Bridge structure modeling method and device based on graph data structure

Abstract

This invention provides a bridge structure modeling method and apparatus based on graph data structures, belonging to the field of building information technology. The method includes: constructing a graph data structure for the bridge based on original engineering data; the graph data structure includes a set of nodes and a set of directed edges; the directed edges are associated with nodes in the node set that have dependencies and configured with rule functions; traversing along the dependency directions indicated by the directed edges, and using the rule functions to determine the component attribute data of component nodes and the rebar attribute data of rebar nodes; modeling the bridge to obtain a bridge structure model; and automatically updating the bridge structure model if the attribute data of the target nodes of the bridge structure model changes. This invention organizes nodes through a graph data structure and explicitly expresses mapping relationships and computational logic using directed edges configured with rule functions, thereby breaking the limitations of static parent-child relationships, effectively handling complex multi-directional dependencies between components and rebar, and achieving adaptive and linked updates when model parameters change.

Description

Technical Field

[0001] This invention relates to the field of building information technology, and in particular to a method and apparatus for modeling bridge structures based on graph data structures. Background Technology

[0002] Bridge structural modeling refers to generating models of components such as piers, cap beams, and abutments, as well as steel reinforcement models. It is an important part of the digitalization of bridge engineering design and construction.

[0003] Currently, hierarchical tree-like data structures are typically used to organize model objects for structural modeling. However, this method results in fixed node relationships, leading to only static parent-child relationships between components and reinforcing bars, making it difficult to express complex multi-directional dependencies. Summary of the Invention

[0004] This invention provides a bridge structure modeling method and apparatus based on graph data structure, which solves the defect in the prior art that the node relationship is fixed when using hierarchical tree data structure for structural modeling, resulting in only static parent-child relationship between components and steel bars, making it difficult to express complex multi-directional dependencies.

[0005] This invention uses a graph data structure to uniformly organize component nodes and rebar nodes, and utilizes directed edges configured with regular functions to connect nodes with dependencies, thus achieving an intuitive expression of the relationship and computational logic between components and rebars. Furthermore, this invention also has the following significant advantages: This invention accurately captures attribute changes of target nodes by introducing a change detection mechanism and triggers local recalculation by performing dependency traversal along directed edges, thus avoiding global reconstruction in traditional methods. At the same time, it introduces a path length threshold control mechanism to intelligently define the boundary between local recalculation and global synchronization in extremely complex dependency networks, thereby maximizing the computational performance and response rate of large bridge models.

[0006] This invention adopts a three-dimensional node structure of "type-attribute-identifier" and a five-element structure of directed edges including priority information, which finely maps the real construction logic in bridge engineering; the underlying storage structure can rely on graph database adjacency lists and other storage structures to achieve efficient storage and fast topology retrieval of massive nodes and complex multi-directional dependencies.

[0007] This invention utilizes rule functions for automated parameter derivation while simultaneously performing constraint checks and conflict warnings, ensuring that the generated bridge structure model fully complies with engineering design specifications in terms of geometric dimensions and layout rules. By combining the globally unique identifiers of nodes with semantically related edges, it establishes a complete lifecycle data traceability chain from design and manufacturing to quality inspection.

[0008] This invention provides a bridge structure modeling method based on graph data structures, comprising the following steps: A graph data structure for the bridge is constructed based on the original engineering data; the graph data structure includes a set of nodes and a set of directed edges. The node set includes component nodes representing bridge components and rebar nodes representing rebar objects; the directed edge set includes directed edges, which are associated with nodes in the node set that have dependencies, and are configured with rule functions; the rule functions represent the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes. Traverse along the dependency direction indicated by the directed edge, and use the rule function to determine the component attribute data of the component node and the rebar attribute data of the rebar node; Based on the component attribute data and the rebar attribute data, the bridge is modeled to obtain a bridge structure model; wherein, if the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node, the target node includes component nodes, rebar nodes and processing nodes.

[0009] According to the present invention, a bridge structure modeling method based on graph data structure, wherein automatically updating the bridge structure model based on the target node includes: Starting from the target node, perform a dependency traversal along the directed edge to obtain the set of downstream nodes of the target node; By using the rule function configured on the target directed edges pointing to each node in the downstream node set, the local attributes of the nodes in the downstream node set are recalculated to obtain the updated bridge structure model.

[0010] According to the bridge structure modeling method based on graph data structure provided by the present invention, the step of recalculating the local attributes of the nodes in the downstream node set to obtain an updated bridge structure model includes: The nodes in the downstream node set are added to the update queue, which is a queue structure built based on first-in-first-out logic; The parameter update steps are executed iteratively until the update queue is empty, resulting in the updated bridge structure model. The parameter update steps include: The current processing node is obtained from the update queue, and the attribute data of the current processing node is recalculated and updated using the rule function configured with the target directed edge pointing to the current processing node. If the updated current processing node is associated with a next-level directed edge, then obtain the next-level downstream node pointed to by the next-level directed edge; Determine whether the update queue already contains the next-level downstream node, and whether the next-level downstream node has been marked as updated in the current adaptive update process; If the update queue does not contain the next-level downstream node, and the next-level downstream node is not marked as updated, then the next-level downstream node pointed to by the next-level directed edge is added to the update queue.

[0011] According to the present invention, a bridge structure modeling method based on graph data structure is provided, wherein the rule function includes a first rule function and a second rule function; the construction of the graph data structure of the bridge based on the original engineering data includes: Based on the original engineering data, the component nodes and the rebar nodes are generated. The rebar nodes include rebar type nodes, rebar group nodes, and single rebar nodes. Configure the data structure of the directed edge, which includes the starting node identifier, the target node identifier, the dependency type, the rule function reference, and the priority information; Establish a geometrically dependent directed edge between the component node and the rebar group node, and configure the first rule function for the geometrically dependent directed edge; the first rule function characterizes the mapping relationship between the component attribute data of the component node and the rebar arrangement parameters of the rebar group node; Establish a first rule constraint directed edge between the rebar type node and the rebar group node. The first rule constraint directed edge represents the constraint relationship between the rebar category attribute and the rebar arrangement parameter. A second rule-constrained directed edge is established between the rebar group node and the single-reinforced node, and a second rule function is configured for the second rule-constrained directed edge; the second rule function characterizes the mapping relationship between the rebar arrangement parameters and the rebar entity parameters of the single-reinforced node; The graph data structure of the bridge is constructed based on all the component nodes, the rebar nodes, the geometrically dependent directed edges, the first rule-constrained directed edges, and the second rule-constrained directed edges.

[0012] According to the bridge structure modeling method based on graph data structure provided by the present invention, the rebar attribute data includes rebar category attributes, rebar layout parameters, and rebar entity parameters; the step of determining the component attribute data of the component nodes and the rebar attribute data of the rebar nodes using the rule function includes: Based on the geometrically dependent directed edges, the component attribute data of the component nodes are input into the first rule function to obtain the reinforcement layout parameters output by the first rule function; Based on the first rule constraining the directed edges, obtain the rebar category attribute of the rebar type node, and pass the rebar category attribute to the rebar group node; Based on the second rule constraining the directed edges, the rebar arrangement parameters of the rebar group node and the rebar category attributes are input into the second rule function to obtain the rebar entity parameters output by the second rule function.

[0013] According to the bridge structure modeling method based on graph data structure provided by the present invention, the node set further includes quality inspection nodes; the graph data structure for constructing the bridge based on the original engineering data further includes: Establish a first semantic association directed edge between the component node and the rebar node, wherein the first semantic association directed edge represents the data tracing path between the component node and the rebar node; Establish a second semantic association directed edge between the rebar node and the processing node and / or the quality inspection node. The second semantic association directed edge represents the business logic association path between the rebar node and the downstream production and inspection links. Based on all the component nodes, the rebar nodes, the processing nodes, the quality inspection nodes, the geometrically dependent directed edges, the first rule-constrained directed edges, the second rule-constrained directed edges, the first semantically associated directed edges, and the second semantically associated directed edges, the graph data structure of the bridge is constructed.

[0014] According to the bridge structure modeling method based on graph data structure provided by the present invention, after recalculating the local attributes of the nodes in the downstream node set to obtain the updated bridge structure model, the method further includes: Identify the attribute update node, assign a target version identifier to the attribute update node, and obtain the globally unique identifier of the attribute update node; wherein, the globally unique identifier is an identity code pre-assigned to each node in the node set when constructing the graph data structure; Obtain the attribute difference data of the attribute update node before and after the update, as well as the dependency path information of the attribute update node during the traversal process; Based on the target version identifier, the globally unique identifier, the attribute difference data, and the dependency path information of the attribute update node, differential version data of the bridge structure model is generated. Based on the differential version data, in response to the user's query command, the change history of the bridge structure model is queried and compared by node dimension, component dimension, or time dimension.

