A support and hanger design method based on BIM

Abstract

The present application discloses a support and hanger design method based on BIM, which relates to the field of support and hanger design, including: obtaining geometric parameters and attribute parameters of pipelines, constructing an initial pipeline model; solving the optimal pipeline layout plan using an ant colony algorithm, and generating a pipeline BIM model; establishing a support and hanger parametric model library through a parametric family file, associating and matching the pipeline parameters in the pipeline BIM model with the parameters in the support and hanger parametric model library, generating a support and hanger component family, and adding the generated support and hanger component family to the pipeline BIM model to obtain a support and hanger BIM model; parametrically optimizing the support and hanger components through a topology optimization algorithm to obtain the optimal design parameters of the support and hanger, and using the optimal design parameters of the support and hanger to update the parameters of the support and hanger component family in the support and hanger BIM model; generating support and hanger drawings using BIM software based on the updated support and hanger BIM model. In view of the unreasonable pipeline layout in the support and hanger design in the prior art, the present application reduces redundant design.

Description

Technical Field

[0001] The present application relates to the field of support and hanger design, and in particular to a support and hanger design method based on BIM. Background Art

[0002] Traditional support and hanger design mainly relies on the experience of designers, and is manually arranged and checked through a two-dimensional CAD platform. This design model is inefficient and it is difficult to fully consider the rationality of pipeline layout, which often leads to redundant pipeline layout, a large number of unnecessary collisions and intersections, and serious waste of pipe materials. According to statistics, the cost of building mechanical and electrical engineering materials accounts for as much as 20% to 30% of the total project cost, among which the problem of material waste caused by improper comprehensive pipeline design is particularly prominent. Therefore, it is urgent to innovate the design method of supports and hangers to maximize the savings in engineering materials while improving design quality and efficiency.

[0003] The emergence of BIM technology provides a new idea for solving the above problems. BIM uses digital and parametric building information models as carriers to provide a three-dimensional visualization collaborative platform for pipeline integrated design. Technologies such as pipeline collision inspection and pipeline optimization layout based on BIM have made certain progress, but the application research in the field of support and hanger design is still relatively lacking.

[0004] The existing methods of using BIM technology for support and hanger design mainly focus on the creation of parametric support and hanger component families and the regular generation of support and hanger layout. Although the efficiency of support and hanger design has been improved to a certain extent, there is a lack of effective solutions to the problem of redundant support and hanger design caused by unreasonable comprehensive layout of pipelines. The key to this problem is that the existing research mainly focuses on the single professional design of pipelines or supports and hangers, and lacks the overall optimization of pipeline layout and support and hanger design. Unreasonable pipeline layout will inevitably lead to redundant layout of supporting supports and hangers. Summary of the invention

[0005] In response to the problem of wire waste caused by unreasonable pipeline layout in support and hanger design in the prior art, the present application provides a support and hanger design method based on BIM, which optimizes the pipeline layout using an ant colony algorithm and optimizes the design of support and hanger components using a topology optimization method, thereby improving the design quality and reducing redundant design.

[0006] Technical solution, the purpose of this application is achieved through the following technical solution.

[0007] The present application provides a support and hanger design method based on BIM, comprising: S1, obtaining geometric parameters and attribute parameters of pipelines from construction drawings of building structures and mechanical and electrical pipeline layout as input, and using BIM software to construct an initial pipeline model; S2, according to the initial pipeline model, taking the geometric parameters, attribute parameters, optimization objectives and constraints of the pipeline as input, using an ant colony algorithm to solve the optimal pipeline layout plan, using the optimal pipeline layout plan to update the parameters in the initial pipeline model, and generating a pipeline BIM model; wherein the optimization objectives include the shortest total length of the pipeline and the least collision between the pipeline and the building structure; the constraints include the minimum spacing between pipelines and the minimum spacing between the pipeline and the building structure; S3, according to the pipeline parameters in the pipeline BIM model, establishing a support and hanger parametric model library through a parametric family file, associating and matching the pipeline parameters in the pipeline BIM model with the parameters in the support and hanger parametric model library, generating a support and hanger component family, and adding the generated support and hanger component family to the pipeline BIM model to obtain a support and hanger BIM model.

[0008] Specifically, in BIM software, a support and hanger parametric family file is established based on the general design parameters of the support and hanger, including the material, cross-sectional size, length, connection method, etc. of the support and hanger; in the parametric family file, the geometric shape and size parameters of the support and hanger components are defined, and the geometric parameters are associated with the design parameters of the support and hanger to build a support and hanger parametric model library. The geometric parameters and attribute parameters of the pipeline in the pipeline BIM model are extracted and used as input to search for support and hanger components that match the pipeline parameters in the support and hanger parametric model library, and automatically generate a support and hanger component family that matches the pipeline layout. In the BIM software, the generated support and hanger component family is inserted into the pipeline BIM model, the pipeline endpoints are automatically captured, and the support and hanger components are connected to the pipeline to form a support and hanger BIM model.

[0009] S4, import the support and hanger BIM model into the structural finite element analysis software to perform structural force analysis, and use the force analysis results as input to parametrically optimize the support and hanger components through the topology optimization algorithm to obtain the optimal design parameters of the support and hanger, and use the optimal design parameters of the support and hanger to update the parameters of the support and hanger component family in the support and hanger BIM model; S5, generate the support and hanger drawings using the BIM software based on the updated support and hanger BIM model.

