevation direction).

[0024] Next, calculate the initial rotation matrix. : From the feature points of the geometric feature sets of both solid components and model components, high-confidence feature point sets corresponding to the mounting reference plane of the components are selected. Principal component analysis (PCA) is then used to calculate the unit normal vector of the reference plane of the solid components. The unit normal vector of the reference plane of the model component Calculate the axis of rotation Calculate the rotation angle ; Calculate the initial rotation matrix using the Rodriguez formula ,in, It is a 3×3 identity matrix. For the axis of rotation The corresponding antisymmetric matrix, ; Finally, determine the initial transformation matrix. : Will Synthesize a 4×4 initial transformation matrix according to the standard format. Then the initial transformation matrix for: .

[0025] Furthermore, in step S4, based on the geometric nearest neighbor matching rule, a set of valid matching point pairs between the solid component and the BIM model component is selected. The specific process is as follows: First, in the geometric feature set of the solid component and the geometric feature set of the BIM model component, the coordinates of the feature points of the BIM model component are... Apply the initial transformation matrix Obtain the transformed coordinates ; Then, among the feature points of the corresponding solid component, select the point with the smallest Euclidean distance, whose coordinates are... , forming matching point pairs ; Finally, outlier point pairs with a distance greater than the component accuracy threshold are removed, and all valid matching point pairs are finally determined.

[0026] Furthermore, in step S4, a ternary joint optimization objective function is constructed that integrates geometric feature constraints, coding feature constraints, and hierarchical topological constraints based on a three-level topological benchmark for urban rail transit across all disciplines. The specific process is as follows: Let the current one be the first...l ( l ≥1) In the first iteration, the ternary joint optimization objective function is... as follows: ; In the formula: Firstly, This represents the 4×4 homogeneous rigid body transformation matrix used to solve for the objective function of the subsequent ternary joint optimization. M l ; Secondly, The geometric feature loss term quantifies the relative spatial deviation between feature points of the solid component and the BIM model component relative to the allowable threshold of the component's safety level, and then performs dimensionless normalization. ; in, The first in the set of station anchoring components The first solid component The coordinates of the feature points For the corresponding number The first BIM model component The feature points are transformed by the optimal transformation matrix in the previous round. M l-1 (when l When =1, M l-1 = M 0) The coordinates of the transformed feature points, For the first The number of matching point pairs for each component For the first The maximum permissible threshold for geometric deviation for each component is determined according to the safety level: 0.5mm for Level 1 components, 2mm for Level 2 components, and 5mm for Level 3 components. The total number of component pairs in the station anchoring component set; Thirdly, The loss term for the encoded features is normalized based on cosine similarity. ; in, The first in the set of station anchoring components The encoded feature vector of each entity component, This refers to the coded feature vector of the corresponding BIM model component in the station anchor component set. Under normal matching conditions, the coded feature vectors of the entity component and the BIM model component are completely identical. It does not affect iteration, but the loss term increases sharply when there is a mismatch, and it penalizes mismatches across components; Fourthly, To address the hierarchical topological constraint loss term across all urban rail transit disciplines, ensuring the relative topological relationships between components remain unchanged and avoiding global registration drift, dimensionless normalization is applied. , in, For the first A set of third-level topological neighborhood components for each component. The first in the set of station anchoring components The centroid coordinates of each solid component For the first The BIM model component corresponding to each physical component undergoes the optimal transformation matrix from the previous round. M l-1 Transformed centroid coordinates For the first In the topological neighborhood set of the nth entity component The centroid coordinates of each component For the first In the topological neighborhood set of the nth entity component The BIM model component corresponding to each component undergoes the optimal transformation matrix from the previous round. M l-1 Transformed centroid coordinates For the first The number of components in the three-level topological neighborhood set corresponding to each component. For the first The maximum allowable threshold for the topological relative distance deviation corresponding to each component is determined according to the safety level: 0.3mm for Level 1 components, 1mm for Level 2 components, and 3mm for Level 3 components. Fifthly, The weighting coefficients for the three types of loss terms are respectively, satisfying... The weights are adaptively adjusted according to the operating conditions: under normal operation and maintenance conditions, Prioritize ensuring the accuracy of geometric feature matching; Sunroof maintenance / component replacement conditions: Prioritize ensuring stable coded identity matching and topological relationships; driving safety management conditions: Prioritize ensuring the stability of global topology relationships to avoid registration drift.

[0027] Furthermore, in step S4, the initial transformation matrix is ​​used as the starting point for iteration, and the ternary joint optimization objective function is used as the core. The optimal rigid body transformation matrix for the current iteration is solved through singular value decomposition to complete the precise mapping between virtual and real. The specific process is as follows: First, calculate the centroid-free coordinates. For each feature point in the matching point pair set, perform centroid removal to eliminate the influence of translation on the rotation matrix calculation. The calculation formula is: ; in, The first matching point in the set of matching points after centroid removal is respectively Coordinates of feature points of individual solid components and BIM model components; For the first The coordinates of the feature points; For the first The feature points of each BIM model component are transformed by the optimal transformation matrix in the previous round. M l-1 The coordinates of the transformed feature points; The optimal transformation matrix of the previous round for the feature points of the BIM model components. M l-1 The transformed feature point coordinates are used to calculate the centroid of the feature points in the geometric feature set of the BIM model component. Then, construct a 3×3 covariance matrix. : ; Then, regarding the covariance matrix H Perform SVD singular value decomposition. The SVD decomposition formula is: ; in, All are 3×3 orthogonal matrices, satisfying It is a 3×3 diagonal matrix, with diagonal elements These are the singular values ​​of the covariance matrix, arranged in descending order; Then, the optimal rotation matrix is ​​solved based on the SVD decomposition results. , The basic solution formula is ;right To avoid model mirror distortion, a forced correction to the right-handed coordinate system is performed using the following formula: Perform orthogonality verification to confirm. The error does not exceed Ensure no scaling or shearing distortion; perform matrix verification: verification The error does not exceed To ensure there is no mirror distortion; if the verification fails, return to re-filter valid matching point pairs and iterate again. Then, based on the optimal rotation matrix obtained in this round... Solve for the corresponding optimal translation vector. This achieves complete alignment of the centroids of all feature points in the geometric feature set of the solid component with those in the BIM model component: ;Will , Synthesize according to standard format. 4×4 optimal transformation matrix for multiple iterations ; Finally, determine whether the current iteration has reached the global optimum, decide whether to terminate the iteration, and calculate the ternary joint optimization objective function values ​​for the current and previous iterations. relative rate of change The iteration will terminate if any of the following conditions are met: Condition 1, ,Right now When the value is less than the convergence threshold, the objective function converges and the global optimum is reached; Condition 2, iteration rounds This maximizes the number of iterations and avoids infinite loops.

[0028] The beneficial effects of this invention are as follows: This invention, combined with the specific needs of virtual-physical mapping in operation and maintenance, constructs a six-level coding system specifically for operation and maintenance. It specifically supplements and optimizes the design of dedicated fields related to hierarchical control of operation and maintenance security levels, precise anchoring of component spatial locations, and full lifecycle identity traceability, completing customized optimization to adapt to the entire process of virtual-physical mapping. Through one-hot encoding vectorization conversion, the coding is transformed from traditional auxiliary information tags into quantitative features that can deeply participate in registration optimization and deviation verification, providing underlying support for accurate matching of virtual and physical components and full lifecycle identity continuation during the operation and maintenance phase, and also realizing a smooth connection between the coding system during the construction and operation and maintenance phases.

[0029] This invention constructs a fully automated identity anchoring method that employs initial screening and matching of coded attributes and secondary verification based on a three-level topological benchmark. This method replaces the inefficient traditional manual binding and simple name matching approach, significantly improving anchoring efficiency and fully adapting to the large-scale operation and maintenance needs of urban rail stations with their massive number of components. Through a three-level topological neighborhood consistency verification mechanism, it fundamentally solves the problem of cross-component mismatch among a large number of standardized components of the same model and name within the station, establishing a stable one-to-one correspondence between virtual and physical components. Combined with the full lifecycle design of operation and maintenance-specific codes, it achieves synchronous and automated updates of identity correspondence after component replacement and relocation, completely avoiding the long-term chaos in the identity correspondence between virtual and physical components.