[0015] The present invention also provides a bridge structure modeling device based on graph data structure, comprising the following modules: The graph structure determination module is used to construct a graph data structure for the bridge based on the original engineering data. The graph data structure includes a set of nodes and a set of directed edges. The set of nodes includes component nodes representing the bridge's constituent parts and rebar nodes representing rebar objects. The set of directed edges includes directed edges that are associated with nodes in the set that have dependencies, and are configured with rule functions. The rule functions represent the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes. The parameter determination module is used to traverse along the dependency direction indicated by the directed edge and use the rule function to determine the component attribute data of the component node and the rebar attribute data of the rebar node. The model generation module is used to model the bridge based on the component attribute data and the rebar attribute data to obtain a bridge structure model; wherein, if the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node, the target node including component node, rebar node and processing node.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the bridge structure modeling method based on graph data structure as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the bridge structure modeling method based on graph data structure as described above.

[0018] The bridge structure modeling method and apparatus based on graph data structure provided by this invention organizes nodes through graph data structure and uses directed edges of configuration rule functions to explicitly express mapping relationships and calculation logic, thereby breaking the limitations of static parent-child relationships, effectively handling complex multi-directional dependencies between components and steel bars, and realizing adaptive linkage updates when model parameters change. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is one of the flowcharts illustrating the bridge structure modeling method based on graph data structure provided by the present invention.

[0021] Figure 2 This is a schematic diagram of the graph data structure for constructing a bridge provided by the present invention.

[0022] Figure 3 This is a flowchart illustrating the process of determining differential version data of a bridge structural model provided by the present invention.

[0023] Figure 4 This is the second flowchart illustrating the bridge structure modeling method based on graph data structure provided by the present invention.

[0024] Figure 5 This is a node relationship diagram of the graph data structure provided by the present invention.

[0025] Figure 6 It is the edge relationship graph of the graph data structure provided by this invention.

[0026] Figure 7 This is one of the schematic diagrams of change detection and dependency traversal provided by the present invention.

[0027] Figure 8 This is the second schematic diagram of change detection and dependency traversal provided by the present invention.

[0028] Figure 9 This is a schematic diagram of rule recalculation and attribute refresh provided by the present invention.

[0029] Figure 10 This is a schematic diagram of the local recalculation modeling provided by the present invention.

[0030] Figure 11 This is a schematic diagram of the version record modeling provided by the present invention.

[0031] Figure 12 This is a schematic diagram of the bridge structure modeling device based on graph data structure provided by the present invention.

[0032] Figure 13 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] It should be noted that, in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0035] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0036] To facilitate a full understanding of the technical solution of this application, the following content is hereby introduced: With the rapid development of digitalization in bridge engineering design and construction, Building Information Modeling (BIM) technology has been widely applied to bridge structural modeling, construction simulation, and quantity calculation. In existing bridge BIM systems, models of bridge components such as piers, cap beams, and abutments are typically generated using parametric modeling, while the rebar models representing rebar objects are generated through plugins or scripts based on the component geometry. However, traditional BIM platforms generally organize model objects based on a hierarchical tree-like data structure, with fixed node relationships and unidirectional dependencies. This results in only static parent-child relationships between components and rebar, making it difficult to express complex multidirectional dependencies. When the geometric parameters of upper-level components change, the rebar model often cannot be automatically updated, requiring manual rearrangement or complete reconstruction. This not only leads to low modeling efficiency and high data redundancy but also increases the risk of operational errors causing rebar layouts to deviate from specifications.

[0037] Furthermore, while some existing software systems attempt to achieve inter-component linkage through parameter association or constraint expressions, these methods are mostly limited to local constraints or two-dimensional parameter binding, making it difficult to support the complex multi-layered dependencies in bridge substructures. Existing BIM platforms generally use data structures geared towards geometric representation rather than relational modeling, lacking explicit representations of semantic dependencies and layout rules between components. Reinforcement layout rules are scattered across plugins or scripts, failing to form a traceable calculation chain with the model data in the form of rule functions. This leads to parameter inconsistencies during model version iterations and makes it difficult to verify compliance with design specifications.

[0038] Meanwhile, due to limitations in data structure and rule expression, existing parameter updates mostly involve overall recalculation, lacking a local update mechanism based on dependencies. When the model is updated, only all steel reinforcement objects can be recalculated, making local recalculation and incremental updates impossible. This results in large computational loads and slow response times for large bridge structure models, severely impacting practical engineering applications.

[0039] Therefore, this invention provides a bridge structure modeling method based on graph data structure, which realizes an intuitive expression of the relationship and calculation logic between components and steel bars, thereby effectively expressing and handling complex multi-directional dependencies, and finally realizing efficient modeling of bridge structures based on determined attribute data.

[0040] The following is combined Figures 1-13 This invention describes the bridge structure modeling method and apparatus based on graph data structures.

[0041] Figure 1 This is one of the flowcharts illustrating the bridge structure modeling method based on graph data structure provided by the present invention, such as... Figure 1 As shown, the execution subject of the bridge structure modeling method based on graph data structure provided by the present invention can be a server, a cloud computing platform, or a computer capable of executing the method of the present invention, etc. Unless otherwise specified, the following embodiments will be described using a server as an example.

[0042] As an optional embodiment, this bridge structure modeling method based on graph data structure mainly includes, but is not limited to, the following steps: Step 110: Construct the graph data structure of the bridge based on the original engineering data; the graph data structure includes a set of nodes and a set of directed edges.

[0043] The node set includes component nodes representing bridge components and rebar nodes representing rebar objects; the directed edge set includes directed edges, which are associated with nodes in the node set that have dependencies, and are configured with rule functions; the rule functions represent the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes.

[0044] Raw engineering data refers to the basic design parameters or tabular information input into a building information system. It is a set of specifications and parameters used to describe the initial design of a bridge. For example, raw engineering data may be a table of pier component dimensions and a table of steel reinforcement materials.

[0045] The graph data structure of a bridge refers to a network topology model formed by unifying and abstracting the various physical entities of a bridge and the geometric constraints, construction rules and semantic relationships between them. For example, the graph data structure of a bridge can be a structural steel reinforcement graph model with various components and steel reinforcement entities as data nodes and the logical influence relationships between entities as directed edges.

[0046] Bridge components refer to the various basic structural units that make up the main body of a bridge. For example, bridge components can be piers and their constituent geometric entities such as abutments, pier bodies, or cap beams.

[0047] A steel reinforcement object refers to the steel reinforcement material and its collection arranged inside bridge components to enhance structural performance. For example, a steel reinforcement object can be information on steel reinforcement types such as main bars, stirrups, hoops, and prestressed tendons. It can also be a group of main bars or stirrups with the same diameter, spacing, and arrangement rules within the same component. Alternatively, it can be a single steel reinforcement entity that represents attributes such as specific shape code, segment length sequence, and bending angle.

[0048] Nodes with a dependency relationship refer to two node objects that have upstream and downstream constraints or derivations in parameter calculation or physical layout. For example, nodes with a dependency relationship can be pier component nodes and main reinforcement group nodes with a geometric dependency relationship. The cross-sectional dimensions or protective layer parameters of the pier component node will directly affect the main reinforcement spacing and layout requirements of the downstream main reinforcement group node.

[0049] A rule function refers to a predefined mathematical formula or operation rule that covers the core calculation logic of rebar design and layout. For example, a rule function can be a calculation relationship used to describe the anchorage length, lap length, and minimum bending radius of rebar, or a function expression to calculate the number of rebars and the rebar spacing based on the cross-sectional dimensions and protective layer thickness of the relevant components.

[0050] Step 120: Traverse along the dependency direction indicated by the directed edge, and use the rule function to determine the component attribute data of the component node and the rebar attribute data of the rebar node.

[0051] The dependency direction indicated by a directed edge refers to the logical deduction path in a graph data structure, from the upstream node that provides the input parameters to the downstream node that receives the parameters for calculation. Essentially, it represents the order of parameter calculation and causal relationship between various physical entities in bridge engineering design.

[0052] For example, in the modeling of bridge piers, the direction of the directed edge can be a geometric direction pointing from the pier component node to the main reinforcement group node. When traversing along this direction, the known component attribute data (such as pier cross-sectional dimensions and protective layer thickness) from the upstream pier component node is used as input conditions and substituted into the rule function configured on the directed edge (such as the calculation formula for rebar spacing and number). This automatically derives and determines the rebar attribute data (such as the actual spacing and number of main reinforcement bars) of the downstream main reinforcement group node. Subsequently, the system can continue along the direction of dependence from the main reinforcement group node to the individual reinforcement node, substituting the calculated parameters such as the main reinforcement diameter into the next-level rule function to further derive microscopic entity parameters such as the anchorage length and lap length of a single rebar. This achieves fully automated parameter assignment from macroscopic components to microscopic rebars.

