REVIT-based self-adaptive steel bar modeling method, system and equipment and medium

Abstract

The invention discloses a self-adaptive steel bar modeling method, system and device based on REVIT and a medium, and relates to the technical field of building information models.The method comprises the steps that according to a three-dimensional grid chart of the surface of a target component, target paths of all steel bars in the target component are obtained through fitting; parameterizing each target path into a spline curve, and projecting the spline curve to the concrete surface of the target component; according to each projection result, changing nodes in the corresponding spline curve are updated, and each updated spline curve is determined as a steel bar model of the target component; the steel bar copying and transferring device solves the limitation of steel bar copying and transferring in the prior art, can conduct automatic steel bar copying between different components and concrete bodies of different shapes, automatically adapts to the shape of a target component, and ensures accurate matching of the position and the shape of the steel bar.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and more specifically, it relates to an adaptive steel bar modeling method, system, device and medium based on REVIT. Background Art

[0002] In traditional building design, steel bar modeling mainly relies on manual operation or building with fixed rules. With the increasing complexity of structural design, existing modeling methods often require manual adjustment, which is inefficient and error-prone. Especially when copying steel bars, traditional methods cannot well handle the differences between different concrete components, resulting in a cumbersome copying process and an inability to efficiently adapt to structural changes.

[0003] Although Revit, a BIM software widely used in building design, has certain steel bar modeling capabilities, it cannot automatically handle the precise copying and adaptive transfer of steel bars in complex structures when facing a changing and complex construction environment. Therefore, there is an urgent need for a more intelligent and automated steel bar modeling technology that can efficiently and accurately perform adaptive copying and transfer of steel bars, reduce manual intervention, and improve the efficiency of design and construction. Summary of the Invention

[0004] The purpose of the present invention is to provide an adaptive steel bar modeling method, system, device and medium based on REVIT, to solve the limitations of steel bar copying and transfer in the prior art, and to be able to perform automated steel bar copying between different components and concrete bodies with different shapes, and automatically adapt to the shape of the target component to ensure the precise matching of the position and form of the steel bars.

[0005] The above technical purpose of the present invention is achieved through the following technical solutions:

[0006] In the first aspect, the present application provides an adaptive steel bar modeling method based on REVIT, including the following specific steps:

[0007] According to the three-dimensional mesh diagram of the surface of the target component, fit the target paths of each steel bar in the target component;

[0008] Parameterize each target path into a spline curve and project the spline curve onto the concrete surface of the target component;

[0009] Update the changed nodes in the corresponding spline curves according to each projection result, and determine the updated spline curves as the steel bar model of the target component.

[0010] On the basis of the above technical solutions, the present invention can also be improved as follows.

[0011] Further, the target paths of each steel bar in the above target component are specifically:

[0012] P output = [(p1, W(p1)), (p2, W(p2)), (p3, W(p3))…(p n , W(p n )) +;

[0013] In the formula, P output represents the target path of one of the steel bars, which is formed by successively connecting the nodes p1, p2, p3, …, p n on the three-dimensional grid diagram in three-dimensional space, and the coordinates of p n on the three-dimensional grid diagram in three-dimensional space are (x n , y n , z n ); W(p n ) represents the cumulative weight from the starting point of the steel bar to the node p n .

[0014] Furthermore, the above cumulative weight is specifically:

[0015] where 1 ≤ i ≤ n;

[0016] In the formula, represents the total path weight, and W(p i ) represents the cumulative weight from the starting point of the steel bar to the node p i .

[0017] Furthermore, the above total path weight is specifically:

[0018] W(p) = α·L(p) + β·C(p) + γ·A(p);

[0019] In the formula, W(p) represents the total path weight, L(p) is the total path length, C(p) is the curvature penalty term, A(p) is the fitting penalty term, α, β, γ are weight coefficients, α, β, γ ∈ [0, 1], and α + β + γ = 1.