[0010] Further, S1, constructing an initial pipeline model, including: inputting pipeline geometric parameters extracted from building structure and electromechanical pipeline layout construction drawings, including pipeline starting point coordinates, end point coordinates, pipe diameter, pipeline length, etc., and pipeline attribute parameters, including pipeline material, design pressure, design temperature, medium type, etc., into the BIM software; in the BIM software, based on the input pipeline geometric parameters, using pipeline modeling commands to generate a three-dimensional solid pipeline, through stretching, sweeping, lofting and other three-dimensional modeling operations, with the pipeline starting point and end point coordinates as a reference, and the pipe diameter as the cross-sectional size, a three-dimensional solid pipeline equal to the pipeline length is generated.

[0011] In the BIM software, the input pipeline attribute parameters are associated with the generated three-dimensional solid pipeline. Through the attribute assignment function of the BIM software, the pipeline material, design pressure, design temperature, medium type and other attribute parameters are assigned to the corresponding three-dimensional solid pipeline to build an initial pipeline BIM model containing pipeline geometry and attribute information; among them, the medium type is one of the pipeline attribute parameters, which refers to the type of fluid or material transported inside the pipeline. The choice of medium type will affect other attribute parameters such as the material, design pressure, and design temperature of the pipeline, and further affect the layout of the pipeline and the setting of the support bracket. Therefore, when constructing the initial pipeline BIM model, it is necessary to assign the medium type as an important attribute parameter to the corresponding three-dimensional solid pipeline. Specifically, in building mechanical and electrical engineering, common media types may include: Water: including cold water, hot water, chilled water, reclaimed water, etc., used for life, production, air conditioning systems, etc. Steam: used for heating, thermal systems, process heating, etc. Gas: including natural gas, liquefied petroleum gas, coal gas, etc., used for combustion equipment, kitchens, hot water systems, etc. Compressed air: used in industrial production, instrument control, equipment power, etc. Vacuum: used in special environments such as industrial production, medical equipment, and laboratories. Chemicals: including chemical solutions such as acids, alkalis, and salts, used in industrial production, water treatment, etc. Oil products: including diesel, gasoline, lubricating oil, etc., used in generators, boilers, mechanical equipment, etc. Special gases: including oxygen, nitrogen, argon, carbon dioxide, etc., used in special occasions such as medical treatment and industrial production. Sewage: including domestic sewage, industrial wastewater, etc., which needs to be transported to sewage treatment facilities through pipelines. Fire water: used in fire protection systems of buildings, including fire hydrants, automatic sprinklers, etc. In the BIM software, the constructed initial pipeline BIM model is visualized, and the spatial position, connection relationship, etc. of the pipeline are intuitively checked and verified through three-dimensional and two-dimensional views, and the initial pipeline BIM model is manually adjusted and modified as needed.

[0012] Further, S2, using the ant colony algorithm to solve the optimal pipeline layout solution and generate a pipeline BIM model, including: S21, extracting the pipeline geometric parameters in the initial pipeline BIM model constructed in step S1, including the starting point coordinates (xstart ,y start ,z start )、end point coordinates (x end ,y end ,z end ), pipe diameter d, pipeline length l, etc., convert the pipeline geometric parameters into spatial search dimensions and distance calculation parameters in the ant colony algorithm, and input them into the data structure of the ant colony algorithm; extract the pipeline attribute parameters in the initial pipeline BIM model, including the material m, design pressure p, design temperature t, medium type f, etc. of the pipeline, convert the pipeline attribute parameters into pipeline layout constraint condition parameters in the ant colony algorithm, and input them into the data structure of the ant colony algorithm; take the shortest total pipeline length and the least collision between the pipeline and the building structure as optimization goals; take the minimum spacing between pipelines and the minimum spacing between pipelines and the building structure as constraints; S22, in the ant colony algorithm, ants search for the optimal path in the solution space, continuously update pheromones, iteratively find the pipeline layout plan that meets the constraints and optimization goals, obtain the optimal solution for the pipeline layout, and output the optimal pipeline layout plan.

[0013] S23, according to the optimal pipeline layout plan, adjust the spatial position and connection relationship in the initial pipeline BIM model through the BIM software to generate an optimized pipeline BIM model; specifically, read the optimal pipeline layout plan output in step S22, and extract the pipeline space coordinate sequence therein; call the corresponding API interface in the BIM software, and perform parameterized modification on the initial pipeline BIM model according to the pipeline space coordinate sequence; for each pipeline section in the pipeline layout plan, update its geometric parameters such as the starting point coordinates, end point coordinates, and spatial direction in the BIM model; At the connection point of two adjacent pipeline sections, check whether the connection method meets the pipeline layout specifications, and add flanges, elbows and other pipe fittings to achieve the connection when necessary; insert the support and hanger family in the BIM model at each support point of the pipeline layout plan, and associate the support and hanger with the pipeline and building structure; according to the load conditions of the pipeline and the design parameters of the support and hanger, parametrically adjust the support and hanger family instances in the BIM model to meet the strength and stiffness requirements; after completing the geometric parameter update and associative connection of all pipelines and supports and hangers in the pipeline BIM model, output the optimized pipeline BIM model.

[0014] S24, perform a collision check on the optimized pipeline BIM model; check whether there is collision interference between pipelines and between pipelines and building structures; input the collision check results into the ant colony algorithm as a new constraint condition, repeat steps S22 to S24 until the pipeline BIM model no longer collides, and output the final optimized pipeline BIM model. Among them, collision interference refers to the conflict or overlap in spatial position between pipelines or between pipelines and building structures during the pipeline layout process. Collision interference will make the pipeline layout plan infeasible and require optimization and adjustment. In the BIM software, the collision check function can be used to automatically detect collision interference in the pipeline BIM model, and the inspection results can be used as one of the constraints of the ant colony algorithm to avoid collision problems in the optimized pipeline layout plan.