[0053] Component attribute data refers to a set of physical parameters that describe the external geometry and internal structural constraints of bridge foundation structural units. For example, component attribute data can be the geometric dimensions, coordinate information, duct coordinates, and protective layer thickness of component nodes such as abutments, piers, or cap beams.

[0054] Reinforcing bar attribute data refers to a set of parameters that characterize the mechanical properties, spatial distribution rules, and specific geometric shapes of reinforcing bar materials. For example, reinforcing bar attribute data can be the material grade and mechanical parameters of reinforcing bar type nodes, the diameter, spacing, quantity, distribution range, and structural requirements of the main bars or stirrups of reinforcing bar group nodes, or specific parameters such as the shape code, segment length sequence, bending angle, minimum bending radius, and anchorage length of single-reinforced nodes.

[0055] Step 130: Based on the component attribute data and the reinforcement attribute data, model the bridge to obtain the bridge structure model; if the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node. The target node includes component nodes, reinforcement nodes and processing nodes.

[0056] A bridge structural model refers to a comprehensive three-dimensional digital representation that integrates the external geometric shape of a bridge with the spatial arrangement of its internal steel reinforcement skeleton. For example, a bridge structural model can be an integrated building information model that includes the geometric model of the piers and the corresponding three-dimensional models of the internal main reinforcement bars, stirrups, and other steel reinforcement bars.

[0057] A processing node refers to a business entity or data object that represents the steel reinforcement required for reference in subsequent engineering stages such as actual production, CNC machining, or quality inspection. For example, a processing node can be a business data node that includes steel reinforcement cutting length, bending process requirements, CNC bending machine processing instructions, or quality inspection standards.

[0058] Specifically, in the initial modeling stage, corresponding rule functions can be executed along the directed edges in the graph data structure to automatically derive and iteratively calculate the parameters between nodes at all levels. Based on the spatial attributes and topological logic of all nodes obtained through derivation, spatial coordinate mapping and geometric reconstruction of the 3D entity are performed, thereby automatically generating a digital 3D structural entity. For example, the spacing and number of downstream rebar nodes can be derived from the cross-sectional dimensions of component nodes along geometrically dependent edges, and the bending parameters and anchorage lengths of downstream monoreinforced nodes can be further derived from the rebar nodes along construction rule edges. After the parameter calculations are completed throughout the entire process, a geometric model of the bridge pier structure and a 3D model of the rebar that conform to design standards in both geometry and logic are automatically generated.

[0059] Furthermore, after the model is built, the system continuously monitors the status of each node. When designers modify the attribute data of a component node (such as adjusting the cross-sectional dimensions of the pier), a rebar node (such as changing the diameter of the main reinforcement), or a processing node (such as changing the bending process requirements), the system locks the node that has undergone substantial changes as the target node. Subsequently, the system breaks away from the traditional global reconstruction model and directly uses the target node as the starting point to perform dependency traversal along the directed edges in the graph data structure, triggering only the affected downstream nodes to execute the corresponding rule functions for local attribute recalculation. Through this automatic derivation mechanism based on graph dependencies, the system can quickly complete the adaptive linkage update of the bridge structure model, which not only significantly reduces the computational load of large models but also ensures the consistency of data across the entire chain from front-end geometric design to back-end processing and manufacturing.

[0060] The bridge structure modeling method based on graph data structure provided by this invention organizes nodes through graph data structure and uses directed edges of configuration rule functions to explicitly express mapping relationships and calculation logic, thereby breaking the limitations of static parent-child relationships, effectively handling complex multi-directional dependencies between components and steel bars, and realizing adaptive linkage updates when model parameters change.

[0061] In another embodiment of the present invention, the bridge structure model is automatically updated based on the target node, including: taking the target node as the starting point, performing dependency traversal along the directed edge to obtain the downstream node set of the target node; using the rule function configured by the target directed edge pointing to each node in the downstream node set, performing local attribute recalculation on the nodes in the downstream node set to obtain the updated bridge structure model.

[0062] Dependency traversal refers to the process of starting from the node where the attribute data has changed and searching for subsequent nodes affected by the change along the logical association direction defined in the graph data structure. For example, it can be achieved by querying all outgoing edges of the target node to obtain all subsequent nodes that directly or indirectly depend on the target node.

[0063] The downstream node set refers to the combination of nodes in a graph data structure whose parameter calculation or geometric arrangement depends on the upstream node attribute data as input conditions. For example, the downstream node set can be the set of main reinforcement nodes affected by the geometric dependence edge when the cross-sectional size of the pier component node changes, as well as the set of stirrup nodes further affected by the distribution of the main reinforcement.

[0064] A target directed edge is an edge that connects an upstream changed node to a downstream affected node, and records a specific dependency type and the execution logic used to recalculate the parameters of the downstream node. For example, a target directed edge can be identified by identifying edge objects in the graph data structure that point to each node in the downstream node set, and configured with rule functions for recalculating the spacing or quantity of rebars.

[0065] Specifically, after capturing a design modification operation and locating the target node whose attributes have changed, a dependency-based local attribute recalculation is triggered. The rule function recalculation is only performed on downstream nodes along the dependent path; nodes on non-dependent paths are not included in the calculation, thus achieving a globally adaptive update driven by local modifications. For example, when the geometric parameters of a bridge pier change, only the cross-sectional dimensions and protective layer parameters of the affected main reinforcement group nodes are recalculated along the target directed edge. The stirrup arrangement of the stirrup group nodes is also recalculated based on the new main reinforcement distribution. The recalculation results are then written back to refresh the attribute set without regenerating the entire reinforcement model or the unaffected component model, significantly reducing computational load and improving response efficiency.

[0066] As an optional implementation, to further optimize computational performance, when performing iterative parameter updates on the bridge structural model, local recalculation based on dependency traversal is only performed globally when the path length of the dependency chain is less than a preset threshold. By introducing path length threshold control, meaningless deep recalculation caused by minor changes at a single point in extremely complex dependency networks can be avoided. This ensures the global consistency of key structural parameters of the model while maximizing the computational performance and response rate of the large bridge architectural information model.

[0067] The bridge structure modeling method based on graph data structure provided by this invention accurately obtains the set of affected downstream nodes by performing dependency traversal along directed edges based on the target node when the attribute data of the target node changes. It then uses the rule function configured on the target directed edge pointing to the downstream node to perform iterative parameter updates. This enables a local automatic recalculation and linkage update mechanism based on graph data dependency relationships, avoiding the need for overall recalculation or manual rearrangement of all objects during modification. This significantly reduces the computational load during large-scale model changes and greatly improves the response efficiency, modification accuracy, and data consistency of model updates.

[0068] In another embodiment of the present invention, iterative parameter updates are performed on the bridge structure model to obtain an updated bridge structure model, including: recalculating the local attributes of the nodes in the downstream node set to obtain an updated bridge structure model, including: adding the nodes in the downstream node set to an update queue, wherein the update queue is a queue structure constructed based on first-in-first-out logic; iteratively executing the parameter update steps until the update queue is empty to obtain an updated bridge structure model.

[0069] The parameter update steps include: obtaining the current processing node from the update queue, and recalculating the attribute data of the current processing node and updating the current processing node using the rule function configured by the target directed edge pointing to the current processing node; if the updated current processing node is associated with a next-level directed edge, then obtaining the next-level downstream node pointed to by the next-level directed edge; determining whether the update queue already contains the next-level downstream node, and whether the next-level downstream node has been marked as updated in the current adaptive update process; if the update queue does not contain the next-level downstream node, and the next-level downstream node has not been marked as updated, then adding the next-level downstream node pointed to by the next-level directed edge to the update queue.

[0070] An update queue is a data structure used to temporarily store and schedule nodes to be processed and computed when model parameters change. For example, an update queue can be a node sequence built based on first-in-first-out (FIFO) logic. In the initial stage, it records nodes marked as changed and dynamically receives downstream nodes affected by dependencies in subsequent processes. The determination of whether the next-level downstream node is already in the queue and whether it has been updated constitutes the queue deduplication and anti-duplication mechanism of this invention, aiming to solve the problem of duplicate computation caused by diamond dependencies or multi-path intersections in complex graph data structures.

[0071] Specifically, by iteratively extracting nodes from the queue and performing rule recalculation and attribute refresh, the system expands its dependency judgment after updating the currently processed node. If there are lower-level nodes affected by it, the system first performs deduplication verification on them. Only if the lower-level node is neither waiting in the queue nor has it been calculated in the current adaptive update loop is it allowed to be enqueued. This achieves hierarchical dependency propagation and local recalculation starting from the initial modified parameters, and ensures that each affected node is calculated only once in a single global update process, avoiding infinite loops and wasted computing power.