[0020] Furthermore, in the above formula for calculating the total path weight, the total path length is specifically:

[0021] Among them:

[0022] In the formula, L(p) is the total path length, and ‖p i+1 - p i ‖ represents the Euclidean distance between adjacent nodes p i+1 and p i ;

[0023] The curvature penalty term is specifically:

[0024] Among them:

[0025] In the formula, k i is the discrete curvature at node p i , C(p) is the curvature penalty term, and × represents the vector cross product;

[0026] The fitting penalty term is specifically:

[0027] Among them: d(p i ) = min q∈s ‖p i - q‖;

[0028] In the formula, A(p) is the fitting penalty term, and d(p i ) represents the distance from node p i to the concrete surface S, S represents the three-dimensional point set of the concrete surface of the target member, and q represents the three-dimensional point set of the concrete surface of the target member.

[0029] Furthermore, the above spline curve is specifically:

[0030]

[0031] In the formula, P i represents the node set of the target path formed by each node, U = {u0, u1…u m} satisfies m = n + p + 1, and u0 ≤ u1 ≤ …u m , N i , p (u) represents the i-th p-th B-spline basis function, where:

[0032]

[0033] Furthermore, the above projection result is specifically:

[0034] C proj (u) = argmin q∈S ‖C(u) - q‖, or C proj (u) = C(u) - d(u)·n(u);

[0035] In the formula, C(u) represents the spline curve, C proj (u) represents the projection result of the spline curve, q represents the point closest to the spline curve, d(u) represents the signed distance from node C(u) to the concrete surface S and is positive outside and negative inside, and n(u) represents the unit normal vector of the concrete surface S at C proj (u);

[0036] In each of the updated spline curves, the updated nodes satisfy:

[0037]

[0038] wherein, represents the updated node.

[0039] In a second aspect, the present application provides an adaptive steel bar modeling system based on REVIT, which is applied to the adaptive steel bar modeling method based on REVIT in any one of the first aspects, and includes:

[0040] A first module, configured to fit the target paths of each steel bar in the target member according to the three-dimensional mesh diagram of the surface of the target member;

[0041] A second module, configured to parameterize each target path into a spline curve and project the spline curve onto the concrete surface of the target member;

[0042] A third module, configured to update the changed nodes in the corresponding spline curve according to each projection result, and determine the updated spline curves as the steel bar model of the target member.

[0043] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method in any one of the first aspects is implemented.

[0044] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, and the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method in any one of the first aspects.

[0045] Compared with the prior art, the present invention has at least the following beneficial effects:

[0046] Through intelligent algorithms and adaptive steel bar modeling technology, the present invention realizes the accurate replication and adaptive transfer of steel bars in complex structures. Its main advantages include:

[0047] Improve modeling efficiency: The automated steel bar replication and transfer technology reduces manual operations and improves modeling efficiency.

[0048] Reduce human errors: Through intelligent algorithms, the system can accurately transfer and replicate steel bars, reducing human errors.

[0049] Adapt to complex structural changes: The system can cope with changes in different concrete bodies and different member forms, ensuring the accuracy and consistency of the steel bar model.

[0050] Enhanced operation flexibility: Users can choose to copy all the steel bars or individual steel bars, providing flexible operation options. Description of the Drawings

[0051] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0052] Figure 1 is the method flow chart of the modeling method in the embodiments of the present invention;

[0053] Figure 2 is the connection schematic diagram of the modeling system in the embodiments of the present invention;

[0054] Figure 3 is the connection schematic diagram of the electronic device in the embodiments of the present invention. Detailed Embodiments

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0057] It should be noted that: Similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0058] In the description of the embodiments of the present invention, "a plurality of" represents at least 2.

[0059] Embodiment 1: To solve the problem of being unable to automatically process the precise replication and adaptive transfer of steel bars in complex structures in the face of a changeable and complex construction environment, and to achieve efficient and accurate adaptive replication and transfer of steel bars, reduce manual intervention, and improve the efficiency of design and construction, this embodiment provides an adaptive steel bar modeling method based on REVIT, as Figure 1 shown, including the following specific steps:

[0060] S1. Based on the three-dimensional mesh diagram of the surface of the target component, fit the target paths of each steel bar in the target component.