[0015] Further, S21, setting optimization objectives and constraints, including: extracting geometric parameters and attribute parameters of the pipeline from the initial pipeline BIM model; converting the extracted geometric parameters into spatial search dimensions and distance calculation parameters in the ant colony algorithm; converting the extracted attribute parameters into pipeline layout constraint condition parameters in the ant colony algorithm; constructing the pipeline layout optimization objective function as follows: minF = ω1 × ∑l i +ω2×∑c j +ω3×∑n k , where Σl i represents the total length of the pipeline, Σc j represents the total collision point penalty value between pipeline and building structure, ∑n k represents the number of supports and hangers in the pipeline layout plan; ω1, ω2, ω3 represent the weight coefficients of pipeline length, collision point, and number of supports and hangers respectively; according to the design specifications of pipeline layout, the minimum spacing threshold d between pipelines is set minp , the minimum spacing threshold d between pipelines and building structures mins , Maximum spacing of pipeline supports and hangers s maxi , Maximum length of pipeline cantilever f maxi and the allowable value of pipeline stress [σ], construct the ant colony algorithm constraint conditions:

[0016] Constraints:

[0017]

[0018] Among them, d ij represents the minimum distance between pipeline i and pipeline j, d ik represents the minimum distance between pipeline i and building structure k, s i is the support and hanger spacing of pipeline i, f i is the cantilever length of pipeline i, σ i is the stress of pipeline i, N pis the pipeline set, N s A collection of building structures.

[0019] Among them, the spatial search dimension refers to the dimension of the solution space searched by the ant colony algorithm during the pipeline layout optimization process. When arranging pipelines in three-dimensional space, the position of the pipeline can be represented by three coordinates (x, y, z). Therefore, the spatial search dimension of the ant colony algorithm is usually 3-dimensional. In some cases, if the pipeline layout problem can be simplified to a two-dimensional plane problem, the spatial search dimension can be reduced to 2 dimensions. The choice of spatial search dimension will affect the computational complexity and convergence speed of the ant colony algorithm. The spatial search dimension refers to the dimension of the solution space searched by the ant colony algorithm during the pipeline layout optimization process. When arranging pipelines in three-dimensional space, the position of the pipeline can be represented by three coordinates (x, y, z). Therefore, the spatial search dimension of the ant colony algorithm is usually 3-dimensional. In some cases, if the pipeline layout problem can be simplified to a two-dimensional plane problem, the spatial search dimension can be reduced to 2 dimensions. The choice of spatial search dimension will affect the computational complexity and convergence speed of the ant colony algorithm.

[0020] Further, S22, in the ant colony algorithm, the ants search for the optimal path in the solution space and output the optimal pipeline layout solution, including: S221, initializing the ant colony algorithm parameters, the parameters including the number of ants m, the maximum number of iterations N_max, the pheromone importance factor α, the heuristic function importance factor β, the pheromone volatility coefficient ρ and the global pheromone update intensity Q; S222, according to the objective function, constructing the fitness function of the ant colony algorithm; S223, in the pipeline starting point coordinates (x start ,y start ,z start ), end point coordinates end In the pipeline layout solution space composed of geometric parameters such as pipe diameter d, m feasible solutions that meet the constraints are randomly generated as the initial positions of the ants. Each feasible solution consists of a spatial coordinate sequence of n pipeline segments {(x1, y1, z1), ..., (x n ,y n ,z n )} represents; S224, according to the state transition probability function, calculate the next moving position of each ant to obtain m new pipeline layout solutions; and calculate the objective function value of each new pipeline layout solution according to the fitness function, and verify whether the new pipeline layout solution satisfies the constraints; at the same time, calculate the objective function value of each pipeline layout solution according to the fitness function, and verify whether it satisfies the constraints, and obtain the fitness value and constraint satisfaction.

[0021] S225, for the path (i, j) passed by the kth ant, according to the local pheromone update formula Update the pheromone concentration on the corresponding path (i, j); where, represents the local pheromone increment, L k represents the length of the path traveled by the kth ant in this iteration; S226, according to the global pheromone update formula τ ij (t+1)=(1-ρ)×τ ij (t)+ρ×ΔτΔτ ij (t), updates the pheromone concentration on the path (i, j) in the entire solution space; where, ΔτΔτ ij (t) represents the global pheromone increment, L* represents the shortest path length of all ants in this iteration; S227, repeat steps S224 to S226, iteratively search for the optimal pipeline layout solution {(x1',y1',z1'),......,(x1') with the highest fitness function value and satisfying the constraints. n ',y n ',z n ')} The output is the optimal pipeline layout solution.

[0022] Furthermore, the fitness function expression is as follows:

[0023]

[0024] Where F_k(t) represents the pipeline layout solution of the kth ant at the tth iteration. The fitness function value of represents the length of the i-th pipeline in the layout solution; L0 represents the reference value of the total length of the pipeline; represents the penalty value of the jth collision point in the layout solution; C0 represents the reference value of the collision point penalty value; Indicates the number of supports and hangers in the layout solution; N0 indicates the reference value of the number of supports and hangers; represents the minimum distance between pipeline i and building structure k in the layout solution; D0 represents the reference value of the minimum spacing between pipelines and building structures; represents the maximum stress value of pipeline i in the layout solution; σ0 represents the reference value of pipeline stress; w1, w2, w3, w4, w5 represent the weight coefficients of pipeline length, collision point, number of supports and hangers, minimum spacing and stress factor respectively. By introducing dimensionless reference values, the indicators of fitness function are in the same order of magnitude, and the values ​​of weight coefficients are more comparable. At the same time, the addition of minimum spacing factor and stress factor makes the evaluation of pipeline layout scheme more comprehensive and reasonable.