[0072] For example, when the cross-sectional dimensions of a bridge pier change, the affected main reinforcement group nodes are added to the update queue. The server retrieves the main reinforcement group nodes and recalculates their spacing and quantity. Since the updated main reinforcement group nodes still have directed edges pointing to downstream stirrup group nodes, the stirrup group nodes are added to the update queue as downstream nodes to continue waiting for processing. At this point, assuming the protective layer thickness of the bridge pier also changes, and the node containing the protective layer thickness also has a dependency edge directly pointing to the aforementioned stirrup group node; when the system traverses along the protective layer thickness path to the stirrup group node and attempts to enqueue it, the deduplication mechanism detects that the stirrup group node already exists in the update queue (or its attributes have been marked as updated), and directly intercepts its request to enqueue again. The system continuously repeats this queue expansion and recalculation process until the affected dependency chains are traversed and the update queue is empty, ultimately achieving globally consistent adaptive updates.

[0073] The bridge structure modeling method based on graph data structure provided by this invention can establish a hierarchical dependency propagation mechanism based on queue expansion by adding nodes in the downstream node set to the update queue and iteratively executing the steps of extracting the recalculated attribute data of the currently processed node and continuously adding the affected next-level downstream nodes to the queue until the update queue is empty. This enables the full-link automated derivation starting from the initial parameter change, thereby improving processing efficiency by only performing local recalculation on the affected path, while effectively ensuring the global data consistency and logical accuracy of complex bridge models after parameter iteration.

[0074] Figure 2 This is a flowchart illustrating the graph data structure for constructing a bridge provided by the present invention, as shown below. Figure 2 As shown, as another optional embodiment provided by the present invention, the rule function includes a first rule function and a second rule function; the graph data structure of the bridge is constructed based on the original engineering data, including but not limited to the following steps: Step 210: Based on the original engineering data, generate component nodes and reinforcement nodes. Reinforcement nodes include reinforcement type nodes, reinforcement group nodes, and single reinforcement nodes.

[0075] A rebar type node refers to a data abstraction entity used to store the category attributes and mechanical parameters of rebar materials. For example, a rebar type node can be a node representing categories such as main bars, stirrups, and prestressed tendons, and its internal attributes include specific material grades, standard diameters, and mechanical parameters such as tensile strength.

[0076] A rebar group node refers to an abstract entity representing a group of rebars with the same diameter, spacing, and arrangement rules within the same component. For example, a rebar group node can be a data node representing the main reinforcement group or stirrup group of a bridge pier. Its node attributes include information such as rebar diameter, spacing, quantity, distribution range, and structural requirements.

[0077] A single-reinforced node refers to a digitally mapped object representing a single steel bar entity. For example, a single-reinforced node can be a specific main reinforcement node that constitutes a steel bar cage. Its node attributes include parameters such as shape code, segment length sequence, bending angle, bending radius, length, and material grade.

[0078] It should be noted that in the graph data structure provided by this invention, various data nodes, such as component nodes, rebar type nodes, rebar group nodes, and single-reinforcement nodes, can all adopt a three-dimensional structure with type, attributes, and identifiers. Each node has a globally unique identifier for subsequent version tracking and semantic delivery. Furthermore, the structure of directed edges can adopt a five-element structure including the starting node identifier, the target node identifier, dependency type, rule function reference, and priority information. This five-element structure accurately and completely records the geometric dependencies, construction rules, or semantic relationships between nodes.

[0079] Step 220: Configure the data structure of the directed edge. The data structure of the directed edge includes the starting node identifier, the target node identifier, the dependency type, the rule function reference, and the priority information.

[0080] Specifically, by defining a standardized edge structure containing five elements, the system can accurately and completely record the geometric dependencies, construction rules, or semantic relationships between nodes. Among them, priority information is used to determine the order of parameter calculation and weight allocation when the target node has multiple incoming edges (i.e., is constrained by multiple upstream nodes), thereby ensuring the determinism of the computational logic under complex dependency networks.

[0081] For example, in a directed edge data structure, the starting node is identified by the pier component node ID, the target node is identified by the main reinforcement group node ID, the dependency type is defined as "geometric dependency", the rule function references the preset "reinforcement spacing calculation formula", and the priority information can be set to "highest level" to ensure that recalculation is performed based on the pier geometry when changes occur.

[0082] Step 230: Establish geometrically dependent directed edges between component nodes and rebar group nodes, and configure a first rule function for the geometrically dependent directed edges; the first rule function characterizes the mapping relationship between the component attribute data of the component node and the rebar arrangement parameters of the rebar group node.

[0083] Geometrically dependent directed edges refer to directed paths that represent the direct influence of component geometric parameters on reinforcement arrangement parameters. For example, a geometrically dependent directed edge can be a connection path from a pier component node to a main reinforcement group node, used to transmit the logic of the influence of pier cross-sectional dimensions on main reinforcement spacing or protective layer on reinforcement clear spacing.

[0084] Reinforcement arrangement parameters refer to specific values ​​used to define the spatial arrangement and quantity distribution characteristics of reinforcement bars within a structural member. For example, reinforcement arrangement parameters can be the spacing between reinforcement bars at the main reinforcement group nodes, the calculated number of reinforcement bars, and the distribution range affected by the thickness of the protective layer.

[0085] The first rule function can be represented by the following formula: ; in, This refers to the actual spacing of the reinforcing bars. These are the cross-sectional dimensions of the relevant components. The thickness of the protective layer for the relevant components. This refers to the number of steel bars. The maximum spacing specified by the standard or project.

[0086] As an optional embodiment, the first rule function may further include a rule formula describing the relationship for calculating the number of reinforcing bars. Specifically, the first rule function may also be as shown in the following formula: ; in, This refers to the number of steel bars. These are the cross-sectional dimensions of the relevant components. The thickness of the protective layer for the relevant components. d The diameter of the reinforcing bar. The maximum spacing specified by the standard or project. ceil This is the floor function.

[0087] Step 240: Establish the first rule constraint directed edge between the rebar type node and the rebar group node. The first rule constraint directed edge represents the constraint relationship between the rebar category attribute and the rebar arrangement parameter.

[0088] Specifically, the first rule directed edge is established between the material category and the macroscopic reinforcement set to transfer the basic physical properties of the reinforcement from top to bottom, serving as a prerequisite for the internal layout calculation of the reinforcement group. For example, the first rule can constrain the directed edge to transfer the "reinforcement diameter d" defined in the reinforcement type node to the downstream main reinforcement group node, so that when the main reinforcement group performs overall layout calculations, it can verify whether the clear spacing of the reinforcement meets the code requirements based on the actual physical diameter.

[0089] Step 250: Establish a second rule constraint directed edge between the rebar group node and the single-reinforced node, and configure a second rule function for the second rule constraint directed edge; the second rule function characterizes the mapping relationship between the rebar arrangement parameters and the rebar entity parameters of the single-reinforced node.

[0090] The second rule-constrained directed edge refers to a directed connection path that represents the inherent structural requirements and geometric derivation relationships between different levels or categories of steel reinforcement objects. For example, the second rule-constrained directed edge can be a constraint line that represents the influence of the overall layout parameters of the main reinforcement group nodes (such as the spacing and distribution range of the main reinforcement) on the specific shape of the downstream single reinforcement node. It is used to transform the macroscopic layout requirements into the precise geometric expression of the microscopic single steel reinforcement (such as bending parameters and anchorage length).

[0091] Rebar entity parameters refer to a set of data used to accurately describe the specific geometric shape and processing dimensions of a single rebar entity in three-dimensional space. For example, rebar entity parameters can be the anchorage length, lap length, minimum bending radius, shape code, and segment length sequence of a single rebar node.

[0092] The second rule function can be represented by the following formula: ; in, The anchorage length to be calculated for a single-reinforced joint. This refers to the overlap length. Minimum bending radius, The diameter of the reinforcing bar is obtained from the rebar group node. The coefficient is determined by the specifications or project rules.

[0093] Step 260: Construct the graph data structure of the bridge based on all component nodes, rebar nodes, geometrically dependent directed edges, first rule-constrained directed edges, and second rule-constrained directed edges.

[0094] Specifically, based on the preset bridge modeling logic, all the abstractly generated entity data nodes are connected layer by layer through directed edges carrying mathematical calculation logic to form a complete multi-directional dependency network covering the geometric relationship of components and the internal structural constraints of steel bars.