[0030] This invention constructs a ternary joint optimization objective function that integrates geometric feature constraints, coding feature constraints, and hierarchical topological constraints across all urban rail transit disciplines. The coding feature loss term and the hierarchical topological constraint loss term are deeply integrated into the iterative solution process of the rigid body transformation matrix. The coding feature loss term increases sharply during cross-component mismatches, effectively penalizing mismatches. The topological constraint loss term ensures that the relative topological relationships between components remain unchanged, avoiding global registration drift. Compared to traditional pure geometric registration schemes, this invention significantly improves registration accuracy in complex operation and maintenance scenarios and reduces component mismatch rates, stably meeting the high-precision and high-reliability registration requirements of core components related to driving safety and passenger safety.

[0031] This invention establishes a closed-loop operation and maintenance system covering the entire lifecycle of virtual-physical consistency, encompassing three-dimensional deviation quantification, single-dimensional verification of safety level matching, deviation classification judgment, and differentiated adaptive correction. This system achieves continuous and automated verification of virtual-physical consistency. For high-frequency dynamic change scenarios such as component replacement, relocation, and modification during the operation and maintenance phase of urban rail stations, it can automatically identify consistency deviations and match corresponding adaptive correction strategies, synchronously updating the entity-model mapping relationship and operation and maintenance ledger. This completely solves the problem of the model becoming disconnected from the physical entity over time after one-time registration in traditional solutions, preventing the digital twin model from becoming a static dead model that cannot adapt to dynamic needs. It provides a stable and accurate dynamic digital foundation for the long-term intelligent operation and maintenance application of urban rail stations. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 yes Figure 1 The detailed flowchart of S4 in China; Figure 3 yes Figure 1 The detailed flowchart of S5 in China. Detailed Implementation

[0033] The present invention will now be described in detail with reference to the accompanying drawings and embodiments: like Figure 1 to Figure 3 As shown, the digital twin mapping and operation and maintenance closed-loop method for rail transit stations, where the virtual part refers to the digital twin model of the rail transit station and its various BIM model components, and the real part refers to the physical entities within the operation and maintenance scope of the rail transit station and their physical components, includes the following steps: S1. Undertake the construction phase of rail transit stations' completed BIM models, as-built data, and operation and maintenance ledgers, and complete the reconstruction of the station's digital twin model with an operation and maintenance orientation; S2. Based on the station's digital twin model and physical entities, construct a six-level coding system dedicated to operation and maintenance, build a three-level topology benchmark, complete the identification of virtual and physical components, and generate a coding feature vector that is consistent between virtual and physical components; S3. Construct a geometric feature set for the solid components, construct a geometric feature set for the BIM model components, and encapsulate the two to form a common geometric feature set; S4. Based on the geometric feature set of virtual and real components, calculate the initial transformation matrix by the centroid coordinate deviation and spatial orientation deviation of the feature points of virtual and real components; based on the geometric nearest neighbor matching rule, select the set of effective matching point pairs between the solid components and the model components; construct a ternary joint optimization objective function that integrates geometric feature constraints, coded feature constraints, and urban rail transit full-profession hierarchical topological constraints based on a three-level topological benchmark; with the initial transformation matrix as the starting point of the iteration and the ternary joint optimization objective function as the core, solve the optimal rigid body transformation matrix of the current iteration round through singular value decomposition to complete the accurate mapping between virtual and real components; S5. Based on the ternary constraints of the ternary joint optimization objective function, quantitatively calculate the consistency deviation of a single component in three dimensions: geometry, coding features, and topological relationships; set a threshold according to the security level corresponding to the component's security level code to perform single-dimensional verification; if the verification fails, trigger deviation classification; execute differentiated adaptive correction strategies for different deviation levels to complete the deviation closed-loop handling, and synchronously update the entity-model mapping relationship and operation and maintenance ledger.

[0034] More specifically, the safety level codes include Level 1 Safety Critical, Level 2 Operationally Important, and Level 3 General. Level 1 Safety Critical involves core components related to train operation safety, main structure, and personal safety. Level 2 Operationally Important are important components that affect the station's operational functions and passenger flow organization. Level 3 General are components that only serve a decorative or auxiliary purpose and have no critical impact on safety or operation.

[0035] Step S1 involves acquiring the completed BIM model, as-built data, and operation and maintenance records of the rail transit station during the construction phase, and completing the operation and maintenance-oriented digital twin model reconstruction of the station. The specific process is as follows: S11. Remove redundant features from the completed BIM model during the construction period that are irrelevant to operation and maintenance; S12. To address the mismatch between the granularity of the completed BIM model during the construction phase and the control requirements during the operation and maintenance phase, the physical components obtained by disassembling the station's physical entities according to the boundary of operation and maintenance functions are used as the sole benchmark. Standardized granularity adaptation processing is performed on the completed BIM model during the construction phase to achieve complete granularity matching between the BIM model components and the physical components, resulting in a matched BIM model. S13. Supplement the operation and maintenance phase of newly added components not included in the as-built BIM model during the construction period; S14. Based on the matching BIM model and newly added components during the operation and maintenance period, reconstruct the model to generate an operation and maintenance-oriented digital twin model of the station.

[0036] Specifically, in S11, temporary construction components, decorative surfaces, and small non-maintenance critical parts features in the as-built BIM model during the construction period need to be deleted, and only the main installation structure, positioning reference plane, and core geometric structure related to operation and maintenance of the components should be retained.

[0037] Specifically, S12 performs standardized granularity adaptation processing on the as-built BIM model during the construction period, as follows: First, the large-scale components generated during the construction period by merging construction sections are broken down into the corresponding smallest single operation and maintenance components according to their independent operation and maintenance functions. Then, multiple small components that were modeled separately during the construction period but belonged to the same operation and maintenance function were merged into a single operation and maintenance component.

[0038] The standard form of the six-level coding system for operations and maintenance in step S2 is as follows: Project type code - Professional system code - Spatial location code - Security level code - Installation time code - Instance number code.

[0039] Specifically, the project type code identifies the type of station project; the professional system code identifies the professional system to which the component belongs; the safety level code identifies the degree of impact of the component on operation and maintenance events such as train safety, passenger safety, and operational services; the installation time code identifies the year the component was installed; and the instance number code distinguishes different individuals of the same type of component.

[0040] The three-level topology benchmark in step S2 is described in detail below: First, the primary topology is the core component, including the main structural columns, side walls, and track foundation of the station. The primary topology serves as the spatial positioning benchmark for the entire station. Then, the secondary topology is the backbone component, including the main fan cabinet in the ventilation room, the power distribution cabinet in the power supply room, the signal cabinet, and the fire control panel. The secondary topology is the positioning reference for the backbone. Finally, the three-level topology consists of end components, including sensors, lighting, access control, and end valves.

[0041] The identification of virtual and real components in step S2 is anchored as follows: First, based on the names of each BIM model component in the station's digital twin model, the professional system code and spatial location code in the operation and maintenance-specific six-level code, we initially screened physical entities with similar names and locations to form initial corresponding candidate pairs. Then, the initial corresponding candidate pairs are checked again by combining the three-level topology benchmark to eliminate false matches and verify that the topology neighbor benchmark components of the entity components and the BIM model components are completely consistent. After the verification is passed, the identity belonging of the entity components and the BIM model components is established in a one-to-one correspondence anchoring relationship. The entity components and BIM model components with the anchored identity correspondence relationship are added to the station anchoring component set. Finally, the bound operation and maintenance exclusive level 6 code serves as a unique identifier for the entire operation and maintenance lifecycle of the component. After the component is replaced or moved, the operation and maintenance exclusive level 6 code and the corresponding identity relationship are updated synchronously.

[0042] Specifically, a one-to-one correspondence is established between the physical components and the BIM model components, as follows: First, based on the station's 3D laser point cloud data, as-built measured coordinates, and operation and maintenance records, the topological neighborhood component set of the candidate centering entity components is extracted. Then, based on the station's digital twin model, the topological neighborhood component set of the BIM model components in the candidate pair is extracted; Next, based on the three-level topology benchmark, it is verified whether the first- to third-level topology components of the two topology neighborhood sets are completely consistent. If the verification passes, the candidate pair is confirmed as the final one-to-one correspondence. If the verification fails, the candidate pair is removed and the matching is repeated until the corresponding anchoring relationship between all physical components and BIM model components is determined, and all physical components are bound with the same maintenance-specific level six code as the model components.

