A curved wood base deepening design method based on BIM and intelligent algorithm

Abstract

The present application relates to the technical field of building keel design, and particularly relates to a curved wood keel deepening design method based on BIM and intelligent algorithm. The steps are as follows: S1: constructing a mathematical modeling of segmented curved wood keel of curved building keel design; S2: parameterizing the splicing point position on each keel segment; S3: optimizing the segmented design of the segmented curved wood keel of the curved building keel design through two-stage multi-objective optimization. The present application provides a curved wood keel deepening design method based on BIM and intelligent algorithm, which constructs a mathematical model and corresponding calculation formula of segmented curved wood keel (CWK) of curved building keel design, so as to comprehensively consider the factors of factory manufacturing and on-site assembly, and through three parameterization methods of 'each keel method', 'common straight reference line method' and 'common curved reference line method', efficient work can be carried out, and through a two-stage optimization strategy, the optimization efficiency can be improved while a scheme more in line with the designer's preference is generated.

Description

Technical Field

[0001] The present invention relates to the technical field of building keel design, and in particular to a method for in-depth design of curved wooden keels based on BIM and intelligent algorithms. Background Art

[0002] Curved building keels have been widely used in recent years, particularly in large public building keels, due to their fluid visual dynamics, seamless integration with the natural environment, and ability to meet specific functional requirements (such as acoustic transmission in concert halls). The design and construction of curved building keels incorporate the most advanced concepts and technologies in contemporary building keel design, including computer-aided design, computer-aided engineering, and computer-aided manufacturing. Formwork keels are a critical component of the design and construction process, directly impacting the aesthetics and quality of the finished product. Considering both ease of fabrication and environmental considerations, curved wooden keels (CWK) are commonly used as the formwork for curved building keels.

[0003] CWK's design, manufacturing and assembly processes are as follows Figure 1 As shown. Building keel designers design the curved appearance of building keels based on functional and aesthetic requirements, while structural engineers determine the arrangement of keels based on force analysis (see Figure 1 (a)). After completing the keel layout design, the designer will carry out the specific keel design based on the model. The first step is the CWK segmentation and layout ( Figure 1 (b)). Next, the factory produces CWK segments according to the cutting diagram ( Figure 1 (c)) and finally assembled at the construction site ( Figure 1 (d)). CWK segmentation and arrangement (see Figure 1 (a) and Figure 1 (b) is fundamental to its manufacturing and assembly. Therefore, manufacturing and assembly requirements must be fully considered during design. However, research in this area is currently relatively scarce. The current design process primarily relies on manual operations by designers. Although some processes have been automated, full automation and intelligentization have yet to be achieved.

[0004] To this end, a detailed design method for curved wooden keels based on BIM and intelligent algorithms was designed to provide another technical solution to the above technical problems. Summary of the Invention

[0005] Based on this, it is necessary to provide a curved wooden keel in-depth design method based on BIM and intelligent algorithm to solve the technical problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for in-depth design of curved wooden keels based on BIM and intelligent algorithms, with the following steps:

[0008] S1: Constructing mathematical modeling of curved wooden keel segments for curved building keel design;

[0009] S2: parameterize the position of the splicing points on each keel segment;

[0010] S3: Optimize the segmented design of curved timber keels for curved building keels through a two-stage multi-objective optimization approach;

[0011] S4: numbering each segment of the optimization completed in step S3;

[0012] S5: Layout the encoded segments completed in step S4.

[0013] As a preferred embodiment of the method for in-depth design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, in step S1, mathematical modeling of curved wooden keel segments for curved building keel design is constructed as follows:

[0014] The curved wooden keel of the curved building keel design is divided into three layers, the innermost of which is the "inner layer" and the other two layers are the outer layers, and the two outer layers are symmetrically distributed on the outside of the inner layer;

[0015] Determine the joint points between adjacent layers, and the direction of each joint point should be perpendicular to the tangent line of the curve;

[0016] All tying steel ribbons that divide the curved wooden keel into sections of the curved building keel design should be arranged in the same straight line;

[0017] Design the splicing points of different segments on different straight lines.

[0018] As a preferred embodiment of the method for in-depth design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, the segments of the curved wooden keels of each curved building keel design are cut from standard wooden boards of fixed sizes.