[0095] For example, taking the component nodes of the abutment or pier as the starting point of the topology, the geometrically dependent directed edges configured with the first rule function point to the corresponding main reinforcement group nodes; at the same time, the attributes of the rebar type nodes are aggregated to the main reinforcement group nodes using the first rule constraint directed edges; and then connected to the single reinforcement nodes through the second rule constraint directed edges configured with the second rule function, thereby completely breaking the traditional static parent-child tree hierarchy and constructing a bridge integrated graph data structure that can realize automatic derivation and iterative calculation of the underlying data logic.

[0096] The bridge structure modeling method based on graph data structure provided by this invention meticulously divides the rebar nodes into rebar group nodes and single-rebar nodes, and specifically establishes directed edges of geometric dependence between component nodes and rebar group nodes, as well as directed edges of rule constraints between rebar group nodes and single-rebar nodes. Corresponding first and second rule functions are configured to construct the graph data structure. This method can finely map the actual construction logic and hierarchical constraints in bridge engineering to the bottom layer of the data structure, establishing a hierarchical automated derivation and calculation chain from the external geometric attributes of components to the overall macroscopic arrangement of rebars, and then to the microscopic specific entities of individual rebars. This significantly improves the accuracy of parameter derivation and the rigor of modeling logic under complex multi-directional dependency networks, ensuring that the final generated bridge structure model fully conforms to engineering design specifications in terms of geometric dimensions and layout rules.

[0097] In another embodiment provided by the present invention, the rebar attribute data includes rebar category attributes, rebar arrangement parameters, and rebar entity parameters; determining the component attribute data of the component node and the rebar attribute data of the rebar node using a rule function includes: inputting the component attribute data of the component node into a first rule function based on geometrically dependent directed edges to obtain the rebar arrangement parameters output by the first rule function; obtaining the rebar category attributes of the rebar type node based on the first rule constraint directed edges, and passing the rebar category attributes to the rebar group node; inputting the rebar arrangement parameters and rebar category attributes of the rebar group node into a second rule function based on the second rule constraint directed edges to obtain the rebar entity parameters output by the second rule function.

[0098] Reinforcing bar category attributes refer to the set of basic data describing the essential characteristics and physical and mechanical properties of reinforcing bar materials. These attributes are usually independent of the specific spatial arrangement of the reinforcing bars within a structural member, but they play a decisive role in the calculation of subsequent individual reinforcing bar parameters (such as the derivation of anchorage length and lap length). For example, reinforcing bar category attributes can be parameters such as the material grade (e.g., HRB400), standard diameter, design value of tensile strength, and surface shape (e.g., ribbed or plain).

[0099] Specifically, following the logical connections established in the graph data structure, the attribute data of upstream nodes are extracted sequentially and substituted into the pre-defined mathematical formulas on the corresponding directed edges for algebraic operations. During this process, downstream nodes (such as rebar group nodes) can simultaneously aggregate parameters transmitted from multiple incoming edges (such as geometric parameters from structural members and material properties from type nodes) for comprehensive calculation, thereby automatically deriving the target parameters required by the downstream nodes. This achieves a step-by-step parameterization and assignment from macroscopic physical components to microscopic single rebars. Furthermore, while automatically calculating, the system can compare the calculation results with built-in standard thresholds, simultaneously performing constraint checks and conflict warnings to ensure that the generated rebar model fully conforms to design standards both geometrically and logically.

[0100] For example, along the geometrically dependent edge, the cross-sectional dimensions and protective layer thickness of the abutment or pier component nodes can be substituted into the first rule function as input conditions to preliminarily calculate the reinforcement arrangement parameters such as the spacing and number of main reinforcement bars for the main reinforcement group node. Simultaneously, along the directed edge constrained by the first rule, the reinforcement category attributes such as the reinforcement diameter defined in the reinforcement type node are passed and assigned to the main reinforcement group node. Subsequently, along the directed edge constrained by the second rule, the overall distribution pattern (reinforcement arrangement parameters) calculated for the main reinforcement group node and the received reinforcement diameter (reinforcement category attribute) are input as known parameters into the second rule function to further derive and calculate the specific anchorage length, lap length, minimum bending radius, and shape code of the corresponding single reinforcement node, thereby establishing a fully automated derivation process from bridge foundation dimensions and material properties to reinforcement processing and forming parameters.

[0101] The bridge structure modeling method based on graph data structure provided by this invention clarifies the steel reinforcement attribute data, including steel reinforcement layout parameters and steel reinforcement entity parameters. By strictly following geometrically dependent directed edges and rule-constrained directed edges, the attribute parameters of upstream nodes are input into the first rule function and the second rule function for step-by-step derivation calculation and parameter acquisition. This enables a fully automated parameter derivation process from macroscopic component geometric features to mesoscopic overall steel reinforcement group layout and then to microscopic individual steel reinforcement specific entity morphology. This effectively avoids data fragmentation and errors caused by traditional manual calculation and decentralized definition, and further improves the calculation efficiency and accuracy of various steel reinforcement parameter derivations in complex bridge structures.

[0102] In another embodiment provided by the present invention, the node set further includes quality inspection nodes; the construction of a graph data structure for the bridge based on the original engineering data further includes: establishing a first semantic association directed edge between component nodes and rebar nodes, wherein the first semantic association directed edge represents the data traceability path between component nodes and rebar nodes; establishing a second semantic association directed edge between rebar nodes and processing nodes and / or quality inspection nodes, wherein the second semantic association directed edge represents the business logic association path between rebar nodes and downstream production and inspection links; and constructing a graph data structure for the bridge based on all component nodes, rebar nodes, processing nodes, quality inspection nodes, geometrically dependent directed edges, first rule constraint directed edges, second rule constraint directed edges, first semantic association directed edges, and second semantic association directed edges.

[0103] A quality inspection node refers to a business data entity used to inspect and evaluate the quality of steel reinforcement processing and binding during bridge construction or precast component production. For example, a quality inspection node may be a data node containing quality acceptance specifications such as allowable deviations in steel reinforcement processing dimensions, sampling standards for the mechanical properties of welded or mechanical connection joints, and requirements for protective layer thickness testing.

[0104] The first semantic association directed edge refers to a non-geometric computational connection path used to characterize the connection between structural components and internally configured reinforcing bars at the business logic and data traceability levels. For example, the first semantic association directed edge can be a data traceability path connecting the nodes of the pier component and the nodes of the internal main reinforcement group, so that the specific component part to which the reinforcing bar belongs can be quickly located in the subsequent operation and maintenance or construction process.

[0105] The second semantic association directed edge refers to the business logic association path used to characterize the steel reinforcement entity data generated by the design and the downstream processing, production and quality inspection links. For example, the second semantic association directed edge can be the instruction transmission path connecting the single reinforcement node and the processing node of the specific processing plant or CNC bending machine, or the acceptance standard matching path connecting the steel reinforcement group node and the quality inspection node. It is used to directly guide the specific production operation and on-site quality acceptance of a single steel reinforcement by using the calculated steel reinforcement morphology parameters.

[0106] Specifically, business entities and logical relationships spanning multiple stages such as design, processing, and quality inspection are fully integrated into the underlying graph data model, forming a comprehensive digital twin network covering the entire lifecycle from geometric dimension derivation to project delivery. For example, after completing the geometric dependency and rule derivation of the foundation components and corresponding reinforcing bars, a first semantic association directed edge is used to establish a mapping relationship between the foundation and the internal reinforcing bars. Then, a second semantic association directed edge is used to directly associate entity parameters such as the specific shape code, bending angle, and segment length sequence of a single main reinforcing bar with the corresponding processing node for automated material cutting. At the same time, it is associated with the quality inspection node to automatically generate the corresponding dimensional inspection list. Thus, at the underlying graph data structure level, it directly supports the open semantic delivery of subsequent cross-professional modules and the traceability of the entire model data.

[0107] The bridge structure modeling method based on graph data structure provided by this invention introduces processing nodes into the graph data structure and further constructs first semantic association directed edges between component nodes and rebar nodes, as well as second semantic association directed edges between rebar nodes and processing nodes. This method can break down data barriers across professional stages of bridge projects, from digital design to processing, manufacturing, construction, and operation and maintenance. It enables semantic information transmission and full lifecycle data traceability between various entity objects in the bridge structure model, thereby effectively enhancing the cross-stage scalability of the model.

[0108] Figure 3 This is a flowchart illustrating the process of determining differential version data for a bridge structural model provided by the present invention, as shown below. Figure 3 As shown, as another optional embodiment provided by the present invention, after recalculating the local attributes of the nodes in the downstream node set to obtain the updated bridge structure model, the method further includes, but is not limited to, the following steps: Step 310: Determine the attribute update node, assign a target version identifier to the attribute update node, and obtain the globally unique identifier of the attribute update node; wherein, the globally unique identifier is an identity code pre-assigned to each node in the node set when constructing the graph data structure.