[0025] Furthermore, the expression of the state transition probability function is as follows:

[0026]

[0027] in, represents the probability that the kth ant moves from path i to path j in the tth iteration; τ ij (t) represents the pheromone concentration on path (i, j); η ij Represents the heuristic function of path (i, j), and the pipeline length l ij and the number of collision points n ij Related; ij Represents the turning factor of path (i, j), which is related to the turning angle θ ij Related to the standard rotation angle θ0; δ ij Represents the height change rate factor of path (i, j), and the height change Δh ij It is related to the standard height difference Δh0 of the pipeline; α, β, μ, ν represent the importance coefficients of pheromone, heuristic function, turning factor and height change rate factor respectively; allow k represents the set of optional branch paths at the current location of ant k; γ ij and δ ij The calculation formula is: Among them, ε represents a small positive number to prevent the denominator from being zero. By introducing the angle factor and the height change rate factor, when choosing the next moving position, the ants will give priority to paths with small angles and gentle height changes, thereby obtaining a pipeline solution with a more reasonable layout and more convenient construction. At the same time, by adjusting the importance coefficients μ and ν, the influence of the local layout characteristics of the pipeline on the ant colony search can be weighed. τ iu (t) represents the pheromone concentration between node i and the allowed access node u in the tth iteration. η iu Represents the heuristic information between node i and the allowed access to node u, usually related to the distance between nodes or the inverse of the cost. ij Indicates the turning factor from node i to the allowed access node u, which is used to measure the turning angle of the pipeline layout between nodes i and u. The larger the turning factor, the smaller the turning angle of the path, and the smoother the pipeline layout. ij It represents the height change rate factor from node i to the accessible node u, which is used to measure the height change of the pipeline layout between nodes i and u. The larger the height change rate factor, the smaller the height change of the path, and the smoother the pipeline layout.

[0028] Furthermore, S24, performing a collision check on the optimized pipeline BIM model, including: S241, extracting the pipeline family, support and hanger family and building structure family in the optimized pipeline BIM model through the collision check function in the BIM software to form a collision check set, and using a collision detection algorithm based on the axis-aligned bounding box AABB to perform geometric collision calculations on any two elements in the collision check set. If a geometric collision exists, outputting a collision report containing the three-dimensional spatial coordinates of the collision point and the type of collision element; S242, performing data screening and classification on the collision report, extracting the collision element type information of each collision point, and determining whether the collision type belongs to a collision between pipelines or between pipelines and building structures; specifically, subdividing the collision type between pipelines into a collision between pipeline centerlines and a collision between pipeline outer contour surfaces. S243, if there is a collision between pipelines or between pipelines and building structures, the three-dimensional spatial coordinate position of the corresponding collision point and the collision type information are input into the ant colony algorithm as a new constraint condition, and the new constraint condition is added on the basis of the original pipeline layout constraint condition, and the constraint condition parameters in the ant colony algorithm are updated; S244, the updated pipeline geometric parameters, attribute parameters, optimization objective function and constraint conditions are used as input, and the process returns to step S22, and the ant colony algorithm is re-executed using the updated model parameters and constraint conditions to obtain a new optimal solution for the pipeline layout; the optimal solution is mapped to the corresponding pipeline family instance in the BIM model using the parametric family API based on the secondary development of Revit, and a pipeline BIM model is generated; S245, steps S22 to S24 are repeatedly executed until no collision occurs between pipelines and between pipelines and building structures in the collision check, and the corresponding pipeline BIM model is output as the final optimized pipeline BIM model.

[0029] Among them, the pipeline family refers to the parametric three-dimensional model used to represent the pipeline in the BIM software. The pipeline family usually contains the geometric parameters of the pipeline (such as the starting point coordinates, the end point coordinates, the pipe diameter, etc.) and the attribute parameters (such as the material, the design pressure, the design temperature, etc.), and the pipeline model can be flexibly adjusted and modified through parametric control. In BIM software such as Revit, the pipeline family can be created, stored and called in the form of a family file (.rfa). The support and hanger family refers to the parametric three-dimensional model used to represent the pipeline support and hanger in the BIM software. The support and hanger family usually contains the geometric parameters of the support and hanger (such as length, width, height, etc.) and attribute parameters (such as material, load, etc.), which can be parametrically adjusted and arranged according to the requirements of pipeline layout. The support and hanger family together with the pipeline family constitute a complete pipeline BIM model, which is used to represent the spatial layout and support relationship of the pipeline system. The building structure family refers to the parametric three-dimensional model used to represent the structural components of the building in the BIM software. Building structure families usually include structural components such as columns, beams, walls, and plates, including geometric parameters (such as length, width, height, etc.) and attribute parameters (such as material, strength, etc.) of the components. In the process of pipeline comprehensive optimization, it is necessary to perform collision checks between the pipeline BIM model and the building structure family to ensure that the pipeline layout plan meets the minimum spacing requirements and avoids collisions with the building structure. Axis-Aligned Bounding Box (AABB) is a simple geometric collision detection algorithm. In three-dimensional space, AABB represents a cuboid parallel to the coordinate axis, which is used to approximate the spatial range of an object. In the collision check process, by comparing whether there is overlap between two AABBs, it is possible to quickly determine whether two objects may collide. Compared with precise geometric collision detection, the collision detection algorithm based on AABB is faster in calculation, but there may be some misjudgment. Geometric collision refers to the situation where two or more objects overlap or intersect in space. In the process of pipeline comprehensive optimization, it is necessary to check whether there is geometric collision between pipelines and between pipelines and building structures to ensure the feasibility and safety of the pipeline layout plan.