[0019] As a preferred embodiment of the method for in-depth design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, in step S2, the position of the splicing points on each keel segment is parameterized as follows:

[0020] The geometric center line of each keel is used as a reference line, and the position of the cutting point is optimized along the reference line;

[0021] Determine the cutting point of each keel section through a common straight reference line;

[0022] The cutting points of the longer curved keels are determined by the common curved reference line.

[0023] As a preferred embodiment of the method for in-depth design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, the steps of determining the cutting points of each keel section by using a common straight reference line are as follows:

[0024] A common straight reference line is set at a position perpendicular to the horizontal plane, and the x and y coordinates of the starting point and end point of the common straight reference line are the average values ​​of the x and y coordinates of the starting point and end point of each keel center line, and the z coordinate of the common straight reference line is the minimum and maximum z values ​​of the starting point and end point of the keel center line respectively;

[0025] Optimize the position of the cutting point along the common straight line, specifically by optimizing the cutting point through the proposed two-stage multi-objective optimization algorithm;

[0026] Construct a horizontal plane at the optimized cutting point to intersect with the center line of each keel, and determine the cutting point of each keel section;

[0027] The steps for determining the cutting point of the longer curved keel through the common curved reference line are as follows:

[0028] The center line of the longest CWK is set as the common curve reference line, and the CWK segments are evenly divided.

[0029] As a preferred embodiment of the method for in-depth design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, in step S3, data of corresponding segmentation schemes and their objective function values ​​are generated through two-stage multi-objective optimization, and the steps are as follows:

[0030] The parent generation is selected through the tournament selection method, and then the offspring generation is generated using the two-point crossover operator and mutation operator;

[0031] Merge the generated offspring with the parent generation and perform non-dominated sorting and crowding distance sorting;

[0032] Individuals with higher frontier grades or smaller crowding distances are selected until the number of individuals in the new population is equal to the initial population;

[0033] The initial population of each stage is optimized through two-stage multi-objective optimization and dynamic crossover and mutation operations are adopted;

[0034] The different initial design populations obtained through each stage are used as inputs to the NSGA-II algorithm;

[0035] Obtain data corresponding to the segmentation scheme and its objective function value.

[0036] As a preferred embodiment of the method for detailed design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, the initial population of each stage is optimized through two-stage multi-objective optimization and dynamic crossover and mutation operations are adopted, and the steps are as follows:

[0037] In the first stage, the initial population is generated by replicating uniformly segmented individuals;

[0038] In the second stage, the initial population is generated by replicating the best individuals selected at the end of the first stage of evolution.

[0039] As a preferred embodiment of the method for in-depth design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, in step S4, the optimization completed in step S3 is subjected to segment numbering processing, and the steps are as follows:

[0040] Determine the geometric center of each segment;

[0041] Use K-means clustering to cluster the z coordinates of the centers;

[0042] When the z-values ​​of the segment centers are clustered, the corresponding segments will be grouped in turn;

[0043] Each group is encoded according to letters + numbers.

[0044] As a preferred embodiment of the method for in-depth design of curved wooden keels based on BIM and intelligent algorithms provided by the present invention, in step S5, the encoded segments completed in step S4 are nested as follows:

[0045] In step S5, the encoded segments completed in step S4 are arranged in the following steps:

[0046] Get the original center position (x) of each CWK segment i ,y i ,z i ) and its converted corresponding bit

[0047] ″″″

[0048] Set (x i ,y i ,z i ), get the position conversion parameter (x i -x i ,y i -y i ,z i -z i ), where i represents the number of CWK;

[0049] ″′

[0050] According to the corresponding conversion parameters (x i -x i ,y i -y i ,z i -z i ) Convert the original position of the segmented encoding.

[0051] It can be seen without a doubt that the above-mentioned technical solution of this application can definitely solve the technical problem to be solved by this application.

[0052] At the same time, through the above technical solutions, the present invention has at least the following beneficial effects:

[0053] 1. The present invention provides a method for in-depth design of curved wooden keels based on BIM and intelligent algorithms. By constructing a mathematical model of CWK segmentation and corresponding calculation formulas, it can comprehensively consider factory manufacturing and on-site assembly factors. At the same time, it uses three parameterization methods: "each keel method", "common straight reference line method" and "common curved reference line method" to work efficiently. It can also use a two-stage optimization strategy to improve optimization efficiency while generating a solution that better suits the designer's preferences.