[0109] An attribute update node refers to a data entity whose internal attribute values ​​or states have undergone substantial changes during model modification or rule recalculation. For example, the main reinforcement group node whose spacing has been recalculated due to changes in the pier's geometric parameters can be determined by monitoring the execution results of the system update queue and comparing the attribute sets before and after the node update.

[0110] A target version identifier is a encoded sequence used to define the stage state of a model or specific node after a particular iteration of modification. Specifically, its generation can be triggered by a single user commit as a transaction. For example, the system can use a hash algorithm (such as SHA-256) to generate a unique version hash value as an identifier for the dataset of all attribute update nodes participating in the local recalculation; alternatively, the system can adopt a semantic versioning rule of "major version number, minor version number, revision number," combined with millisecond-accurate timestamp information, to automatically generate an incrementing target version identifier for each local recalculation operation, ensuring the strict order of the version sequence.

[0111] A globally unique identifier refers to an identity code assigned to each node during the initialization phase of the graph data structure, which remains unique and unchanged throughout the entire project lifecycle. For example, a globally unique identifier can be generated using Universally Unique Identifier (UUID) technology, serving as a universally unique identifier for accurately tracking the source and modification history of parameters of a specific single-reinforcement node throughout the entire process of design, processing, construction, and operation and maintenance.

[0112] Step 320: Obtain the attribute difference data of the attribute update node before and after the update, as well as the dependency path information of the attribute update node during the traversal process.

[0113] Attribute difference data refers to the specific changes in the key parameters of a node before and after the update and recalculation operation. For example, the specific numerical differences in the spacing of main bars or the number of steel bars before and after the change can be extracted by retrieving the historical parameter cache stored before the update and comparing it with the newly generated dataset after the update.

[0114] Dependency path information refers to the series of directed edge connections that the system traverses along the graph data structure from the initial change point to each affected node when a local recalculation is triggered. For example, this dependency path information can be obtained by recording the complete topological traversal route from the pier component node with modified cross-sectional dimensions, through geometrically dependent edges to the affected rebar group node, and then through rule-constrained edges to the single-reinforced node.

[0115] Step 330: Generate differential version data of the bridge structure model based on the target version identifier, globally unique identifier, attribute difference data, and dependency path information of the attribute update node.

[0116] Differential version data refers to a data set that records only the changes between two adjacent model states, rather than a complete copy of the entire model structure. For example, differential version data can be a lightweight incremental update log that only contains the node identifier that has changed, the comparison results of the old and new attributes, the dependency chain it belongs to, and the corresponding version number.

[0117] Specifically, after each local update operation based on dependency propagation is completed, the system extracts the change details of all nodes participating in the recalculation and packages them together with their unique identity code and traversal path to form an incremental record, thereby realizing version control based on differential management.

[0118] For example, when a designer modifies the protective layer thickness of a pier cap and triggers a bridge-wide update, the system no longer regenerates and saves the complete model file of the entire pier and all unaffected reinforcement. Instead, it only solidifies the changes in the attribute update nodes affected by the pier cap and the influence paths between them into a differential version of the data. This significantly reduces the system's storage space consumption and allows designers to retrospectively review the specific impact of a design modification on the reinforcement layout parameters based on this differential data when needed.

[0119] Step 340: Based on the differential version data, responding to the user's query command, query and compare the change history of the bridge structure model by node dimension, component dimension, or time dimension.

[0120] Specifically, because the differential version data stores globally unique identifiers, dependency paths, and timestamps / version numbers in a structured manner, the system can support multi-dimensional, fine-grained data retrieval and tracing: For example, queries can be performed by node dimension: users can enter the globally unique identifier of a specific monoreinforced node, and the system can retrieve differential version data to show in reverse timeline how the shape code or length of the rebar was modified in each design iteration, and which upstream parameter change triggered each modification.

[0121] Furthermore, queries can be performed by component dimension: users can select a pier (component node) in the 3D model, and the system will filter and summarize the attribute difference data of all related steel bar groups and individual bars within the pier in the specified version range based on the dependency path information, so as to intuitively compare the reinforcement changes of the component before and after design optimization.

[0122] It also allows for querying by time dimension: users can set a specific time period or version number range, and the system will extract all differential version data within that interval, highlight the parts that have been changed in the global bridge model, and generate an incremental modification log for that time period, which greatly facilitates collaborative review by the design team and management of engineering change claims.

[0123] The bridge structure modeling method based on graph data structure provided by this invention determines the nodes where attribute updates occur and assigns target version identifiers and obtains globally unique identifiers after iterative updates of model parameters. It extracts attribute difference data before and after the update and dependency path information during the traversal process, thereby generating differential version data of the bridge structure model. This enables lightweight differential management and full-link version traceability of the bridge structure model, effectively avoiding the massive storage space consumption caused by saving the overall model after each local modification. At the same time, it supports accurate querying, comparison, and backtracking of the specific impact of any design change on subsequent reinforcement layout across time or component dimensions, greatly improving the data reliability and operation and maintenance traceability of complex models during design iteration.

[0124] Figure 4 This is the second flowchart illustrating the bridge structure modeling method based on graph data structure provided by the present invention, as shown below. Figure 4 As shown, the adaptive update process of this bridge structure modeling method based on graph data structure starts from the initial step. First, data structure initialization is performed, specifically including the initialization of nodes and edges. Various bridge components and steel reinforcement entities are generated based on the original engineering data, and an integrated graph data structure is constructed by configuring the edges of the relationships. When the model construction is completed or the parameters are modified, the process enters the change detection and dependency traversal stage. The system captures the target node whose attributes have changed by detecting node changes, and traverses the dependency edges from the target node to accurately obtain the set of downstream affected nodes that directly or indirectly depend on the node.

[0125] Subsequently, rule recalculation and attribute refresh operations are performed. The system recalculates the relevant parameters affecting nodes by calling preset rule functions and writes the calculation results back to update node attributes, completing the data synchronization of the current node. Next, the system determines whether the attribute refresh of the current node affects other nodes. If the result is no, it means that the dependency path has been traversed and there are no more affected nodes, and the process ends directly. If the result is yes, it indicates that there are lower-level dependencies, and the system enters the local recalculation stage. By determining the affected scope, the downstream dependency chain is sorted out, further triggering dependency propagation to add relevant nodes to the update sequence, and differential calculation and updates are performed level by level along the edges. After the relevant local recalculation operations are completed, the process enters the version recording stage. The system captures and saves the key attribute and dependency path information that has changed between adjacent versions by recording node differences to generate differential version data. Finally, the process ends after the version traceability information maintenance is completed.

[0126] Figure 5 This is a node relationship graph of the graph data structure provided by the present invention, such as... Figure 5 As shown, it covers a multi-level data object abstraction that ranges from the physical composition of the bridge foundation to the detailed internal steel reinforcement configuration, and establishes a complete node sequence through dependency associations.

[0127] Specifically, the leftmost end of this topological network begins with component nodes, which represent the physical and geometric properties of the piers and their constituent components such as abutments, pier bodies, and cap beams. Connected to the component nodes are reinforcement type nodes, used to store the category attributes and mechanical parameters of reinforcement materials such as main bars, stirrups, hoops, tie bars, mesh, and prestressed tendons. Following the logical derivation, the reinforcement type nodes are further associated with reinforcement group nodes, which abstractly represent a macroscopic set of reinforcements within the same structural component that have the same diameter, spacing, and arrangement pattern. Their internal parameters include reinforcement diameter, spacing, quantity, distribution range, and overall structural requirements. At the end of this relational network, the reinforcement group nodes ultimately point to single-reinforcement nodes. These nodes, as the most microscopic unit of granularity, are used to precisely represent the physical morphological data of a single, independent reinforcement entity. Their parameters cover shape codes, segment length sequences, bending angles, bending radii, lengths, and specific material grades used to guide actual processing and three-dimensional spatial arrangement.

[0128] Figure 6 This is the edge relationship graph of the graph data structure provided by the present invention, such as... Figure 6 As shown, the nodes in the graph data structure are interconnected in multiple dimensions through directed edges of different types to achieve automatic parameter derivation and logical tracing. The edge relationship graph, from left to right, displays component nodes, rebar type nodes, rebar group nodes, and single-reinforcement nodes, and details the specific attribute sets of each type of node.

[0129] The attributes of component nodes include cross-sectional width b, cross-sectional height h, protective layer thickness c, and duct coordinates; the attributes of connected rebar type nodes include rebar diameter d, spacing s, and quantity n; the attributes of rebar group nodes cover spacing s_t and the number of layers L; and the attributes of end-positioned single-reinforcement nodes include the length l of a single rebar entity and shape code F. A complex multi-directional dependency topology network is established between these four types of nodes through geometric dependency edges, construction rule edges, and semantic association edges, respectively.