[0030] Geometric collision detection usually uses computational geometry algorithms, such as AABB, OBB (Oriented Bounding Box), SAT (Separating Axis Theorem), etc., to determine whether a collision occurs by comparing the geometric boundaries or separation axes of objects. The collision element type refers to the type of two objects that collide during the collision check process. In the pipeline comprehensive optimization problem, the collision element types usually include pipelines and pipelines, pipelines and supports, pipelines and building structures, etc. By judging the collision element type, the severity of the collision and the optimization adjustment strategy can be determined. For example, the collision between pipelines usually requires adjusting the spatial position of the pipeline, while the collision between pipelines and building structures may require adjusting the layout path of the pipeline or taking special avoidance measures. The parametric family API (Application Programming Interface) refers to the secondary development interface provided by BIM software, which is used to operate and control the parametric geometry and attribute information in the family file. Through the parametric family API, family files can be created, modified, queried, and other operations can be performed outside the BIM software (such as C#, Python, and other programming environments) to achieve parametric control and automatic generation of family models. During the comprehensive optimization of pipelines, the parametric family API can be used to map the output results of the optimization algorithm (such as the ant colony algorithm) to the pipeline family instance in the BIM model, automatically generate the optimized pipeline BIM model, and improve the optimization efficiency and model accuracy.

[0031] Further, S4, using the optimal design parameters of the support and hanger, update the parameters of the support and hanger component family in the support and hanger BIM model, including: S41, inputting the support and hanger BIM model into the structural finite element analysis software, meshing the support and hanger BIM model, and generating the support and hanger finite element model; S42, extracting the support and hanger load conditions, material properties and boundary conditions from the support and hanger BIM model, inputting the support and hanger finite element model, performing structural statics analysis, and outputting the stress-strain cloud map and displacement cloud map of the support and hanger components; S43, constructing a topological optimization mathematical model of the support and hanger components based on the SIMP interpolation method; S44, The stress-strain cloud map and displacement cloud map of the support frame components are converted into stress constraints and displacement constraints of the mathematical model of topological optimization of the support frame components; S45, solving the mathematical model of topological optimization of the support frame components, outputting the optimal relative density distribution of the support frame components, and topologically reconstructing the support frame finite element model according to the optimal relative density distribution, and outputting the optimal topological structure and size parameters of the support frame components; S46, inputting the optimal topological structure and size parameters of the support frame components into the support frame BIM model, updating the parameters of the corresponding support frame component family in the support frame BIM model, and generating the optimized support frame BIM model.

[0032] Among them, boundary conditions refer to the constraints and load conditions imposed on the structural model in structural analysis. Boundary conditions usually include displacement constraints (such as fixed supports, hinged supports, etc.) and load conditions (such as concentrated forces, distributed forces, couples, etc.). Reasonable setting of boundary conditions is the basis for structural static analysis and directly affects the accuracy of the analysis results. In the support and hanger BIM model, boundary conditions can be set according to the connection relationship between the support and hanger and the pipeline and building structure. Structural static analysis refers to the method of calculating the stress, strain, displacement and other responses of the structure under the action of static load. Through structural static analysis, the force performance and deformation of the structure under given load conditions can be evaluated, providing a basis for structural optimization design. Commonly used structural static analysis methods include finite element method, matrix displacement method, etc. In the optimization design of the support and hanger, by performing structural static analysis on the support and hanger BIM model, the stress, strain and displacement distribution of the support and hanger components can be obtained for constructing a mathematical model for topological optimization.

[0033] The stress-strain cloud diagram refers to the stress or strain distribution of each point of the structure in the form of colored contour lines or cloud diagrams in the results of structural static analysis. The stress cloud diagram is usually expressed by VonMises equivalent stress, which can intuitively reflect the stress state and stress concentration area of ​​the structure. The strain cloud diagram represents the deformation degree and deformation mode of each point of the structure. By analyzing the stress-strain cloud diagram, the weak parts and optimization potential of the structure can be identified, providing guidance for topological optimization. The displacement cloud diagram refers to the displacement distribution of each point of the structure in the form of colored contour lines or cloud diagrams in the results of structural static analysis. The displacement cloud diagram can intuitively reflect the deformation characteristics and maximum displacement position of the structure under load. By analyzing the displacement cloud diagram, the overall stiffness and stability of the structure can be evaluated, and the structure can be optimized in combination with the deformation control requirements.

[0034] SIMP (Solid Isotropic Material with Penalization) interpolation method is a commonly used topology optimization method. In SIMP interpolation method, the design variable ρ is introduced e Relative density of the unit (0≤ρ e ≤1), through the exponential interpolation formula Establish the element stiffness matrix K e and relative density ρ e , where p is the penalty factor (usually 3), and K0 is the stiffness matrix of the solid material. e, the topological optimization of the structure can be achieved and the optimal material distribution can be obtained. The optimal relative density distribution refers to the optimal value of the relative density ρe of each unit obtained by solving the mathematical model of the topological optimization of the support frame components. The optimal relative density distribution reflects the optimal material distribution scheme of the support frame components and can guide the topological reconstruction and size optimization of the support frame components. In the optimal relative density distribution, the area with a larger relative density value indicates that more material should be retained, while the area with a smaller relative density value indicates that the material can be appropriately reduced to achieve the purpose of weight reduction and efficiency improvement.