[0043] In step S2, a consistent virtual and real encoded feature vector is generated. The specific process is as follows: First, core coding attributes are extracted. For components in the station anchoring component set, based on the component operation and maintenance exclusive six-level code, the engineering type code, professional system code, spatial location code, safety level code, and installation time code are extracted as the core coding attributes of the component. The instance number code is removed to avoid the unique identifier from destroying the matching logic of components of the same model. Then, the encoded feature vector is generated by vectorizing the core encoded attributes of the component through one-hot encoding, and generating the component encoded feature vector through weighted concatenation. C The coded feature vectors of the physical components are completely consistent with those of the corresponding BIM model components. When a component is replaced or moved, the coding is updated, and the coded feature vector is also updated to reflect the corresponding changes in the core coding attributes. C The calculation formula is as follows: ; in, This is a vector concatenation operator that concatenates five weighted vectors in a fixed order into a single vector; one-hot(m) is the one-hot encoded vector of the m-th core encoded attribute of the component; oh m The weight coefficient of the m-th core coded attribute is determined using the Analytic Hierarchy Process (AHP) in the urban rail transit operation and maintenance field, satisfying the following conditions: ; Finally, the coded feature vectors are bound to the entity components and BIM model components in the station anchored component set that have been anchored to the identity correspondence.

[0044] Specifically, in step S3, the geometric feature set of the solid component is constructed. The specific process is as follows: S311. Perform point cloud data acquisition and standardization preprocessing. First, based on the operational constraints of the four functional zones of the urban rail station, namely the station hall / platform public area, equipment room area, track area, and inter-station connection area, three-dimensional point cloud data of physical components are collected for each zone. Then, for the public areas of the station hall / platform, high-density point cloud full feature extraction is used to extract the number of neighboring points. count Set the value to 30-50 to preserve geometric details across the entire area; Next, for the equipment room area, feature enhancement extraction is performed on the equipment installation reference plane and the core structural plane of the equipment, filtering out non-critical decorative features and neighboring point counts. count Values ​​range from 20 to 30; Furthermore, for the sparse point cloud data obtained from track maintenance windows, an enhanced extraction strategy that preserves core feature points is adopted. Only the core positioning geometric feature points of the track, overhead contact line, platform screen doors, and ventilation shafts are extracted, along with the number of neighboring points. count Values ​​range from 15 to 20; Finally, for the section connection area, based on the end wall of the station's main structure, feature points of the linear engineering connection surface are extracted to ensure the continuity of the network-level mapping and the number of neighboring points. count The value is 20-25. Standardized preprocessing, such as point cloud registration and noise reduction filtering, is performed on the acquired point cloud data.

[0045] S312. Perform component individualization and segmentation. First, the preprocessed overall point cloud is segmented into individual entities to separate the point cloud of each entity corresponding to each entity in the set of station anchoring components. Then, the operation and maintenance-specific level 6 code of each physical component is synchronously bound to the point cloud of each physical component.

[0046] S313. Perform layered extraction of core geometric features of solid components. For a single solid component, core geometric features are extracted at three levels: The first level of global geometric features is to extract the global geometric features of the component based on the component point cloud data, including the three-dimensional coordinates of the component's spatial centroid, the plane equation of the component's installation reference surface, and the normal vector. The second level of feature points refers to each point in the point cloud of a single component. p c,i ( i ≥1), calculate the eigenvalues ​​of its local neighborhood covariance matrix. l 0、 l 1. l 2, of which l 0≤ l 1≤ l 2. Then the curvature at each point k i The calculation formula is: The rate of change Δ of the normal vector of the feature pointn i The calculation formula is: ,in, n i For point p c,i The normal vector, j For point p c,i The index of the neighboring points, For serial number j The unit normal vector of the corresponding neighboring points. N ( i ) is a point p c,i The set of neighborhood points, count For the number of neighboring points, count The values ​​are adaptively selected based on the functional zone to which the component belongs and the point cloud density. Points in the point cloud of a single component with abrupt curvature changes and disordered neighborhood normal vectors are identified as unstable edge points and noise points and are removed. Points with high stability in the point cloud of a single component are retained as feature points of the solid component. The third-level feature point description subset generates a unique description subset for each feature point, specifically including: the spatial coordinates of the feature point, and the curvature of the feature point. k i With the rate of change of the normal vector Δ n i .

[0047] S314. The component name, operation and maintenance-specific six-level code, and three-level features of each entity component are structurally encapsulated to form a complete set of geometric features of the entity component.

[0048] Specifically, in step S3, the geometric feature set of BIM model components is constructed. The specific process is as follows: S321. BIM Digital Twin Model Preprocessing The reconstructed digital twin model of the station is converted to a coordinate system consistent with the station's 3D point cloud to ensure that the coordinate systems are of the same origin. S322. Homology Feature Extraction For BIM model components with anchored identity correspondence in the station anchored component set, the global geometric features, feature points and feature point description subsets of the corresponding BIM model components are extracted using feature extraction rules and curvature / normal vector change rate calculation formulas that are completely consistent with those of the solid components. S323. Adopting the same encapsulation format as the entity geometric feature set, a complete BIM model component geometric feature set is constructed based on the component name, operation and maintenance exclusive six-level code, and three-level features of each BIM model component; based on the operation and maintenance exclusive six-level code, bidirectional lookup of entity components and BIM model components with anchored identity correspondence can be realized.

[0049] In step S4, based on the geometric feature set of the virtual and real components, the initial transformation matrix is ​​calculated through the centroid coordinate deviation and spatial orientation deviation of the feature points of the virtual and real components. The specific process is as follows: First, calculate the centroids of all feature points in the geometric feature set of the solid component and the geometric feature set of the BIM model component, respectively. : , in, These represent the number of feature points in the geometric feature sets of solid components and BIM model components, respectively. These are the geometric feature sets of solid components and BIM model components, respectively. The, the The coordinates of the feature points; Then, calculate the initial translation vector. : ; Next, calculate the initial rotation matrix. : From the feature points of the geometric feature sets of both solid components and model components, high-confidence feature point sets corresponding to the mounting reference plane of the components are selected. Principal component analysis (PCA) is then used to calculate the unit normal vector of the reference plane of the solid components. Unit normal vector of the reference plane of BIM model components Calculate the axis of rotation Calculate the rotation angle ; Calculate the initial rotation matrix using the Rodriguez formula ,in, It is a 3×3 identity matrix. For the axis of rotation The corresponding antisymmetric matrix, ; Finally, determine the initial transformation matrix. : Will Synthesize a 4×4 initial transformation matrix according to the standard format. Then the initial transformation matrix for: .

[0050] In step S4, based on the geometric nearest neighbor matching rule, the set of valid matching point pairs between the solid component and the BIM model component is filtered. The specific process is as follows: First, in the geometric feature set of the solid component and the geometric feature set of the BIM model component, the coordinates of the feature points of the BIM model component are... Apply the initial transformation matrix Obtain the transformed coordinates ; Then, among the feature points of the corresponding solid component, select the point with the smallest Euclidean distance, whose coordinates are... , forming matching point pairs ; Finally, outlier point pairs with a distance greater than the component accuracy threshold are removed, and all valid matching point pairs are finally determined.