[0061] Among them, before fitting the target path, the geometric differences between the source component and the target component, including features such as shape, size, and surface, can be dynamically calculated through a three-dimensional geometric difference recognition algorithm to generate preliminary suggestions for steel bar adaptation. First, the geometric data of the source component and the target component, including vertices, edges, faces, etc., can be extracted from the Revit model. Second, a point cloud matching algorithm or three-dimensional volume difference calculation can be used to identify the shape differences by comparing the geometric forms between the two. For example: Angle difference: When there are different angles between the source component and the target component, the algorithm can identify the need to bend or adjust the angle of the steel bar. Shape difference: By comparing the differences in surfaces or polygons, calculate the shortest path or the maximum matching points to deduce the required shape of the steel bar.

[0062] Specifically, the above-mentioned point cloud mapping technology can be used to calculate the geometric differences between the two based on the three-dimensional coordinate data of the source component and the target component through a point cloud registration algorithm. If the component shape has multiple irregular surfaces or complex curves, the least squares fitting algorithm is used to calculate the geometric path most suitable for steel bar replication.

[0063] Furthermore, when calculating the path, the input is the geometric surface data of the source component and the target component, as well as the positions of the starting point and the ending point of the steel bar in the source component. The surface can be discretized into three-dimensional grid nodes, and each node represents a possible steel bar path point. Weights (such as distance, curvature penalty coefficient) can also be assigned to the edges between adjacent nodes according to factors such as the bending radius limit of the steel bar and the degree of fit with the concrete surface: In the formula, d ij represents the Euclidean distance between node i and node j, θ ij is the curvature angle of the path between the two nodes, λ is the weight balance factor, and R min is the minimum allowable bending radius of the steel bar.

[0064] Among them, when calculating the shortest weighted path from the starting point to the ending point with the starting point of the steel bar as the initial node and the ending point as the target node, the path needs to meet the following constraints: 1. The path curvature does not exceed the allowable bending radius of the steel bar; 2. The path maintains the minimum cover thickness from the concrete surface. Optionally, the target paths of each steel bar in the above-mentioned target component are specifically:

[0065] P output =[(p1, W(p1)), (p2, W(p2)), (p3, W(p3))…(p n , W(p n ))+;

[0066] In the formula, P output represents the target path of one of the steel bars, which is formed by successively connecting nodes p1, node p2, node p3, …, node p n in a three-dimensional grid diagram, and the coordinates of p n in the three-dimensional grid diagram in three-dimensional space are (x n , y n , z n ); W(p n ) represents the cumulative weight from the starting point of the steel bar to node p n .

[0067] Specifically, the objective function of the above steel bar path aims to minimize the weighted sum of geometric differences and path curvature, that is:

[0068] In the formula, P represents the parametric curve of the steel bar path, ΔG i represents the geometric difference vector of the i-th node, including position, direction, curvature difference, etc.; λ is the curvature penalty weight coefficient, used to balance geometric matching and path smoothness, k j represents the curvature of the j-th node on the path, n represents the number of sampling points on the surface of the target component, and m is the number of discretized nodes on the path.

[0069] Among them, the above cumulative weight is specifically:

[0070] where 1 ≤ i ≤ n;

[0071] In the formula, represents the total path weight, and W(p i ) represents the cumulative weight from the starting point of the steel bar to node p i .

[0072] Furthermore, the above total path weight is specifically:

[0073] W(p) = α·L(p) + β·C(p) + γ·A(p);

[0074] In the formula, W(p) represents the total path weight, L(p) is the total path length, C(p) is the curvature penalty term, A(p) is the fitting penalty term, and α, β, γ are weight coefficients, α, β, γ ∈ [0, 1], and α + β + γ = 1.