[0054] 2. The present invention automatically and efficiently groups and nests the coded segments on the standard board through parallel grouping and nesting procedures to ensure accurate positioning of the CWK segment coding.

[0055] 3. The present invention realizes the automated manufacturing design of CWK based on BIM technology and intelligent algorithms.

[0056] 4. The present invention uses Grasshopper software as the BIM platform to visualize the entire design process, which is convenient for designers to use interactively.

[0057] 5. The present invention can quickly generate multiple solutions that meet the designer's preferences through a two-stage optimization method based on the intelligent algorithm based on NSGA-II, thereby accelerating the design process.

[0058] 6. The present invention inputs the design into a CWK model and outputs the design into a CWK segment with coding after blanking and layout, which can be directly used for processing, manufacturing and subsequent assembly. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 Schematic diagram of the design process of curved wooden keel (CWK) designed for existing curved building keels;

[0061] Figure 2 Schematic diagram of the method framework of the present invention;

[0062] Figure 3 It is a schematic diagram of the assembly and manufacturing details of the present invention;

[0063] Figure 4 Schematic diagram of different parameterization methods of the present invention;

[0064] Figure 5 Schematic diagram of the workflow of the two-stage optimization process of the present invention;

[0065] Figure 6 It is a schematic diagram of the coding and arrangement process of the present invention;

[0066] Figure 7 This is a schematic diagram of the automated segmented coding of the present invention;

[0067] Figure 8 Schematic diagram of the preliminary blanking and arranging results of the present invention;

[0068] Figure 9 Schematic diagram of the initial situation of CWK in the case of the present invention;

[0069] Figure 10 This is a schematic diagram of the preliminary arrangement results of the optimization algorithm in the case of the present invention;

[0070] Figure 11 Schematic diagram of the sample arrangement results in the case of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0073] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions therein may be combined with each other.

[0074] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0075] Reference Figure 2-Figure 8 , a detailed design method for curved wooden keels based on BIM and intelligent algorithms.

[0076] Using Grasshopper as the BIM design platform, refer to Figure 2 , Figure 2 The five modules of the framework are presented in [1]: (1) mathematical modeling of CWK segments, (2) parameterization, (3) two-stage multi-objective optimization, (4) automated cluster encoding, and (5) parallel automatic nesting.

[0077] (1) Mathematical modeling of CWK segmentation

[0078] 1.1. Design conditions

[0079] In order to realize the manufacture and assembly of CWK, it must be divided into wooden sections, such as Figure 3 As shown in the figure, during construction, a CWK typically consists of three layers. The innermost layer is called the "inner layer," while the two outer layers are called the "outer layers," and they are symmetrically distributed. CWK segmentation must meet various constraints, which can be mainly divided into two categories: on-site assembly constraints and factory manufacturing constraints.

[0080] A. On-site assembly constraints

[0081] First, ensuring that each segment can be assembled into a complete CWK structure is a fundamental requirement. Second, to facilitate manufacturing and assembly, the two "outer" segments must be symmetrical in position and geometry. Furthermore, to ensure keel stability, the joints between adjacent layers cannot be too close or too far apart. Finally, to facilitate on-site assembly and ensure proper transfer of structural loads, each joint must be perpendicular to the tangent line at the curve.

[0082] B. Factory manufacturing constraints:

[0083] CWK segments are processed in the factory using CNC cutting machines. All CWK segments are cut from standard wooden boards of fixed dimensions, such as Figure 3 As shown in Figure 2. Since the CWK segments and standard wood panels have the same thickness, only the planar segments need to be considered. Therefore, the factory manufacturing constraint is that the segments must be able to be cut from standard panels.

[0084] 1.2. Design Goals

[0085] While considering the structural reliability and assembly process convenience after on-site assembly, it is also necessary to consider material savings in factory manufacturing. Therefore, the optimization design objectives of the CWK segment should cover three aspects: structural safety, on-site assembly convenience, and factory manufacturing efficiency.

[0086] A) Structural safety goals

[0087] To ensure that the structure can bear load evenly after installation, the CWK segments should not be too short. Too short segments will lead to excessive concentration of splicing points, thus causing significant stress changes.