[0051] In step S4, a ternary joint optimization objective function is constructed that integrates geometric feature constraints, coding feature constraints, and hierarchical topological constraints based on a three-level topological benchmark for urban rail transit across all disciplines. The specific process is as follows: Let the current one be the first... l ( l ≥1) In the first iteration, the ternary joint optimization objective function is... as follows: ; In the formula: Firstly, This represents the 4×4 homogeneous rigid body transformation matrix used to solve for the objective function of the subsequent ternary joint optimization. M l ; Secondly, The geometric feature loss term quantifies the relative spatial deviation between feature points of the solid component and the BIM model component relative to the allowable threshold of the component's safety level, and then performs dimensionless normalization. ; in, The first in the set of station anchoring components The first solid component The coordinates of the feature points For the corresponding number The first BIM model component The feature points are transformed by the optimal transformation matrix in the previous round. M l-1 (when l When =1, M l-1 = M 0) The coordinates of the transformed feature points, For the first The number of matching point pairs for each component For the first The maximum permissible threshold for geometric deviation for each component is determined according to the safety level: 0.5mm for Level 1 components, 2mm for Level 2 components, and 5mm for Level 3 components. The total number of component pairs in the station anchoring component set; Thirdly, The loss term for the encoded features is normalized based on cosine similarity. ; in, The first in the set of station anchoring components The encoded feature vector of each entity component, This refers to the coded feature vector of the corresponding BIM model component in the station anchor component set. Under normal matching conditions, the coded feature vectors of the entity component and the BIM model component are completely identical. It does not affect iteration, but the loss term increases sharply when there is a mismatch, and it penalizes mismatches across components; Fourthly, To address the hierarchical topological constraint loss term across all urban rail transit disciplines, ensuring the relative topological relationships between components remain unchanged and avoiding global registration drift, dimensionless normalization is applied. , in, For the first A set of third-level topological neighborhood components for each component. The first in the set of station anchoring components The centroid coordinates of each solid component For the first The BIM model component corresponding to each physical component undergoes the optimal transformation matrix from the previous round. M l-1 Transformed centroid coordinates For the first In the topological neighborhood set of the nth entity component The centroid coordinates of each component For the first In the topological neighborhood set of the nth entity component The BIM model component corresponding to each component undergoes the optimal transformation matrix from the previous round. M l-1 Transformed centroid coordinates For the first The number of components in the three-level topological neighborhood set corresponding to each component. For the first The maximum allowable threshold for the topological relative distance deviation corresponding to each component is determined according to the safety level: 0.3mm for Level 1 components, 1mm for Level 2 components, and 3mm for Level 3 components. Fifthly, The weighting coefficients for the three types of loss terms are respectively, satisfying... The weights are adaptively adjusted according to the operating conditions: under normal operation and maintenance conditions, Prioritize ensuring the accuracy of geometric feature matching; Sunroof maintenance / component replacement conditions: Prioritize ensuring stable coded identity matching and topological relationships; driving safety management conditions: Prioritize ensuring the stability of global topology relationships to avoid registration drift.

[0052] In step S4, the initial transformation matrix is ​​used as the starting point for iteration, and the ternary joint optimization objective function is used as the core. The optimal rigid body transformation matrix for the current iteration is solved by singular value decomposition to complete the precise mapping between virtual and real. The specific process is as follows: First, calculate the centroid-free coordinates. For each feature point in the matching point pair set, perform centroid removal to eliminate the influence of translation on the rotation matrix calculation. The calculation formula is: ; in, The first matching point in the set of matching points after centroid removal is respectively Coordinates of feature points of individual solid components and BIM model components; For the first The coordinates of the feature points; For the first The feature points of each BIM model component are transformed by the optimal transformation matrix in the previous round. M l-1 The coordinates of the transformed feature points; The optimal transformation matrix of the previous round for the feature points of the BIM model components. M l-1 The transformed feature point coordinates are used to calculate the centroid of the feature points in the geometric feature set of the BIM model component. Then, construct a 3×3 covariance matrix. : ; Then, regarding the covariance matrix H Perform SVD singular value decomposition. The SVD decomposition formula is: ; in, All are 3×3 orthogonal matrices, satisfying It is a 3×3 diagonal matrix, with diagonal elements These are the singular values ​​of the covariance matrix, arranged in descending order; Then, the optimal rotation matrix is ​​solved based on the SVD decomposition results. , The basic solution formula is ;right To avoid model mirror distortion, a forced correction to the right-handed coordinate system is performed using the following formula: Perform orthogonality verification to confirm. The error does not exceed Ensure no scaling or shearing distortion; perform matrix verification: verification The error does not exceed To ensure there is no mirror distortion; if the verification fails, return to re-filter valid matching point pairs and iterate again. Then, based on the optimal rotation matrix obtained in this round... Solve for the corresponding optimal translation vector. This achieves complete alignment of the centroids of all feature points in the geometric feature set of the solid component with those in the BIM model component: ;Will , Synthesize according to standard format. 4×4 optimal transformation matrix for multiple iterations ; Finally, determine whether the current iteration has reached the global optimum, decide whether to terminate the iteration, and calculate the ternary joint optimization objective function values ​​for the current and previous iterations. relative rate of change The iteration will terminate if any of the following conditions are met: Condition 1, ,Right now When the value is less than the convergence threshold, the objective function converges and the global optimum is reached; Condition 2, iteration rounds This maximizes the number of iterations and avoids infinite loops.

[0053] Specifically, the quantification calculation of the consistency difference in the three dimensions in step S5 is as follows: The geometric feature constraints, coding feature constraints, and urban rail transit full-discipline hierarchical topology constraints in the corresponding ternary joint optimization objective function are used to quantitatively calculate the consistency deviation between the physical components and the BIM model components from three dimensions: geometric features, coding features, and topological relationships.

[0054] S511. Differences in the geometric consistency of individual components , which is the ratio of the geometric deviation of the component to the allowable threshold of the safety level. The first component in the set of station anchoring components... Each component The calculation formula is: ; in: For the corresponding number The first BIM model component Each feature point is transformed by the optimal transformation matrix in the current round. M l The coordinates of the transformed feature points.

[0055] S512. Differences in the consistency of coding features of individual components , which is the cosine similarity deviation of the component coding features. The in the set of station anchoring components... Each component The calculation formula is: .

[0056] S513. Differences in topological consistency of individual components , which is the ratio of the relative distance deviation of the component topology to the allowable threshold of the safety level. The first [unit / component] in the set of station anchoring components. Each component The calculation formula is: ; in, For the first The BIM model component corresponding to each physical component undergoes the optimal transformation matrix in the current round. M l Transformed centroid coordinates For the first In the topological neighborhood set of the nth entity component The BIM model component corresponding to each component undergoes the optimal transformation matrix in the current round. M l Transformed centroid coordinates.

[0057] Specifically, the single-dimensional verification and deviation grading determination in step S5 are carried out as follows: S521. Single-dimensional verification By comparing the consistency differences across the three dimensions with their corresponding thresholds, it is determined whether a single dimension passes the validation: If If the single-dimensional verification passes, then the single-dimensional verification passes; otherwise, the single-dimensional verification fails, triggering the deviation classification judgment. S522. Deviation Classification Judgment: Abnormal deviations are classified into 3 levels: Grade I minor deviation: Grade III components , Through single-dimensional verification .

[0058] Level II warning deviation: For secondary components , Through single-dimensional verification ; or first-level components , Through single-dimensional verification .

[0059] Level III serious deviation: Level I component and If any dimension fails the verification; or if any level of component fails the verification... and Any dimension failed the single-dimensional validation; or any level of component... ≥2.

[0060] Specifically, the graded correction process in step S5 is as follows: Differentiated adaptive correction strategies are implemented for different levels of deviation to ensure closed-loop handling of all deviations.

[0061] Level I minor deviation correction procedure: using the optimal transformation matrix of the current round M l Using the initial values, the SVD iterative solution in step S4 is automatically executed to update the local optimal transformation matrix of the component; the calculation is then performed. ,confirm Reduce the value to within the threshold; automatically update the entity-model mapping relationship using the operation and maintenance-specific level 6 code as the unique primary key; and record correction logs.

[0062] Level II Warning Deviation Correction Handling Procedure: If the deviation is caused by component displacement / replacement / attribute change, re-execute the identity anchoring in step S2, update the maintenance-specific Level 6 code, and then execute the subsequent steps sequentially. If the above is not the case, lock the abnormal component and its topological neighborhood, re-execute the effective matching point pair filtering in step S4, and exclude abnormal point pairs and mismatched matching pairs; use the optimal transformation matrix from the previous round. M l-1 Using the initial values, perform step S4, the full-process registration optimization, to solve for the new optimal transformation matrix. M l Re-execute the ternary consistency check to confirm that all dimensional deviations have been reduced to within the threshold; push the correction confirmation notification to the operation and maintenance personnel, and after manual confirmation, update the optimal transformation matrix, entity-model mapping relationship, and operation and maintenance ledger; record the correction log.