[0075] Among them, in the calculation formula of the above total path weight, the total path length is specifically:

[0076] where:

[0077] In the formula, L(p) is the total path length, ‖pi+1 -p i ‖ represents the Euclidean distance between adjacent node p i+1 and p i ;

[0078] The curvature penalty term is specifically:

[0079] Where:

[0080] In the formula, k i is the discrete curvature at node p i , C(p) is the curvature penalty term, and × represents the vector cross product; among them, the curvature constraint can introduce the Lagrange multiplier, that is: Where μ is the Lagrange multiplier, κ(P) is the curvature of the fitting path, and κ allow represents the maximum allowable curvature.

[0081] Furthermore, a curvature constraint can be introduced to ensure that the fitted path meets the physical bending limit of the steel bar. The curvature constraint is optimized as: Where T k is the tangent vector and Δs is the arc length parameter step size.

[0082] The fitting degree penalty term is specifically:

[0083] Where: d(p i ) = min q∈s ‖p i -q‖;

[0084] In the formula, A(p) is the fitting degree penalty term, and d(p i ) represents the distance from node p i to the concrete surface S. S represents the three-dimensional point set of the concrete surface of the target member. q represents a set that contains all the three-dimensional coordinate points describing the concrete surface. When calculating the distance d(pi) from node pi to the concrete surface S, q, as an element in the set, represents the possible nearest point on the concrete surface; the distance d(pi) is determined by finding the minimum distance from node pi to all points q in the set q, that is, d(pi) = min||pi - q||, where ||pi - q|| represents the Euclidean distance from node pi to point q. Therefore, the parameter q is used in this mathematical function to define the position of the concrete surface, so that the distance from the node to the surface can be calculated. Then, in the mathematical function, the parameter q is the three-dimensional point set describing the concrete surface of the target member.

[0085] Optionally, the above target path can also be optimized and constrained, including local smoothing optimization:

[0086] ΔP j is the path adjustment amount of the j-th node, γ represents the smoothing term weight, and M represents the total number of path nodes; the bending angle constraint is less than or equal to the maximum bending angle allowed by the process.

[0087] S2, parameterize each target path as a spline curve and project the spline curve onto the concrete surface of the target member.

[0088] Among them, the above spline curve can be expressed as:

[0089]

[0090] In the formula, P i represents the node set of the target path formed by each node, U = {u0, u1…u m}, satisfying m = n + p + 1, and u0 ≤ u1 ≤ … u m , N i , p (u) represents the i-th p-th B-spline basis function, where:

[0091]

[0092] In the above, the basis function N i，p (u) is always 0 outside the interval [U i , U i+p+1 , and at the same time, fitting the discrete shortest path point sequence P = {p1, p2, …, p n} to a B-spline curve needs to satisfy: Among them, u k is the parameter value, which can be assigned by chord length parameterization or centripetal parameterization.

[0093] Specifically, curve fitting optimization operations can also be performed, which can be:

[0094] c(u) represents the parameterized curve, Q i is the i-th fitting point (projection point) on the surface of the target member; μ represents the smoothing term weight, which controls the second derivative of the curve (curve change rate); N represents the number of fitting points; for the curvature constraint, it is less than or equal to the maximum curvature allowed by the steel bar material.

[0095] Furthermore, project the spline curve onto the concrete surface of the target member to ensure that the steel bar path is close to the concrete surface, avoid a decrease in structural strength caused by suspension or insufficient embedding, and at the same time strictly constrain the parameterized B-spline curve on the surface of the target member to meet construction feasibility.

[0096] Optionally, the projection of the point C(u) of the B-spline curve onto the nearest point on the concrete surface S:

[0097] C proj (u) = argmin q∈S ‖C(u) - q‖。

[0098] Or project along the surface normal vector: C proj (u) = C(u) - d(u)·n(u);

[0099] 1. In the formula, C(u) represents a spline curve, and C proj (u) represents the projection result of the spline curve, q represents the point corresponding to the shortest distance (i.e., projection) from the point C(u) on the spline curve to the concrete surface S. This point is on the concrete surface and is the q value that minimizes ∥C(u) - q∥. At the same time, although signed distance is mentioned, in the description of this text segment, q itself represents a point, not a distance value; then the definition of the parameter q in the picture is: on the concrete surface, the point closest to the spline curve point C(u), d(u) represents the signed distance from the node C(u) to the concrete surface S, which is positive outside and negative inside, and n(u) represents the unit normal vector of the concrete surface S at C proj (u). If the projection causes a change in the curve shape, the control points p need to be iteratively updated i And satisfy:

[0100] In the formula, represents the updated node.