[0035] Topological reconstruction refers to the process of adjusting and optimizing the topological structure of the support and hanger components according to the optimal relative density distribution. In topological reconstruction, the area with a small relative density value is usually regarded as a void or non-design area, and the area with a large relative density value is regarded as a solid material area. By reasonably selecting the relative density threshold, the optimal topological structure of the support and hanger components can be generated, and its size parameters can be further optimized to meet the strength, stiffness and stability requirements. According to the optimal relative density distribution, the relative density value of each unit in the finite element model of the support and hanger components is compared with the given threshold.

[0036] For units with relative density values ​​greater than or equal to the threshold, their original material properties and geometric dimensions are retained; for units with relative density values ​​less than the threshold, they are regarded as voids or non-design areas, that is, these units are removed from the support and hanger components. The geometric model of the support and hanger components is regenerated based on the retained units to obtain the support and hanger component model after topological optimization. This geometric model reflects the optimal topological structure of the support and hanger components. The size of the support and hanger component model after topological optimization is optimized to obtain the optimal size parameters of the support and hanger components, such as the cross-sectional size and thickness of the components. The support and hanger component model after topological optimization and size optimization is converted into a BIM model, and the geometric information and parameters of the corresponding components in the original support and hanger BIM model are updated. Topological reconstruction is to reconstruct the geometric model of the support and hanger components based on the optimal relative density distribution on the basis of the finite element model to obtain the optimal topological structure and size parameters. Then, the optimized geometric model is converted into a BIM model, and the original support and hanger BIM model is updated. This ensures that the optimization results can be seamlessly integrated with the BIM model, improving the efficiency and accuracy of the support and hanger design.

[0037] Relative density e It is the design variable introduced in SIMP interpolation method, which represents the relative density of unit material and its value range is between 0 and 1. e =1, it indicates that the unit is a solid material; when ρ e = 0, indicating that the unit is empty. By optimizing the relative density of each unit e, the material distribution can be optimized and the optimal topological form of the structure can be obtained. In the optimization process, the intermediate relative density value is penalized by the penalty factor p, which makes the design variable tend to 0 or 1 and obtains a clear solid-void boundary.

[0038] Further, S43, constructing a mathematical model of topological optimization of the support and hanger frame based on the SIMP interpolation method, including: S431, setting the relative density ρ of each unit of the support and hanger frame member e As a design variable, the unit stiffness matrix K of the support frame is established e and relative density ρ e SIMP interpolation relationship: Among them, ρ e is the relative density design variable, with a value range of [0, 1]; p is the penalty factor, which is usually greater than 1; K0 is the solid material unit stiffness matrix; VonMises equivalent stress σ e It is a commonly used stress assessment index used to determine the yield or failure of materials under multi-axial stress states. VonMises equivalent stress takes into account the normal stress and shear stress of the material in all directions and is obtained by calculating the second invariant of the stress tensor. When the VonMises equivalent stress exceeds the yield strength of the material, the material is considered to have yielded or failed. In the topological optimization of support and hanger components, VonMises equivalent stress is usually used as one of the optimization constraints to ensure that the optimized support and hanger components meet the strength requirements.

[0039] S432, taking minimization of the total volume of the support and hanger components as the support and hanger optimization objective, establish the support and hanger optimization objective function: minV = ∑(ρ e ×V e ), where V e is the unit volume of the support frame member; S433 is the VonMises equivalent stress σ of the support frame member in the structural statics analysis e To meet the strength constraint condition, the maximum displacement δ of the support frame member in the structural static analysis is max Satisfy the stiffness constraint and establish the constraint condition of the optimization model: σ e ≤[σ],δ max ≤[δ], where [σ] and [δ] are allowable stress and displacement respectively; S434, combining the optimization objective function and constraint conditions, a mathematical model for topological optimization of the support and hanger components is established:

[0040] minV=∑(ρ e ×V e )

[0041] Constraints:

[0042]

[0043] σ e ≤[σ]

[0044] δ max ≤[δ]

[0045] 0<ρ min ≤ρ e <1

[0046] Compared with the prior art, the advantages of this application are:

[0047] By extracting pipeline geometry and attribute parameters from building structure and electromechanical pipeline construction drawings, and using BIM software to build an initial pipeline model, pipeline design information can be obtained quickly and accurately, providing a data basis for subsequent pipeline layout optimization and improving information acquisition efficiency and accuracy.

[0048] The pipeline parameters, optimization objectives and constraints are input into the ant colony algorithm to optimize the pipeline layout, which fully considers the actual engineering needs of the pipeline layout. While ensuring that the specifications are met, the optimization is guided by the goals of shortest total pipeline length and least collision points, avoiding redundancy in pipeline layout and reducing waste of pipe materials.

[0049] According to the optimized pipeline BIM model, through parametric association and automatic generation of support and hanger component families, parametric intelligent association between supports and pipelines can be achieved, and the layout of supports and hangers better matches the optimized pipeline layout plan. Automatically adding the support and hanger component family to the pipeline model saves time for manual modeling and adjustment, and improves the generation efficiency of the support and hanger BIM model.

[0050] The BIM model of the support and hanger is imported into the structural analysis software for stress analysis, and the SIMP interpolation topology optimization method is used to parametrically optimize the support and hanger components. This can remove redundant materials in the design of the support and hanger components, and specifically reduce the deadweight of the support and hanger while ensuring the stress performance, saving the material consumption of the support and hanger and reducing the cost of the support and hanger.