[0063] Level III Critical Deviation Correction and Handling Procedure: First, immediately trigger an operational safety alert and push it to the station's operation and maintenance control center, and simultaneously suspend the automated operation and maintenance commands related to this component; Then, manual intervention is used to check the root cause of the deviation: if it is caused by component displacement / replacement / attribute change, then the identity anchoring in step S2 is re-executed, and the operation and maintenance-specific level 6 code is updated, and then the subsequent steps are executed in sequence; if it is caused by global topology drift, then the three-level topology benchmark verification of neighborhood consistency in step S2 is re-executed, and then the subsequent steps are executed in sequence; if it is caused by coding / matching mismatch, then the effective matching point pair filtering in step S4 is re-executed, and then the subsequent steps are executed in sequence to lock the correct virtual-real correspondence; Afterwards, once the processing is complete, a full-dimensional ternary consistency check is re-executed to confirm that all dimensions have passed the check. Finally, after manual confirmation, the optimal transformation matrix, entity-model mapping relationship, and operation and maintenance log are updated, and the correction log is recorded.

[0064] Example 1 This embodiment applies to a two-level underground island platform subway station. The station is 208m long, 20.3m wide in a standard section, and 16.8m deep in the foundation. It includes four functional areas: the concourse / platform public area, the equipment room area, the track area, and the inter-station connection area. It covers all disciplines including the main structure, platform screen doors, power supply, signaling, ventilation and air conditioning, water supply and drainage, and motion control.

[0065] S1. Operation-oriented reconstruction of station digital twin model First, redundant features are removed. The station construction phase BIM model (accuracy LOD300) is adopted. Temporary construction components such as temporary construction fences and formwork supports are deleted. Decorative surfaces such as wall tiles and ceiling keel are removed. Small non-maintenance key parts such as bolts and washers are deleted. Only the main installation structure of the components, the positioning reference surface, and the core geometric structure related to operation and maintenance are retained. Then, granularity standardization and adaptation are performed, using the operational and maintenance function boundaries of the station's physical entities as the sole benchmark to complete model granularity adaptation: First, large-granularity components generated by merging construction sections during the construction period are decomposed into corresponding smallest single operational and maintenance components according to independent operational and maintenance functions. For example, the platform screen door group originally modeled by construction section is split into 24 independent sliding door units. Second, multiple groups of small components that were modeled separately during the construction period but belong to the same operational and maintenance function are merged into a single operational and maintenance component. For example, each sliding door unit and its corresponding door controller (originally modeled separately) are merged into a single operational and maintenance component. Ultimately, the granularity of BIM model components and physical components are fully matched. Next, new components were added to supplement the BIM model during the construction period, including new equipment added during the operation and maintenance period that was not included in the BIM model during the construction period. For example, 6 new passenger flow statistics cameras were added to the platform and 8 new environmental sensors were added to the equipment room to complete the model. Finally, the reconstructed operation and maintenance-oriented digital twin model of the station is output. The model coordinate system is completely identical to the station engineering coordinate system, with the origin being the corner point of the base plate at the station's starting mileage K12+350.000m.

[0066] S2. Construction of a dedicated coding system for operations and maintenance, anchoring of virtual and real identities, and generation of coding features. Construction of a dedicated six-level coding system for operations and maintenance Based on a six-level classification system, and by reasonably merging and expanding the codes according to the operation and maintenance scenarios of urban rail stations, a six-level coding system dedicated to operation and maintenance is constructed. The total length of the code segment is 16 bits, and the format is: project type code (2 bits) - professional system code (2 bits) - spatial location code (4 bits) - safety level code (2 bits) - installation time code (4 bits) - instance number code (2 bits). A unique full life cycle code is assigned to each of the 12,862 BIM model components of the reconstructed station. Typical coding example (Platform No. 3 platform screen door on the up line): 01-08-0203-01-2021-03. Meaning of each code segment: Project type code 01 - Underground station project; Professional system code 08 - Platform screen door system; Spatial location code 0203 - Platform level, up line, No. 3 door; Safety level code 01 - Level 1 safety critical component; Installation time code 2021 - Installation completed in 2021; Instance number code 03 - The 3rd individual of the same type of component; Then, the three-level topology benchmark is constructed and the identities of virtual and physical components are anchored. A three-level topology benchmark is constructed. The core components of the first-level topology are the 800mm×800mm main structural columns, side walls, and track foundations of the station, which serve as the spatial positioning benchmark for the entire station. The main components of the second-level topology are the main fan cabinet of the ventilation room, the 35kV power distribution cabinet, the signal cabinet, and the fire control panel, which serve as the main positioning benchmark. The terminal components of the third-level topology are sensors, lighting, access control, and terminal valves. Initial candidate pair generation: Based on the name, professional system code, and spatial location code of BIM model components, matching the corresponding attributes of entity components in the as-built data and operation and maintenance ledger, a total of 12,862 initial corresponding candidate pairs were generated. Secondary topological neighborhood verification extracts the topological neighborhood set of the entity components and BIM model components in the candidate pair, verifies the consistency of the topological components from level one to level three, removes 12 sets of erroneous candidate pairs of the same model of platform screen door, and completes the one-to-one identity anchoring of all components after re-matching, generates the station anchoring component set, and binds all entity components with the same level six code as the BIM model components. Next, the encoded feature vector is generated. Core coding attribute extraction: For all components in the anchored component set, extract five core attributes: project type code, professional system code, spatial location code, safety level code, and installation time code, and remove instance number code; The weighting coefficients were determined using the Analytic Hierarchy Process (AHP) in the field of urban rail transit operation and maintenance, to determine the weighting coefficients for the five core attributes: oh 1 (Project Type) = 0.15 oh 2 (professional system) = 0.25 oh 3 (spatial location) = 0.20 oh 4 (security level) = 0.30 oh 5 (installation time) = 0.10, satisfying ∑ oh m =1 (m=1~5); Vectorization and concatenation: One-hot encoding is used to vectorize each core attribute, including project type code (3 dimensions), professional system code (12 dimensions), spatial location code (24 dimensions), safety level code (3 dimensions), and installation time code (10 dimensions). After weighted concatenation according to the formula, a 52-dimensional component coding feature vector is generated. C The coded feature vectors of the physical components are completely consistent with those of the corresponding BIM model components.

[0067] Calculation example: After the above-mentioned shielding door component codes are vectorized, the engineering type code 01 corresponds to one-hot(1)=[1,0, 0]; the professional system code 08 corresponds to one-hot(2)=[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]; the spatial location code 0203 corresponds to one-hot(3)=[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]; the safety level code 01 corresponds to one-hot(4)=[1, 0, 0]; and the installation time code 2021 corresponds to one-hot(5)=[0, 0, 0, 0, 0, 0, 1, 0, 0, 0].

[0068] Use the corresponding weights oh m Multiplying by each element of the one-hot vector, the weighted vector has the same dimension as the original one-hot vector: Weighted Engineering Type Vector: [1, 0, 0] × 0.15 = [0.15, 0, 0]; Weighted Professional System Vector: [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0] × 0.25 = [0, 0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0]; Weighted Spatial Location Vector: [0, 0, 1, ..., 0] × 0.20 = [0, 0, 0.20, ..., 0]; Weighted Security Level Vector: [1, 0, 0] × 0.30 = [0.30, 0, 0]; Weighted Installation Time Vector: [0, 0, ..., 1, ..., 0] × 0.10 = [0, 0, ..., 0.10, ..., 0].

[0069] The 52-dimensional encoded feature vector is obtained by concatenating the features in sequence.

[0070] S3. Construction of the Geometric Feature Set of Virtual and Real Components First, differentiated point cloud acquisition and preprocessing for different regions. To address the operational constraints of the four functional zones, differentiated acquisition of 3D laser point cloud data is employed, with specific parameters shown in Table 1: Table 1. Differentiated Data Collection Table for the Four Functional Zones .