[0088] B) On-site assembly target

[0089] To facilitate the operation of on-site workers, all tying steel wires should be arranged in the same straight line. This alignment method can simplify the scaffolding layout and make the assembly process simpler and more efficient. To ensure good load-bearing alignment of the tying steel wires, the splicing points of different segments need to be designed on different straight lines, such as Figure 3 shown.

[0090] Fewer straight lines for splice points require less lashing and scaffolding, which reduces labor requirements, another on-site assembly goal.

[0091] C) Factory manufacturing goals

[0092] To improve the efficiency of the material nesting tool, you should avoid long segments, as they will reduce the efficiency of cutting and nesting. Therefore, it is necessary to minimize the segment length.

[0093] (2) Parameterization

[0094] The variable used to optimize the CWK segment design is the location of the splicing point. If the location of the splicing point on each keel is parameterized, such as Figure 4 As shown in (a), the geometric centerline of each keel is used as a reference line, and the location of the cutting point can be optimized along this reference line. However, due to the large number of design constraints and multiple objective constraints, this parameterized method may make it difficult for the algorithm to quickly converge to the optimal or near-optimal solution.

[0095] like Figure 4 As shown in (b), this common reference line is perpendicular to the horizontal plane. The x and y coordinates of its starting and ending points are the averages of the x and y coordinates of the starting and ending points of each keel centerline, respectively. The z coordinates are the minimum and maximum z values ​​of the starting and ending points of the keel centerlines, respectively. The positions of the cutting points along this common line are then optimized, and horizontal planes are constructed at these cutting points to intersect with the centerlines of each keel, thereby determining the cutting points for each keel segment. This parameterized approach reduces the number of parameters while ensuring that the splicing points are aligned along the common line (horizontal plane).

[0096] However, since the curved keel may have a long horizontal distance, using the above common straight reference line may cause some CWK segments to be too long because the probability of these segments being split is low, such as Figure 4 (b) shows the CWK segmentation between planes A and B. Therefore, by using the common reference line method, as shown in Figure 4(c) The common curve reference line is the center line of the longest CWK. When the center line of the longest CWK is used as the reference line, the probability of all CWK segments being segmented is more uniform. Therefore, the variable of the CWK segment is the position of the cutting point on the center axis of the longest keel, that is, {joint1, joint2, ..., joint i ,}.

[0097] (3) Two-stage multi-objective optimization

[0098] 3.1 Fitness Function

[0099] Equation (1) represents the first objective function, which aims to minimize the CWK segmentation The maximum length of the segment to avoid being too long. is the maximum length threshold set by the designer, and C1 is a constant used to balance the objective functions of different orders of magnitude. Similarly, Equation (2) represents the second objective function, which aims to maximize the minimum length of the CWK segment. To ensure that all optimization objectives are minimized, the inverse of the second objective function is taken, where C2 is a constant similar to C1 and is the minimum length threshold set by the designer.

[0100] In order to facilitate construction, equation (3) represents the minimization of the number of straight lines where the CWK splicing points are located, the number of segments and, equation (4) represents the minimization of the type of segment lengths, Indicates the location of the splicing point, Indicates the length of the segment, where C3 and C4 are constants similar to C1.

[0101] In order to make the CWK segment length more concentrated near the average length and reduce the number of segments that are too short or too long, a fifth optimization objective is proposed, as shown in equations (5) and (6), where C5 is a constant similar to C1.

[0102]

[0103] In the above formula, represents the length of the i-th CWK segment, Indicates the maximum value of the CWK segment length. Indicates the minimum length of the CWK segment to be set, {} indicates a set, and num() indicates the type of value in the calculated set. Indicates the height of the horizontal line where the splicing point is located, N indicates the number of CWK segments, μ seg is the mean length of the CWK segment. C1, C2, C3, C4, and C5 are different constants used to balance objective functions of different orders of magnitude.

[0104] 3.2 Two-stage optimization process

[0105] Figure 5 (NSGA-II Algorithm Section) shows the process of the NSGA-II algorithm. First, the parent generation is selected through the tournament selection method, and then the offspring generation is generated using the two-point crossover operator and the mutation operator. Next, the generated offspring are merged with the parent generation and subjected to non-dominated sorting (i.e., sorting the individuals in the population according to dominance and assigning them to different non-dominated fronts) and crowding distance sorting (i.e., calculating the crowding distance of individuals in each non-dominated front). Individuals with higher frontier ranks or smaller crowding distances are selected until the number of individuals in the new population equals the number of individuals in the initial population.