[0101] Specifically, when B-spline fitting may cause the path to deviate from the surface, it can be solved by forced projection operation + control point iterative optimization; when the smoothness of the curve decreases after projection (such as curvature mutation), it can be solved by adding a curvature penalty term to the optimization objective, that is: min∑‖C proj (u k ) - p k ‖ 2 + λ∫k 2 (u)du, where k(u) is the curve curvature and λ is the smoothness weight.

[0102] Among them, an error function can also be set in the above projection process. When the error function does not exceed the threshold, the projection structure is further processed. The error function can be: πs(·) is the orthogonal projection onto the surface S.

[0103] Optionally, the least squares method is used to perform curve fitting on the projected points to generate a smooth adapted path, and the goal is to minimize the distance error between the fitted path and the target surface; the distance error between the minimum fitted path and the target surface can be expressed as:

[0104] where n represents the total number of observation points in the dataset, that is, the number of discrete data pairs (x1, y1), (x2, y2) … (x n , y n ) used for fitting, δ i is the vertical distance from the i-th projection point to the target surface, and σ represents the noise standard deviation.

[0105] S3. Update the changed nodes in the corresponding spline curves according to each projection result, and determine the updated spline curves as the steel bar models of the target component.

[0106] Specifically, the above steps can locally optimize the positions of the steel bars, ensure that the spacing of the steel bars meets the design requirements, and avoid interference between the steel bars and other components; the method includes functions such as spacing inspection, adjustment, interference detection and adjustment, etc., which are applicable to the optimization of steel bar layout, and achieve the purposes of improving the modeling efficiency, reducing human errors, adapting to complex structural changes and enhancing operation flexibility.

[0107] Embodiment 2: The present application provides an adaptive steel bar modeling system based on REVIT, as Figure 2 shown, which is applied to the adaptive steel bar modeling method based on REVIT in Embodiment 1 and includes:

[0108] The first module is used to fit the target paths of each steel bar in the target component according to the three-dimensional mesh diagram of the target component surface;

[0109] The second module is used to parameterize each target path into a spline curve and project the spline curve onto the concrete surface of the target component;

[0110] The third module is used to update the changed nodes in the corresponding spline curves according to each projection result, and determine the updated spline curves as the steel bar models of the target component.

[0111] Specifically, the above system can also be composed of the following. The steel bar adaptive modeling system based on Revit of the present invention mainly consists of the following modules:

[0112] User Interface Module (UI Module): Provides an interactive interface for users to perform steel bar modeling operations. Through the Revit plug-in, users can directly operate the system on the Revit interface, select the steel bars or components to be copied, view the copy results, and adjust the parameter settings.

[0113] Data Input Module: Responsible for extracting the original data from the Revit model, obtaining the component information, steel bar information, and geometric shape of the concrete main body. These data will be passed as inputs to the adaptive algorithm module for subsequent calculations and modeling.

[0114] Adaptive Algorithm Module: The core module responsible for executing the adaptive transfer and replication process of steel bars. This module analyzes the differences between the target component and the original component through intelligent algorithms and generates a new steel bar model that conforms to the shape of the target component.

[0115] Rebar Copy and Transfer Module: Responsible for executing the actual copy and transfer operations of steel bars. By parsing the parameters from the Adaptive Algorithm Module, it performs operations such as rebar copying, transfer, and adaptation.

[0116] Data Output Module: Outputs the generated new steel bar model and the modified component information back to the Revit model, updates the Revit file, and ensures the synchronization and consistency of the model.

[0117] Embodiment 3: The present application provides an electronic device, as Figure 3 shown, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method of Embodiment 1.

[0118] Embodiment 4: The present application provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to execute the method of Embodiment 1.