[0130] Specifically, between component nodes and rebar type nodes, and between rebar type nodes and rebar group nodes, geometric dependency edges for transmitting the influence of geometric constraints, construction rule edges for transmitting internal structural constraints, and semantic association edges for achieving business logic binding are configured simultaneously; while between rebar group nodes and single rebar nodes, construction rule edges for transmitting the overall structural requirements to specific rebar entities, and two semantic association edges for upstream and downstream data traceability are configured.

[0131] Through the comprehensive application of the above-mentioned geometric dependency edges, construction rule edges, and semantic association edges, the system can gradually transform macroscopic geometric parameters such as cross-sectional dimensions and duct coordinates into the number of layers and spacing of steel reinforcement groups, and finally deduce the specific length and shape code of a single steel reinforcement, thereby constructing a comprehensive, detailed and strictly engineering design-compliant parameter dependency flow network in the underlying graph data structure.

[0132] Figure 7 This is one of the schematic diagrams of change detection and dependency traversal provided by the present invention, such as... Figure 7As shown, the system's front-end user interface is primarily used for importing raw engineering data and accurately capturing design changes. Designers can use the system's "Import Bridge Table" function to batch input or modify basic bridge design parameters in a structured table format. This parameter table details key macro-level engineering data such as bridge name, route, and substructure arrangement. During the initial model construction phase, the system reads this raw table data through this interface, automatically verifies its validity, and transforms it into various component nodes in the underlying graph data structure, assigning them corresponding initial geometric attributes. In the adaptive model update phase, this interface serves as the front-end touchpoint for the change detection mechanism. When a designer modifies the span arrangement or substructure form of a specific bridge (e.g., the "Dajianling Bridge") in the table and confirms submission, the system's back-end change detection module captures this interaction in real time. The system accurately locates the corresponding underlying component node based on the modification, identifies a substantial change in its internal attribute values, marks the component node as a "changed node," and pushes it into the update queue as the initial starting point. This process provides a precise trigger source for subsequent deep dependency traversal and local rule recalculation along the graph data structure, ensuring that any minor changes in front-end parameters can be keenly captured by the system and seamlessly transformed into underlying automated update instructions.

[0133] Figure 8 This is the second schematic diagram of change detection and dependency traversal provided by the present invention, as shown below. Figure 8 The diagram illustrates the detailed content of the parameterized table during change detection and dependency traversal. As the carrier of the original engineering data, it defines key geometric constraints of the bridge substructure, such as pier and foundation types (column piers), abutment and foundation types (ribbed abutments), and span arrangement. When designers adjust the geometry, foundation dimensions, or span arrangement of a pier, the system accurately locates the corresponding component node in the graph data structure through a pre-defined mapping relationship. Subsequently, the algorithm initiates a dependency traversal procedure, automatically querying all outgoing edges of the component node in the graph model and identifying downstream reinforcement group nodes that directly or indirectly depend on the geometric parameters. For example, if the specific dimensions in the "Pier and Foundation" column are modified, the system will automatically locate the affected main reinforcement group and stirrup group along the geometric dependency edges and add them to the update queue. This change detection method based on tabular input allows the underlying graph data logic to be presented to designers in an intuitive and easy-to-use engineering language, achieving efficient decoupling and real-time linkage between complex multi-directional dependencies and front-end engineering business logic.

[0134] Figure 9 This is a schematic diagram of the rule recalculation and attribute refresh provided by the present invention, as shown below. Figure 9The diagram illustrates the application of rule recalculation and attribute refresh at the overall bridge layout level. The top of the interface displays a geometric schematic of the bridge's longitudinal section and substructure, while the bottom shows the corresponding parameter control panel. The left panel defines the bridge's macroscopic design parameters (such as center station number, span arrangement, main beam type, pier and abutment types), while the right-hand table details the specific station number, bridge width, and intersection angle of each pier and abutment. Under the graph data-driven modeling logic, each parameter item in the interface corresponds to a specific attribute of a component node in the graph data structure. When a designer modifies a key parameter in the panel (e.g., changing the span arrangement from "7*30" to another value, or changing the pier type) and triggers an update command, the system immediately executes the rule recalculation process. The algorithm first locates the component node that has been changed and automatically extracts the attribute data of that node and its downstream affected nodes (such as main reinforcement group nodes and stirrup group nodes) along the geometric dependency edges and rule constraint edges, substituting them into a preset rule function for algebraic calculations. After the calculation is completed, the system performs an attribute refresh operation, writing the latest calculation results (such as the recalculated rebar spacing, quantity, and length) back to the attribute set of the corresponding node, and synchronously updating the node's version number or timestamp. Through this closed-loop mechanism of rule recalculation and attribute refresh, the system can ensure that any changes in front-end parameters can be mapped to the 3D geometric entity and internal rebar skeleton in real time and accurately, realizing global adaptive updates and data consistency maintenance driven by local modifications.

[0135] Figure 10 This is a schematic diagram of the local recalculation modeling provided by the present invention, as shown below. Figure 10 The diagram illustrates the parametric management interface for the bridge span design phase, detailing key design parameters such as bridge span, associated connection number, associated span number, span length, bridge width, intersection angle, and main girder type. Within the SRG graph algorithm framework proposed in this invention, each row of data corresponds to a component node and its associated attributes at a specific level in the graph data structure. The core value of the local recalculation mechanism lies in the fact that when designers modify only the parameters of a specific connection or span (such as the bridge width or intersection angle of the third span), the system does not trigger a complete reconstruction of the entire bridge model. Instead, based on the directed dependencies defined in the graph data structure, it accurately locates the set of local nodes affected by the change. The algorithm iterates through the outgoing edges of the affected nodes, performing rule recalculation and attribute refresh only on downstream nodes on dependent paths (such as the pier component nodes, main reinforcement group nodes, and single reinforcement nodes corresponding to the span). This local recalculation strategy significantly reduces the computational overhead of large bridge models during parameter iteration, improves system response efficiency, and ensures the stability of model data on non-dependent paths, thereby achieving efficient and accurate local modifications driving global adaptive updates.

[0136] Figure 11This is a schematic diagram of the version record modeling provided by the present invention, such as... Figure 11 As shown, an integrated 3D building information model of a bridge, generated based on graph data, is presented. It encompasses all digital entities, including piers, abutments, main beams, and internal steel reinforcement. Under the version recording and differential management mechanism proposed in this invention, the 3D model is not only a representation of its geometric form but also a snapshot of the underlying graph data structure at a specific time point or design stage. Each physical component and internal steel reinforcement in the model corresponds to a globally unique node in the graph data structure. When designers modify local parameters of the model and trigger adaptive updates, the system extracts the details of attribute update node changes and dependency path information, generates lightweight differential version data, and assigns new target version identifiers to the affected nodes.

[0137] Figure 12 This is a schematic diagram of the bridge structure modeling device based on graph data structure provided by the present invention, as shown below. Figure 12 As shown, it mainly includes, but is not limited to: Graph structure determination module 1210 is used to construct a graph data structure for the bridge based on the original engineering data. The graph data structure includes a set of nodes and a set of directed edges. The set of nodes includes component nodes representing the components of the bridge and rebar nodes representing rebar objects. The set of directed edges includes directed edges, which are associated with nodes in the set of nodes that have dependencies, and are configured with rule functions. The rule functions represent the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes. The parameter determination module 1220 is used to traverse along the dependency direction indicated by the directed edge and use the rule function to determine the component attribute data of the component node and the rebar attribute data of the rebar node. The model generation module 1230 is used to model the bridge based on component attribute data and rebar attribute data to obtain a bridge structure model. If the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node. The target node includes component nodes, rebar nodes, and processing nodes.

[0138] It should be noted that the bridge structure modeling device based on graph data structure provided by the present invention can execute the bridge structure modeling method based on graph data structure described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0139] The bridge structure modeling device based on graph data structure provided by this invention organizes nodes through graph data structure and uses directed edges of configuration rule functions to explicitly express mapping relationships and calculation logic, thereby breaking the limitations of static parent-child relationships, effectively handling complex multi-directional dependencies between components and steel bars, and realizing adaptive linkage updates when model parameters change.

[0140] Figure 13 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 13 As shown, the electronic device may include: a processor 1310, a communications interface 1320, a memory 1330, and a communications bus 1340, wherein the processor 1310, the communications interface 1320, and the memory 1330 communicate with each other through the communications bus 1340. The processor 1310 can call logical instructions in the memory 1330 to execute a bridge structure modeling method based on a graph data structure. This method includes: constructing a graph data structure for the bridge based on original engineering data; the graph data structure includes a set of nodes and a set of directed edges; wherein the set of nodes includes component nodes representing bridge components and rebar nodes representing rebar objects; the set of directed edges includes directed edges, which are associated with nodes in the set of nodes that have dependencies, and configured with rule functions; the rule functions represent the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes; traversing along the dependency direction indicated by the directed edges, and using the rule functions to determine the component attribute data of component nodes and the rebar attribute data of rebar nodes; modeling the bridge based on the component attribute data and the rebar attribute data to obtain a bridge structure model; wherein, if the attribute data of a target node in the bridge structure model changes, the bridge structure model is automatically updated based on the target node, which includes component nodes, rebar nodes, and processing nodes.