[0071] The acquired point cloud was preprocessed by registration, noise reduction, filtering and standardization, and the overall point cloud registration accuracy was controlled within ±0.2mm. Then, point cloud individual segmentation and encoding binding. The preprocessed overall point cloud is segmented into individual point clouds, which correspond one-to-one with the 12,862 entity components of the anchored component set. Each individual point cloud is synchronously bound with a corresponding operation and maintenance exclusive level 6 code to achieve the same source of coding between point cloud, entity component and model component. Finally, hierarchical geometric feature extraction and feature set encapsulation. Geometric feature extraction of solid components: Features are extracted at three levels for a single component. Global geometric features: Extract the global geometric features of the component, including the three-dimensional coordinates of the component's spatial centroid, the plane equation of the component's mounting reference surface, and the normal vector; Feature points: For each point in a single point cloud, calculate the eigenvalues ​​of the local neighborhood covariance matrix. For example, the eigenvalues ​​of a certain feature point. l 0 = 0.002 mm² l 1 = 0.045 mm² l 2 = 0.128 mm², calculate curvature Simultaneously, calculate the rate of change Δ of the normal vector at a point based on the mean of the differences in the normal vectors of its neighboring points. n i Remove curvature abrupt changes and Δ n i Noise levels exceeding the standard; Feature point description subset: Generate a description subset for each feature point that includes spatial coordinates, curvature, and rate of change of the normal vector.

[0072] The component name, maintenance-specific six-level code, and three-level features of each physical component are structurally encapsulated to form a complete set of geometric features of the physical component.

[0073] Geometric feature extraction of BIM model components involves converting the digital twin model to an engineering coordinate system that is identical to the point cloud. Using feature extraction rules and calculation formulas that are completely consistent with those of the solid components, three levels of geometric features of the BIM model components are extracted and encapsulated in the same format to form a geometric feature set of the BIM model components.

[0074] S4. Real-Virtual Mapping Registration in Ternary Constraint Joint Optimization First, the initial transformation matrix is ​​calculated. Centroid Calculation: Calculate the centroid of all feature points in the geometric feature set of the solid component and the BIM model component respectively. , ; Solving for the initial translation vector: ; Initial rotation matrix solution: Calculate the unit normal vectors of the solid and the model mounting reference plane using PCA, and the angle between them. Calculate the unit vector of the rotation axis. The initial rotation matrix is ​​calculated using the Rodriguez formula. ; Synthesize a 4×4 initial transformation matrix As the starting point for iterative solution, .

[0075] Then, filter the valid matching points. Apply the initial transformation matrix to the feature points of the BIM model components. Based on the geometric nearest neighbor matching rule, the corresponding points with the smallest Euclidean distance are selected to generate initial matching point pairs; distance thresholds are set according to the safety level of the components, and abnormal point pairs exceeding the threshold are removed. The threshold rules are: 0.5mm for first-level components, 2mm for second-level components, and 5mm for third-level components; finally, 2,794,300 effective matching point pairs are obtained for the entire station.

[0076] Next, the ternary joint optimization objective function is constructed. The current number is l ( l ≥1) Through iterative rounds, construct a ternary joint optimization objective function that integrates geometric feature constraints, coding feature constraints, and hierarchical topological constraints for all urban rail transit specialties based on a three-level topological benchmark. , ; Sub-item 1: Geometric Feature Loss , ; Let the current one be the first... l =1 iteration, substituted into numerical calculations to obtain .

[0077] Sub-item 2: Encoding Feature Loss Item , ; Among the 12,862 sets of components in the entire station, 12 sets of platform screen doors of the same model and 8 sets of sensors of the same model had cross-component mismatches. The remaining 12,842 sets of components had completely identical coding characteristics. Calculations showed that... .

[0078] Sub-item 3: Hierarchical topology constraint loss item , ; Calculated by weighted average of the safety levels of all station components, the following is obtained: .

[0079] For normal operation and maintenance conditions, set weighting coefficients. ,satisfy Substituting these values ​​into the ternary joint optimization objective function, we obtain the initial objective function value for the first iteration. E 1 = 0.5 × 0.179 + 0.3 × 0.00084 + 0.2 × 0.162 = 0.122152.

[0080] Finally, iterative solution by Starting from the point of iteration, the optimal rigid body transformation matrix is ​​solved iteratively using Singular Value Decomposition (SVD), and a convergence threshold is set. The maximum number of iterations is 100. Each iteration strictly follows these 6 steps, repeating until convergence: For the feature points of the entire station model after the previous transformation, the global centroid is recalculated, and the centroid coordinate processing is completed to eliminate the interference of translation on the solution of the rotation matrix. Construct a 3×3 covariance matrix based on all valid matching point pairs. H ; Perform SVD singular value decomposition on the covariance matrix; Solve for the optimal rotation matrix in the current round. R l Perform orthogonality and determinant double verification; Find the optimal translation vector for the current round. t l Synthesize a 4×4 globally optimal transformation matrix M l ; Calculate the global objective function value for the current round. E l Determine whether the convergence condition is met.

[0081] The results of key iteration rounds in this example are shown in Table 2: Table 2 Iteration Results .

[0082] The convergence condition is met in the 15th iteration, and the globally optimal rigid body transformation matrix for the entire station is output. M 15 The core rotation matrix R 15 for: ; Compliance verification: Orthogonality check, The maximum error is 3.2 × 10⁻⁶. −11 <10 −6 Verification passed; Determinant validation, verification Error <10 −10 <10 −6 The verification passed; there was no scaling, shearing, or mirror distortion, and it conforms to the rigid body transformation rules.

[0083] S5. Quantification of Virtual-Real Consistency Deviation and Closed-Loop Operation and Maintenance The three-dimensional consistency deviation quantification calculation corresponds to the three-dimensional constraints. The three-dimensional deviations of all 12,862 components in the entire station are quantified one by one. Three typical components of the entire station are selected as examples: station hall lighting fixtures (level three general components), up-line track foundation (level one safety critical components), and platform No. 3 platform screen door (level one safety critical components).

[0084] The consistency deviation of the three typical components was quantitatively calculated using a three-dimensional method. The results are shown in Table 3. Table 3. Quantitative Calculation Table of Consistency Deviation in Three Dimensions .

[0085] The three typical components were verified in one dimension, and the verification results are shown in Table 4: Table 4. Verification Table for Typical Components .

[0086] Differentiated adaptive correction strategies are implemented for different levels of deviation to ensure closed-loop handling of all deviations.

[0087] Level I minor deviation correction procedure: using the globally optimal transformation matrix in step S4. M 15 Using the initial values, SVD iterative optimization is automatically performed to update the local optimal transformation matrix of the component; after the iteration is completed, the geometric relative deviation is recalculated. The lamp was corrected. =0.64, reduced to below the threshold of 1, verification passed; using the operation and maintenance exclusive level 6 code as the unique primary key, the entity-model mapping relationship is automatically updated; correction logs are recorded to complete closed-loop processing.

[0088] Level II Warning Deviation Correction Process: Identify the abnormal component and its primary topological neighbor components; re-screen valid matching point pairs, eliminating abnormal and mismatched points; using the previous round's optimal transformation matrix as initial values, re-execute step S4 for full-process registration optimization to solve for a new local optimal transformation matrix; re-execute the three-dimensional consistency check, correcting the track base segment. =0.36, reduced to below the threshold of 1, all dimensions passed the verification; a correction confirmation notification was pushed to the station's track maintenance team, and the track smoothness data and registration accuracy were manually reviewed and confirmed to be effective; the optimal transformation matrix, entity-model mapping relationship and maintenance log were updated, the correction log was recorded, and the closed-loop processing was completed.

[0089] Level III Critical Deviation Correction and Handling Procedure: Safety Alert Triggered: Immediately push a Level 1 operational safety alert to the station's operation and maintenance control center, simultaneously suspend the automated opening and closing control commands of the platform screen door, and switch to manual control mode to eliminate safety risks; Manual Source Tracing and Verification: Operation and maintenance personnel conduct on-site verification and confirm that the root cause of the deviation is that after the platform screen door body was replaced in compliance with regulations, the operation and maintenance ledger and coding were not updated synchronously, resulting in coding feature mismatch and misalignment of topological neighborhood relationships; Root Cause Remediation: Re-execute step S2, the component identity anchoring process, update the operation and maintenance-specific Level 6 coding and topological neighborhood relationships of the platform screen door, and synchronously update the operation and maintenance ledger and change order records; Registration Optimization and Verification: Re-execute the full registration process of steps S3-S4, solve for the new optimal transformation matrix, re-execute the three-dimensional verification, and correct the deviation. =0、 =0.28、 =0.22, all dimensions passed verification; closed-loop effective: maintenance personnel on-site reviewed the shielded door switch function and limit accuracy, and after confirming that there were no errors, the safety warning was lifted, the automated control was restored, the mapping relationship and maintenance ledger were updated, the entire process log of the handling was fully recorded, and the closed-loop handling was completed.