[0119] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An adaptive steel bar modeling method based on REVIT, characterized in that It includes the following specific steps: According to the three-dimensional mesh diagram of the surface of the target component, the target paths of each steel bar in the target component are fitted. Each of the target paths is parameterized into a spline curve, and the spline curve is projected onto the concrete surface of the target component. According to each projection result, the changed nodes in the corresponding spline curve are updated, and each updated spline curve is determined as the steel bar model of the target component.

2. The adaptive steel bar modeling method based on REVIT according to claim 1, wherein The target paths of each steel bar in the target component are specifically: P output = [(p1, W(p1)), (p2, W(p2)), (p3, W(p3))…(p n , W(p n )) +; Wherein, P output represents the target path of one of the steel bars, which is formed by successively connecting nodes p1, node p2, node p3, …, node p n in the three-dimensional grid diagram. The coordinates of p n on the three-dimensional grid diagram in three-dimensional space are (x n , y n , z n ); W(p n ) represents the cumulative weight from the starting point of the steel bar to node p n .

3. The adaptive steel bar modeling method based on REVIT according to claim 2, characterized in that The cumulative weight is specifically: where 1 ≤ i ≤ n; In the formula, represents the total path weight, and W(p i ) represents the cumulative weight from the starting point of the steel bar to node p i .

4. The adaptive steel bar modeling method based on REVIT according to claim 3, characterized in that The total path weight is specifically: W(p) = α·L(p) + β·C(p) + γ·A(p); In the formula, W(p) represents the total path weight, L(p) is the total path length, C(p) is the curvature penalty term, A(p) is the fitting degree penalty term, α, β, and γ are weight coefficients, α, β, γ ∈ [0, 1], and α + β + γ = 1.

5. The adaptive steel bar modeling method based on REVIT according to claim 4, wherein In the calculation formula of the total path weight, the total path length is specifically: Wherein: where \(L(p)\) is the total path length, and \(\|p i+1 - p i \|\) represents the Euclidean distance between adjacent nodes \(p i+1 and \(p i \); The curvature penalty term is specifically: Wherein: where k i is the discrete curvature at node p i , C(p) is the curvature penalty term, and × represents the vector cross product; The fitting degree penalty term is specifically: where: d(p i ) = min q∈s ‖p i - q‖; where A(p) is the fitting penalty term, d(p i ) represents the distance from node p i to the concrete surface S, S represents the three-dimensional point set of the concrete surface of the target member, and q represents the three-dimensional point set of the concrete surface of the target member.

6. The adaptive steel bar modeling method based on REVIT according to claim 1, wherein The spline curve is specifically: where P i represents the set of nodes of the target path formed by each node, U = {u0, u1…u m}, satisfying m = n + p + 1, and u0 ≤ u1 ≤ … u m , N i , p (u) represents the i-th p-th B-spline basis function, where:

7. The adaptive steel bar modeling method based on REVIT according to claim 6, characterized in that The projection result is specifically: C proj (u) = argmin q∈S ‖C(u) - q‖, or C proj (u) = C(u) - d(u)·n(u); where \(C(u)\) represents a spline curve, \(C\) proj (u) represents the projection result of the spline curve, \(q\) represents the point closest to the spline curve, \(d(u)\) represents the signed distance from the node \(C(u)\) to the concrete surface \(S\) that is positive outside and negative inside, and \(n(u)\) represents the unit normal vector of the concrete surface \(S\) at proj \(C(u)\); In each updated spline curve, the updated nodes satisfy: In the formula, represents the updated node.

8. An adaptive steel bar modeling system based on REVIT, which is applied to the adaptive steel bar modeling method based on REVIT according to any one of claims 1-7, characterized in that It includes: The first module is used to fit the target paths of each steel bar in the target component according to the three-dimensional mesh diagram of the surface of the target component. The second module is used to parameterize each of the target paths into a spline curve and project the spline curve onto the concrete surface of the target component. The third module is used to update the changed nodes in the corresponding spline curve according to each projection result and determine each updated spline curve as the steel bar model of the target component.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method described in any one of claims 1-7.

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