[0141] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0142] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the bridge structure modeling method based on graph data structure provided by the above methods. The method includes: constructing a graph data structure of the bridge based on the original engineering data; the graph data structure includes a set of nodes and a set of directed edges; wherein, the set of nodes includes component nodes representing the components of the bridge and rebar nodes representing rebar objects; the set of directed edges includes directed edges, and the directed edges are associated with nodes in the set of nodes that have dependencies, and a rule function is configured; the rule function represents the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes; traversing along the dependency direction indicated by the directed edges, and using the rule function to determine the component attribute data of the component nodes and the rebar attribute data of the rebar nodes; modeling the bridge based on the component attribute data and the rebar attribute data to obtain a bridge structure model; wherein, if the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node, and the target node includes component nodes, rebar nodes, and processing nodes.

[0143] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the bridge structure modeling method based on graph data structure provided by the above methods. The method includes: constructing a graph data structure of the bridge based on original engineering data; the graph data structure includes a set of nodes and a set of directed edges; wherein the set of nodes includes component nodes representing bridge components and rebar nodes representing rebar objects; the set of directed edges includes directed edges, and the directed edges are associated with nodes in the set of nodes that have dependencies, and a rule function is configured; the rule function represents the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes; traversing along the dependency direction indicated by the directed edges, and using the rule function to determine the component attribute data of component nodes and the rebar attribute data of rebar nodes; modeling the bridge based on the component attribute data and the rebar attribute data to obtain a bridge structure model; wherein, if the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node, the target node including component nodes, rebar nodes, and processing nodes.

[0144] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bridge structure modeling method based on a graph data structure, characterized by, include: A graph data structure for the bridge is constructed based on the original engineering data; the graph data structure includes a set of nodes and a set of directed edges. The node set includes component nodes representing bridge components and rebar nodes representing rebar objects; the directed edge set includes directed edges, which are associated with nodes in the node set that have dependencies, and are configured with rule functions; the rule functions represent the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes. Traverse along the dependency direction indicated by the directed edge, and use the rule function to determine the component attribute data of the component node and the rebar attribute data of the rebar node; Based on the component attribute data and the rebar attribute data, the bridge is modeled to obtain a bridge structure model; wherein, if the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node, the target node includes component nodes, rebar nodes and processing nodes.

2. The graph data structure based bridge structural modeling method of claim 1, wherein, The automatic updating of the bridge structure model based on the target node includes: Starting from the target node, perform a dependency traversal along the directed edge to obtain the set of downstream nodes of the target node; By using the rule function configured on the target directed edges pointing to each node in the downstream node set, the local attributes of the nodes in the downstream node set are recalculated to obtain the updated bridge structure model.

3. The bridge structure modeling method based on graph data structure according to claim 1, characterized in that, The step of recalculating the local attributes of the nodes in the downstream node set to obtain the updated bridge structure model includes: The nodes in the downstream node set are added to the update queue, which is a queue structure built based on first-in-first-out logic; The parameter update steps are executed iteratively until the update queue is empty, resulting in the updated bridge structure model. The parameter update steps include: The current processing node is obtained from the update queue, and the attribute data of the current processing node is recalculated and updated using the rule function configured with the target directed edge pointing to the current processing node. If the updated current processing node is associated with a next-level directed edge, then obtain the next-level downstream node pointed to by the next-level directed edge; Determine whether the update queue already contains the next-level downstream node, and whether the next-level downstream node has been marked as updated in the current adaptive update process; If the update queue does not contain the next-level downstream node, and the next-level downstream node is not marked as updated, then the next-level downstream node pointed to by the next-level directed edge is added to the update queue.

4. The bridge structure modeling method based on graph data structure according to claim 1, characterized in that, The rule function includes a first rule function and a second rule function; the graph data structure for constructing the bridge based on the original engineering data includes: Based on the original engineering data, the component nodes and the rebar nodes are generated. The rebar nodes include rebar type nodes, rebar group nodes, and single rebar nodes. Configure the data structure of the directed edge, which includes the starting node identifier, the target node identifier, the dependency type, the rule function reference, and the priority information; Establish a geometrically dependent directed edge between the component node and the rebar group node, and configure the first rule function for the geometrically dependent directed edge; the first rule function characterizes the mapping relationship between the component attribute data of the component node and the rebar arrangement parameters of the rebar group node; Establish a first rule constraint directed edge between the rebar type node and the rebar group node. The first rule constraint directed edge represents the constraint relationship between the rebar category attribute and the rebar arrangement parameter. A second rule-constrained directed edge is established between the rebar group node and the single-reinforced node, and a second rule function is configured for the second rule-constrained directed edge; the second rule function characterizes the mapping relationship between the rebar arrangement parameters and the rebar entity parameters of the single-reinforced node; The graph data structure of the bridge is constructed based on all the component nodes, the rebar nodes, the geometrically dependent directed edges, the first rule-constrained directed edges, and the second rule-constrained directed edges.

5. The bridge structure modeling method based on graph data structure according to claim 4, characterized in that, The rebar attribute data includes rebar category attributes, rebar layout parameters, and rebar entity parameters; the step of using the rule function to determine the component attribute data of the component node and the rebar attribute data of the rebar node includes: Based on the geometrically dependent directed edges, the component attribute data of the component nodes are input into the first rule function to obtain the reinforcement layout parameters output by the first rule function; Based on the first rule constraining the directed edges, obtain the rebar category attribute of the rebar type node, and pass the rebar category attribute to the rebar group node; Based on the second rule constraining the directed edges, the rebar arrangement parameters of the rebar group node and the rebar category attributes are input into the second rule function to obtain the rebar entity parameters output by the second rule function.

6. The bridge structure modeling method based on graph data structure according to claim 4, characterized in that, The node set also includes quality inspection nodes; the graph data structure for constructing the bridge based on the original engineering data also includes: Establish a first semantic association directed edge between the component node and the rebar node, wherein the first semantic association directed edge represents the data tracing path between the component node and the rebar node; Establish a second semantic association directed edge between the rebar node and the processing node and / or the quality inspection node. The second semantic association directed edge represents the business logic association path between the rebar node and the downstream production and inspection links. Based on all the component nodes, the rebar nodes, the processing nodes, the quality inspection nodes, the geometrically dependent directed edges, the first rule-constrained directed edges, the second rule-constrained directed edges, the first semantically associated directed edges, and the second semantically associated directed edges, the graph data structure of the bridge is constructed.

7. The bridge structure modeling method based on graph data structure according to claim 2, characterized in that, After recalculating the local attributes of the nodes in the downstream node set to obtain the updated bridge structure model, the process further includes: Identify the attribute update node, assign a target version identifier to the attribute update node, and obtain the globally unique identifier of the attribute update node; wherein, the globally unique identifier is an identity code pre-assigned to each node in the node set when constructing the graph data structure; Obtain the attribute difference data of the attribute update node before and after the update, as well as the dependency path information of the attribute update node during the traversal process; Based on the target version identifier, the globally unique identifier, the attribute difference data, and the dependency path information of the attribute update node, differential version data of the bridge structure model is generated. Based on the differential version data, in response to the user's query command, the change history of the bridge structure model is queried and compared by node dimension, component dimension, or time dimension.

8. A bridge structure modeling device based on graph data structure, characterized in that, include: The graph structure determination module is used to construct a graph data structure for the bridge based on the original engineering data. The graph data structure includes a set of nodes and a set of directed edges. The set of nodes includes component nodes representing the bridge's constituent parts and rebar nodes representing rebar objects. The set of directed edges includes directed edges that are associated with nodes in the set that have dependencies, and are configured with rule functions. The rule functions represent the mapping relationship between the attribute data of upstream nodes and the attribute data of downstream nodes. The parameter determination module is used to traverse along the dependency direction indicated by the directed edge and use the rule function to determine the component attribute data of the component node and the rebar attribute data of the rebar node. The model generation module is used to model the bridge based on the component attribute data and the rebar attribute data to obtain a bridge structure model; wherein, if the attribute data of the target node of the bridge structure model changes, the bridge structure model is automatically updated based on the target node, the target node including component node, rebar node and processing node.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the bridge structure modeling method based on graph data structure as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the bridge structure modeling method based on graph data structure as described in any one of claims 1 to 7.

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