[0051] Based on the parametric modeling and automatic drawing functions of BIM software, drawings are directly generated from the optimized support and hanger BIM model. The support and hanger BIM model is completely consistent with the drawing information, with high reliability, eliminating the manual drawing process and improving drawing efficiency and accuracy.

[0052] The ant colony algorithm is introduced in pipeline layout optimization. The ant search behavior guides the pipeline layout optimization, and the pheromone positive feedback mechanism is used to accelerate convergence, which overcomes the defect of traditional optimization methods that are prone to fall into local optimality and can search for a more globally optimal pipeline layout solution. Pipeline collision interference is reduced, the layout is more compact and reasonable, and the utilization rate of pipes is improved.

[0053] The SIMP interpolation topology optimization method is used in the optimization of the support and hanger components. The continuum variable parameterization is realized through the interpolation of material properties. The mathematical programming method is used for topological reconstruction to optimize the shape and size of the support and hanger components at the same time. Compared with traditional size optimization, topology optimization can more fully explore the potential of materials and obtain a support and hanger component design scheme with better performance and less material consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present application will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0055] Figure 1 is an exemplary flow chart of a BIM-based support and hanger design method according to some embodiments of the present application;

[0056] Figure 2 is an exemplary flow chart for obtaining an optimized pipeline BIM model according to some embodiments of the present application;

[0057] Figure 3 is an exemplary flow chart of updating constraint conditions according to some embodiments of the present application;

[0058] Figure 4 It is an exemplary flowchart for obtaining an optimized support and hanger BIM model according to some embodiments of the present application. DETAILED DESCRIPTION

[0059] The method and system provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0060] Figure 1This is an exemplary flow chart of a support and hanger design method based on BIM shown in some embodiments of the present application, including: obtaining geometric parameters and attribute parameters of pipelines from building structures and mechanical and electrical pipeline layout construction drawings as input, and using BIM software to build an initial pipeline model; based on the initial pipeline model, taking the geometric parameters, attribute parameters, optimization objectives and constraints of the pipeline as input, using the ant colony algorithm to solve the optimal pipeline layout plan, using the optimal pipeline layout plan to update the parameters in the initial pipeline model, and generating a pipeline BIM model; wherein the optimization objectives include the shortest total length of the pipeline and the least collision between the pipeline and the building structure; the constraints include the minimum spacing between pipelines and the minimum spacing between pipelines and the building structure; based on the pipeline The pipeline parameters in the BIM model are used to establish a parametric model library of supports and hangers through parametric family files, and the pipeline parameters in the pipeline BIM model are associated and matched with the parameters in the parametric model library of supports and hangers to generate a support and hanger component family, and the generated support and hanger component family is added to the pipeline BIM model to obtain a support and hanger BIM model; the support and hanger BIM model is imported into the structural finite element analysis software to perform structural force analysis, and the force analysis results are used as input to perform parametric optimization of the support and hanger components through the topology optimization algorithm to obtain the optimal design parameters of the support and hanger, and the optimal design parameters of the support and hanger are used to update the parameters of the support and hanger component family in the support and hanger BIM model; based on the updated support and hanger BIM model, the support and hanger drawings are generated using BIM software.

[0061] Extract the geometric parameters and attribute parameters of pipelines from the construction drawings of building structures and electromechanical pipeline layouts; first, you need to obtain the construction drawings of building structures and electromechanical pipeline layouts, which are usually drawn using CAD drawing software. Use the two-dimensional drawing import function provided by BIM software to import the building structure construction drawings and electromechanical pipeline layout construction drawings into BIM software. After importing the drawings, use the primitive recognition and extraction tools in BIM software to identify and extract parameters of pipelines. Identify the starting point and end point of the pipeline: By identifying the symbols or text annotations at both ends of the pipeline, extract the starting point coordinates (x1, y1, z1) and end point coordinates (x2, y2, z2) of the pipeline. Extract the pipe diameter: By identifying the line width or annotation of the pipeline primitive, extract the nominal diameter DN of the pipeline. Calculate the length of the pipeline: According to the starting point coordinates and end point coordinates of the pipeline, use the three-dimensional space two-point distance formula to calculate the actual length L of the pipeline. The steps for extracting attribute parameters are as follows: Extract pipeline material: Obtain the material information of the pipeline by identifying the drawing annotations or legends, such as steel pipes, copper pipes, PVC pipes, etc. Extract design pressure: Obtain the design pressure value of the pipeline by identifying the drawing markings, in MPa. Extract design temperature: Obtain the design temperature value of the pipeline by identifying the drawing markings, in °C. Extract medium type: Obtain the type of pipeline medium, such as water, steam, oil, etc., by identifying the drawing markings or pipeline types.

[0062] After extracting the pipeline geometric parameters and attribute parameters, the initial pipeline BIM model is constructed in the BIM software: Create a three-dimensional solid pipeline: Take the pipeline starting point coordinates (x1, y1, z1) and the end point coordinates (x2, y2, z2) as the benchmark, take the extracted pipe diameter DN as the cross-sectional diameter, and use the pipeline modeling command of the BIM software to generate a three-dimensional solid pipeline with a length of L. Associate pipeline attribute parameters: Select the generated three-dimensional solid pipeline and associate the extracted pipeline attribute parameters with it. Specifically include: Material attributes: Select the corresponding pipeline material in the material library of the BIM software, such as steel pipe, copper pipe, PVC pipe, etc., and assign it to the three-dimensional solid pipeline. Design pressure attribute: Enter the extracted design pressure value into the parametric attribute of the three-dimensional solid pipeline, and set the unit to MPa. Design temperature attribute: Enter the extracted design temperature value into the parametric attribute of the three-dimensional solid pipeline, and set the unit to ℃. Medium type attribute: Enter the extracted medium type information into the parametric attribute of the three-dimensional solid pipeline, such as water, steam, oil, etc. The three-dimensional solid models of all pipelines are created and the corresponding attribute parameters are associated.