[0090] This invention, combined with the specific needs of virtual-physical mapping in operation and maintenance, constructs a six-level coding system specifically for operation and maintenance. It specifically supplements and optimizes the design of dedicated fields related to hierarchical control of operation and maintenance security levels, precise anchoring of component spatial locations, and full lifecycle identity traceability, completing customized optimization to adapt to the entire process of virtual-physical mapping. Through one-hot encoding vectorization conversion, the coding is transformed from traditional auxiliary information tags into quantitative features that can deeply participate in registration optimization and deviation verification, providing underlying support for accurate matching of virtual and physical components and full lifecycle identity continuation during the operation and maintenance phase, and also realizing a smooth connection between the coding system during the construction and operation and maintenance phases.

[0091] This invention constructs a fully automated identity anchoring method that employs initial screening and matching of coded attributes and secondary verification based on a three-level topological benchmark. This method replaces the inefficient traditional manual binding and simple name matching approach, significantly improving anchoring efficiency and fully adapting to the large-scale operation and maintenance needs of urban rail stations with their massive number of components. Through a three-level topological neighborhood consistency verification mechanism, it fundamentally solves the problem of cross-component mismatch among a large number of standardized components of the same model and name within the station, establishing a stable one-to-one correspondence between virtual and physical components. Combined with the full lifecycle design of operation and maintenance-specific codes, it achieves synchronous and automated updates of identity correspondence after component replacement and relocation, completely avoiding the long-term chaos in the identity correspondence between virtual and physical components.

[0092] This invention constructs a ternary joint optimization objective function that integrates geometric feature constraints, coding feature constraints, and hierarchical topological constraints across all urban rail transit disciplines. The coding feature loss term and the hierarchical topological constraint loss term are deeply integrated into the iterative solution process of the rigid body transformation matrix. The coding feature loss term increases sharply during cross-component mismatches, effectively penalizing mismatches. The topological constraint loss term ensures that the relative topological relationships between components remain unchanged, avoiding global registration drift. Compared to traditional pure geometric registration schemes, this invention significantly improves registration accuracy in complex operation and maintenance scenarios and reduces component mismatch rates, stably meeting the high-precision and high-reliability registration requirements of core components related to driving safety and passenger safety.

[0093] This invention establishes a closed-loop operation and maintenance system covering the entire lifecycle of virtual-physical consistency, encompassing three-dimensional deviation quantification, single-dimensional verification of safety level matching, deviation classification judgment, and differentiated adaptive correction. This system achieves continuous and automated verification of virtual-physical consistency. For high-frequency dynamic change scenarios such as component replacement, relocation, and modification during the operation and maintenance phase of urban rail stations, it can automatically identify consistency deviations and match corresponding adaptive correction strategies, synchronously updating the entity-model mapping relationship and operation and maintenance ledger. This completely solves the problem of the model becoming disconnected from the physical entity over time after one-time registration in traditional solutions, preventing the digital twin model from becoming a static dead model that cannot adapt to dynamic needs. It provides a stable and accurate dynamic digital foundation for the long-term intelligent operation and maintenance application of urban rail stations.

Claims

1. A method for mapping virtual and real consistency of digital twins for rail transit stations and a closed-loop operation and maintenance mechanism, wherein the virtual part refers to the digital twin model of the rail transit station and its various BIM model components, and the real part refers to the physical entities within the operation and maintenance scope of the rail transit station and their physical components, characterized in that: Includes the following steps: S1. Undertake the construction phase of rail transit stations' completed BIM models, as-built data, and operation and maintenance ledgers, and complete the reconstruction of the station's digital twin model with an operation and maintenance orientation; S2. Based on the station's digital twin model and physical entities, construct a six-level coding system dedicated to operation and maintenance, build a three-level topology benchmark, complete the identification of virtual and physical components, and generate a coding feature vector that is consistent between virtual and physical components; S3. Construct a geometric feature set for the solid components, construct a geometric feature set for the BIM model components, and encapsulate the two to form a common geometric feature set; S4. Based on the geometric feature set of virtual and real components, calculate the initial transformation matrix by the centroid coordinate deviation and spatial orientation deviation of the feature points of virtual and real components; Based on the geometric nearest neighbor matching rule, a set of effective matching point pairs between solid components and BIM model components is selected; a ternary joint optimization objective function is constructed that integrates geometric feature constraints, coded feature constraints, and urban rail transit full-profession hierarchical topological constraints based on a three-level topological benchmark; with the initial transformation matrix as the starting point of the iteration and the ternary joint optimization objective function as the core, the optimal rigid body transformation matrix of the current iteration is solved through singular value decomposition to complete the accurate mapping between the virtual and the real. S5. Based on the ternary constraints of the ternary joint optimization objective function, quantitatively calculate the consistency deviation of a single component in three dimensions: geometry, coding features, and topological relationships; set a threshold according to the security level corresponding to the component's security level code to perform single-dimensional verification; if the verification fails, trigger deviation classification; execute differentiated adaptive correction strategies for different deviation levels to complete the deviation closed-loop handling, and synchronously update the entity-model mapping relationship and operation and maintenance ledger.

2. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 1, characterized in that: Step S1 involves acquiring the completed BIM model, as-built data, and operation and maintenance records of the rail transit station during the construction phase, and completing the operation and maintenance-oriented digital twin model reconstruction of the station. The specific process is as follows: S11. Remove redundant features from the completed BIM model during the construction period that are irrelevant to operation and maintenance; S12. To address the mismatch between the granularity of the completed BIM model during the construction phase and the control requirements during the operation and maintenance phase, the physical components obtained by disassembling the station's physical entities according to the boundary of operation and maintenance functions are used as the sole benchmark. Standardized granularity adaptation processing is performed on the completed BIM model during the construction phase to achieve complete granularity matching between the BIM model components and the physical components, resulting in a matched BIM model. S13. Supplement the operation and maintenance phase of newly added components not included in the as-built BIM model during the construction period; S14. Based on the matching BIM model and newly added components during the operation and maintenance period, reconstruct the model to generate an operation and maintenance-oriented digital twin model of the station.

3. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 1, characterized in that: The standard form of the six-level coding system for operations and maintenance in step S2 is as follows: Project type code - Professional system code - Spatial location code - Security level code - Installation time code - Instance number code.

4. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 1, characterized in that: The three-level topology benchmark in step S2 is described in detail below: First, the primary topology is the core component, including the main structural columns, side walls, and track foundation of the station. The primary topology serves as the spatial positioning benchmark for the entire station. Then, the secondary topology is the backbone component, including the main fan cabinet in the ventilation room, the power distribution cabinet in the power supply room, the signal cabinet, and the fire control panel. The secondary topology is the positioning reference for the backbone. Finally, the three-level topology consists of end components, including sensors, lighting, access control, and end valves.

5. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 1, characterized in that: The identification of virtual and real components in step S2 is anchored as follows: First, based on the names of each BIM model component in the station's digital twin model, the professional system code and spatial location code in the operation and maintenance-specific six-level code, we initially screened physical entities with similar names and locations to form initial corresponding candidate pairs. Then, the initial corresponding candidate pairs are checked again by combining the three-level topology benchmark to eliminate false matches and verify that the topology neighbor benchmark components of the entity components and the BIM model components are completely consistent. After the verification is passed, the identity belonging of the entity components and the BIM model components is established in a one-to-one correspondence anchoring relationship. The entity components and BIM model components with the anchored identity correspondence relationship are added to the station anchoring component set. Finally, the bound operation and maintenance exclusive level 6 code serves as a unique identifier for the entire operation and maintenance lifecycle of the component. After the component is replaced or moved, the operation and maintenance exclusive level 6 code and the corresponding identity relationship are updated synchronously.

6. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 1, characterized in that: In step S2, a consistent virtual and real encoded feature vector is generated. The specific process is as follows: First, core coding attributes are extracted. For components in the station anchoring component set, based on the component operation and maintenance exclusive six-level code, the project type code, professional system code, spatial location code, safety level code, and installation time code are extracted as the core coding attributes of the component, and the instance number code is removed. Then, the encoded feature vector is generated by vectorizing the core encoded attributes of the component through one-hot encoding, and generating the component encoded feature vector through weighted concatenation. C The coded feature vectors of the physical components are completely consistent with those of the corresponding BIM model components. When a component is replaced or moved, the coding is updated, and the coded feature vector is also updated to reflect the corresponding changes in the core coding attributes. C The calculation formula is as follows: ； in, This is a vector concatenation operator that concatenates five weighted vectors in a fixed order into a single vector; one-hot(m) is the one-hot encoded vector of the m-th core encoded attribute of the component; ω m The weight coefficient of the m-th core encoded attribute is determined using the Analytic Hierarchy Process (AHP) in the urban rail transit operation and maintenance field, satisfying the following conditions: ; Finally, the coded feature vectors are bound to the entity components and BIM model components in the station anchored component set that have been anchored to the identity correspondence.

7. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 6, characterized in that: In step S4, based on the geometric feature set of the virtual and real components, the initial transformation matrix is ​​calculated through the centroid coordinate deviation and spatial orientation deviation of the feature points of the virtual and real components. The specific process is as follows: First, calculate the centroids of all feature points in the geometric feature set of the solid component and the geometric feature set of the BIM model component, respectively. : ， in, These represent the number of feature points in the geometric feature sets of solid components and BIM model components, respectively. These are the geometric feature sets of solid components and BIM model components, respectively. The, the The coordinates of the feature points; Then, calculate the initial translation vector. : ； in, The translation of the station's mileage direction along the X-axis of the right-hand Cartesian coordinate system for the unified station project. This represents the translation along the Y-axis, i.e., the horizontal width direction of the station. This represents the translation along the Z-axis, i.e., the elevation direction. Next, calculate the initial rotation matrix. : From the feature points of the geometric feature sets of both the solid component and the BIM model component, high-confidence feature point sets corresponding to the component's installation reference plane are selected. Principal component analysis (PCA) is then used to calculate the unit normal vector of the solid component's reference plane. Unit normal vector of the reference plane of BIM model components Calculate the axis of rotation Calculate the rotation angle ; Calculate the initial rotation matrix using the Rodriguez formula ,in, It is a 3×3 identity matrix. For the axis of rotation The corresponding antisymmetric matrix, ; Finally, determine the initial transformation matrix. : Will Synthesize a 4×4 initial transformation matrix according to the standard format. Then the initial transformation matrix for: 。 8. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 7, characterized in that: In step S4, based on the geometric nearest neighbor matching rule, the set of valid matching point pairs between the solid component and the BIM model component is filtered. The specific process is as follows: First, in the geometric feature set of the solid component and the geometric feature set of the BIM model component, the coordinates of the feature points of the BIM model component are... Apply the initial transformation matrix Obtain the transformed coordinates ; Then, among the feature points of the corresponding solid component, select the point with the smallest Euclidean distance, whose coordinates are... , forming matching point pairs ; Finally, outlier point pairs with a distance greater than the component accuracy threshold are removed, and all valid matching point pairs are finally determined.

9. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 1, characterized in that: In step S4, a ternary joint optimization objective function is constructed that integrates geometric feature constraints, coding feature constraints, and hierarchical topological constraints based on a three-level topological benchmark for urban rail transit across all disciplines. The specific process is as follows: Let the current one be the first... l and l After ≥1 iteration, the ternary joint optimization objective function is... as follows: ; In the formula: Firstly, This represents the 4×4 homogeneous rigid body transformation matrix used to solve for the objective function of the subsequent ternary joint optimization. M l ; Secondly, The geometric feature loss term quantifies the relative spatial deviation between feature points of the solid component and the BIM model component relative to the allowable threshold of the component's safety level, and then performs dimensionless normalization. ； in, The first in the set of station anchoring components The first solid component The coordinates of the feature points For the corresponding number The first BIM model component The feature points are transformed by the optimal transformation matrix in the previous round. M l-1 The transformed feature point coordinates, and when l When =1, M l-1 = M 0， For the first The number of matching point pairs for each component For the first The maximum permissible threshold for geometric deviation for each component is determined according to the safety level: 0.5mm for Level 1 components, 2mm for Level 2 components, and 5mm for Level 3 components. The total number of component pairs in the station anchoring component set; Thirdly, The loss term for the encoded features is normalized based on cosine similarity. ； in, The first in the set of station anchoring components The encoded feature vector of each entity component, This refers to the coded feature vector of the corresponding BIM model component in the station anchor component set. Under normal matching conditions, the coded feature vectors of the entity component and the BIM model component are completely identical. It does not affect iteration, but the loss term increases sharply when there is a mismatch, and it penalizes mismatches across components; Fourthly, For the topology constraint loss terms across all urban rail transit disciplines, dimensionless normalization is performed. ， in, For the first A set of third-level topological neighborhood components for each component. The first in the set of station anchoring components The centroid coordinates of each solid component For the first The BIM model component corresponding to each physical component undergoes the optimal transformation matrix from the previous round. M l-1 Transformed centroid coordinates For the first In the topological neighborhood set of the nth entity component The centroid coordinates of each component For the first In the topological neighborhood set of the nth entity component The BIM model component corresponding to each component undergoes the optimal transformation matrix from the previous round. M l-1 Transformed centroid coordinates For the first The number of components in the three-level topological neighborhood set corresponding to each component. For the first The maximum allowable threshold for the topological relative distance deviation corresponding to each component is determined according to the safety level: 0.3mm for Level 1 components, 1mm for Level 2 components, and 3mm for Level 3 components. Fifthly, The weighting coefficients for the three types of loss terms are respectively, satisfying... The weights are adaptively adjusted according to the operating conditions: under normal operation and maintenance conditions, Prioritize ensuring the accuracy of geometric feature matching; Sunroof maintenance / component replacement conditions: Prioritize ensuring stable coded identity matching and topological relationships; driving safety management conditions: Prioritize ensuring the stability of the global topology.

10. The digital twin virtual-real consistency mapping and operation and maintenance closed-loop method for rail transit stations according to claim 1, characterized in that: In step S4, the initial transformation matrix is ​​used as the starting point for iteration, and the ternary joint optimization objective function is used as the core. The optimal rigid body transformation matrix for the current iteration is solved by singular value decomposition to complete the precise mapping between virtual and real. The specific process is as follows: First, calculate the centroid-free coordinates. For each feature point in the matching point pair set, perform centroid removal to eliminate the influence of translation on the rotation matrix calculation. The calculation formula is: ； in, The first matching point in the set of matching points after centroid removal is respectively Coordinates of feature points of individual solid components and BIM model components; For the first The coordinates of the feature points; For the first The feature points of each BIM model component are transformed by the optimal transformation matrix in the previous round. M l-1 The coordinates of the transformed feature points; The optimal transformation matrix of the previous round for the feature points of the BIM model components. M l-1 The transformed feature point coordinates are used to calculate the centroid of the feature points in the geometric feature set of the BIM model component. Then, construct a 3×3 covariance matrix. : ； Then, regarding the covariance matrix H Perform SVD singular value decomposition. The SVD decomposition formula is: ; in, All are 3×3 orthogonal matrices, satisfying It is a 3×3 diagonal matrix, with diagonal elements The singular values ​​of the covariance matrix are arranged in descending order; Then, the optimal rotation matrix is ​​solved based on the SVD decomposition results. , The basic solution formula is ;right Perform a forced correction to the right-handed coordinate system; the correction formula is as follows: Perform orthogonality verification to confirm. The error does not exceed Perform determinant validation: Verification The error does not exceed If the validation fails, return to the previous step and re-filter for valid matching pairs before iterating again. Then, based on the optimal rotation matrix obtained in this round... Solve for the corresponding optimal translation vector. This achieves complete alignment of the centroids of all feature points in the geometric feature set of the solid component with those in the BIM model component: ;Will , Synthesize according to standard format. 4×4 optimal transformation matrix for multiple iterations ; Finally, determine whether the current iteration has reached the global optimum, decide whether to terminate the iteration, and calculate the ternary joint optimization objective function values ​​for the current and previous iterations. relative rate of change The iteration will terminate if any of the following conditions are met: Condition 1, ,Right now When the value is less than the convergence threshold, the objective function converges and the global optimum is reached; Condition 2, iteration rounds The maximum number of iterations is reached.

Images