[0106] In order to generate a large number of design solutions close to the designer's preferences and quickly obtain the optimal solution, a two-stage evolutionary optimization method ( Figure 5 ), aims to optimize the initial population at each stage and adopt dynamic crossover and mutation operations. In the first stage, the initial population is generated by copying uniform segmented individuals. Uniform segmented individuals refer to the variable parameters obtained by dividing the selected keel into segments of equal length (i.e., ). In the second stage, the best individuals selected from the end of the first stage of evolution are replicated. Generate the initial population.

[0107] Figure 5 This paper presents a two-stage multi-objective optimization process. Different initial design populations (i.e., different CWK segmentation schemes) obtained through the above stages are used as input to the NSGA-II algorithm. Subsequently, the corresponding objective function values ​​for these design variables are calculated in GH and input into the NSGA-II algorithm. Ultimately, data on the corresponding segmentation schemes and their objective function values ​​are obtained.

[0108] The multi-objective optimization problem of CWK segmentation can be expressed by equation (7), where F1(x), F2(x), F3(x), F4(x), and F5(x) are defined by equations (1) to (6), respectively, and x represents the design variable.

[0109] Min F(X)={F1(x),F2(x),F3(x),F4(x),F5(x)} (7)

[0110] To better balance global and local search, the second phase uses smaller crossover and mutation probabilities than the first phase, while increasing the crossover and mutation distribution indices. These two parameters are crucial in the NSGA-II algorithm and help improve search sophistication.

[0111] In addition, the stopping criterion for each stage is to reach the preset maximum number of optimization generations, and the crossover probability and mutation probability are both set to fixed values.

[0112] (4) Automated cluster coding

[0113] After completing the CWK segment design optimization, each segment needs to be numbered for subsequent processing and installation. Figure 6 Demonstrates the workflow of encoding and nesting.

[0114] During the CWK construction process, due to the large number of similar sections, the sections need to be numbered to distinguish them. The sections will be installed sequentially from bottom to top, so it is necessary to first identify the different section groups and assign them numbers.

[0115] Since the construction sequence is bottom-up, segments at similar heights are grouped for installation. First, the geometric center of each segment is determined, and then the z-coordinates of these centers are clustered using K-means clustering. K-means clustering is a commonly used unsupervised learning algorithm that iteratively divides data into K clusters to minimize the sum of the distances from each data point to its cluster center. Once the z-values ​​of the segment centers are clustered, the corresponding segments are grouped accordingly. Figure 7 (a) shows the cluster centers and results of the z-value, with different colors representing different cluster centers and groups.

[0116] Next, encode each group separately, such as Figure 7 (b), where letters represent groups and numbers represent the installation order within each group.

[0117] (5) Parallel automatic nesting

[0118] After segmentation and coding, coded segments are obtained. Directly entering all segments into the nesting tool at this point will generate a single nesting result. However, this method will place segments of varying heights on the same plate, requiring construction to begin only after all segments are produced, which can hinder progress. Furthermore, the large number of CWK segments and their relatively small size relative to standard plates complicate the nesting process. Single-threaded nesting calculations fail to utilize the computer's multithreading capabilities, resulting in slower nesting performance.

[0119] Therefore, a parallel nesting scheme for grouped CWK segments is proposed, such as Figure 7 As shown, the design results are as follows Figure 6 As shown in the figure, CWK segments are entered in groups, and the standard plate is replicated according to the number of CWK segments. The data is then input into the OpenNest plugin in Grasshopper.

[0120] In addition, the original center position (x i ,y i ,z i ) and its corresponding position after transformation (xi ′,y i ′,z i ′), we can get the position conversion parameter (x i ′-x i ,y i ′-y i ,z i ′-z i ), where i represents the CWK number. Then, according to the corresponding conversion parameter (x i ′-x i ,y i ′-y i ,z i ′-z i ) Convert the original position of the segmented encoding.

[0121] Since the CWK consists of three layers, and the outer layers are symmetrical, a single layer of CWK can be obtained by duplicating another layer. To distinguish the three layers of keels, the prefix "I-" for the inner layer keel and "O-" for the outer layer keel are added to the CWK segment code to facilitate renaming.

[0122] To optimize material utilization, the last board of each group ( Figure 8 The combined sheets are prioritized for fabrication and then allocated to their respective groups.

[0123] Specific implementation case reference Figures 9-11 .

[0124] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. The preferred embodiments do not describe all details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for in-depth design of curved wooden keels based on BIM and intelligent algorithms, characterized in that: Here are the steps: S1: Constructing mathematical modeling of curved wooden keel segments for curved building keel design; S2: parameterize the position of the splicing points on each keel segment; S3: Optimize the segmented design of curved timber keels for curved building keels through a two-stage multi-objective optimization approach; S4: numbering each segment of the optimization completed in step S3; S5: Layout the encoded segments completed in step S4; In the S3 step, data corresponding to the segmentation scheme and its objective function value are generated through a two-stage multi-objective optimization, and the steps are as follows: The parent generation is selected through the tournament selection method, and then the offspring generation is generated using the two-point crossover operator and mutation operator; Merge the generated offspring with the parent generation and perform non-dominated sorting and crowding distance sorting; Individuals with higher frontier grades or smaller crowding distances are selected until the number of individuals in the new population is equal to the initial population; The initial population of each stage is optimized through two-stage multi-objective optimization and dynamic crossover and mutation operations are adopted; The different initial design populations obtained through each stage are used as inputs to the NSGA-II algorithm; Obtain data corresponding to the segmentation scheme and its objective function value; The initial population of each stage is optimized through two-stage multi-objective optimization and dynamic crossover and mutation operations are adopted. The steps are as follows: In the first stage, the initial population is generated by replicating uniformly segmented individuals; In the second stage, the initial population is generated by replicating the best individuals selected at the end of the first stage of evolution; In step S4, the optimization completed in step S3 is performed by numbering each segment, and the steps are as follows: Determine the geometric center of each segment; Use K-means clustering to cluster the z coordinates of the centers; When the z-values ​​of the segment centers are clustered, the corresponding segments will be grouped in turn; Encode each group according to letters + numbers; In step S5, the encoded segments completed in step S4 are arranged in the following steps: Get the original center position of each CWK segment and its converted corresponding position , get the position conversion parameters , where i represents the number of CWK; According to the corresponding conversion parameters Convert the original position of the segment code.

2. The method for in-depth design of curved wooden keels based on BIM and intelligent algorithms according to claim 1, characterized in that: In step S1, mathematical modeling of the curved wooden keel segment of the curved building keel design is constructed, and the steps are as follows: The curved wooden keel of the curved building keel design is divided into three layers, the innermost layer is the inner layer, and the other two layers are outer layers, and the two outer layers are symmetrically distributed on the outside of the inner layer; Determine the joint points between adjacent layers, and the direction of each joint point should be perpendicular to the tangent line of the curve; All tying steel ribbons that divide the curved wooden keel into sections of the curved building keel design should be arranged in the same straight line; Design the splicing points of different segments on different straight lines.

3. The method for in-depth design of curved wooden keels based on BIM and intelligent algorithms according to claim 2 is characterized in that: The curved timber keel segments of each curved building keel design are cut from standard wooden boards of fixed dimensions.

4. The method for in-depth design of curved wooden keels based on BIM and intelligent algorithms according to claim 1, characterized in that: In step S2, the position of the splicing points on each keel segment is parameterized as follows: The geometric center line of each keel is used as a reference line, and the position of the cutting point is optimized along the reference line; Determine the cutting point of each keel section through a common straight reference line; The cutting points of the longer curved keels are determined by the common curved reference line.

5. The method for in-depth design of curved wooden keels based on BIM and intelligent algorithms according to claim 4 is characterized in that: The steps for determining the cutting point of each keel section by using a common straight reference line are as follows: A common straight reference line is set at a position perpendicular to the horizontal plane, and the x and y coordinates of the starting point and end point of the common straight reference line are the average values ​​of the x and y coordinates of the starting point and end point of each keel center line, and the z coordinate of the common straight reference line is the minimum and maximum z values ​​of the starting point and end point of the keel center line respectively; Optimizing the positions of the cutting points along the common straight reference line; Construct a horizontal plane at the optimized cutting point to intersect with the center line of each keel, and determine the cutting point of each keel section; The steps for determining the cutting point of the longer curved keel through the common curved reference line are as follows: The center line of the longest CWK is set as the common curve reference line, and the CWK segments are evenly divided.

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