[0063] Figure 2 The exemplary flow chart of obtaining the optimized pipeline BIM model according to some embodiments of the present application is as follows: Generate the pipeline BIM model: Extract the geometric parameters and attribute parameters of the pipeline from the initial pipeline BIM model: Geometric parameter conversion: Extract the starting coordinates (x start ,y start ,z start ) and the end point coordinates (x end ,y end ,z end ), forming the pipeline spatial coordinate parameter set P_line = {(x start ,y start ,z start ), (x end ,y end ,z end)}. The nominal diameter parameter d of each pipeline section in the initial pipeline BIM model is extracted through RevitAPI to form the pipeline diameter parameter set D_line = {d}. According to the coordinates of the starting point and the end point of the pipeline, the actual length l of each pipeline section is calculated to form the pipeline length parameter set L_line = {l}, which is used as the path length parameter of the ant colony algorithm. The coordinate parameter set P_line, the diameter parameter set D_line and the length parameter set L_line are used as the spatial search dimensions and distance calculation parameters of the ant colony algorithm. The ant colony algorithm uses the point coordinates in P_line as the search node, the pipeline length in L_line as the path length between nodes, and the diameter in D_line as the basis for judging the pipeline layout constraints.

[0064] Attribute parameter conversion: The material attribute M of each pipeline section in the initial pipeline BIM model is extracted through RevitAPI to form the pipeline material parameter set Mat_line = {M}. The material parameters in Mat_line are associated with the pipeline support and hanger spacing constraints. For example, when M is "steel pipe", the maximum support and hanger spacing s_max is 6m; when M is "copper pipe", the maximum support and hanger spacing s_max is 4m. The design pressure attribute P of each pipeline section in the initial pipeline BIM model is extracted through RevitAPI to form the pipeline design pressure parameter set Pre_line = {P}. The design pressure parameters in Pre_line are associated with the pipeline support and hanger spacing constraints. For example, when P ≥ 1.6MPa, the maximum support and hanger spacing s_max is 3m; when P < 1.6MPa, the maximum support and hanger spacing s_max is 6m. The design temperature attribute T of each pipeline section in the initial pipeline BIM model is extracted through RevitAPI to form the pipeline design temperature parameter set Tem_line = {T}, which is used as the parameter basis for calculating the thermal expansion and contraction during the pipeline layout process. The medium type attribute F of each pipeline section in the initial pipeline BIM model is extracted through RevitAPI to form the pipeline medium type parameter set Flu_line = {F}. Associate the medium type parameter in Flu_line with the minimum spacing constraint d_min_s between the pipeline and the building structure. For example, when F is "sewage", the value of d_min_s is 500mm; when F is "compressed air", the value of d_min_s is 300mm.

[0065] Constructing the objective function: According to the goal of pipeline layout optimization, the objective function of the ant colony algorithm is constructed as follows:

[0066] minF=ω1×Σl i +ω2×Σc j +ω3×Σn k , where Σl irepresents the total length of the pipeline, Σc j represents the total collision point penalty value between pipeline and building structure, ∑n k Indicates the number of supports and hangers in the pipeline layout plan; ω1, ω2, ω3 represent the weight coefficients of pipeline length, collision point, and number of supports and hangers, respectively, which can be set according to the actual project and optimization preferences. Set constraints: According to the design specifications for pipeline layout, set the following constraints: Minimum spacing threshold d_min_p between pipelines: According to the design specifications, take d_min_p = 100mm. Minimum spacing threshold d_min_s between pipelines and building structures: According to the association between Flu_line and d_min_s, take the corresponding value. Maximum spacing of pipeline supports and hangers s_max_i: According to the association between Mat_line, Pre_line and s_max, take the corresponding value. Maximum length of pipeline cantilever f_max_i: According to the design specifications, the length of the cantilever pipeline shall not exceed 1m, that is, f_max_i = 1000mm. Allowable value of pipeline stress [σ]: According to the design specifications, take the allowable stress value of the pipeline material as [σ].

[0067] Use the ant colony algorithm to optimize the pipeline layout: Initialize the parameters of the ant colony algorithm: set the number of ants m = 50, the maximum number of iterations N_max = 200, the pheromone importance factor α = 1, the heuristic function importance factor β = 5, the pheromone volatility coefficient ρ = 0.1, and the global pheromone update intensity Q = 100. Construct the fitness function: The fitness function is defined as:

[0068] Construct pipeline layout solution space: Based on the extracted pipeline geometric parameter sets P_line, D_line and L_line, and the building structure information in the BIM model, the pipeline layout solution space in three-dimensional space is constructed. In the solution space, m feasible solutions that meet the constraints are randomly generated as the starting point of each ant.

[0069] Ant search phase: For each ant k (k = 1, 2, ..., m), select the node j to move next according to the state transition probability function: Heuristic function η ij (t) is defined as: c ij represents the number of collision points on the path from node i to j, and λ is the collision point penalty coefficient. After ant k moves to the new node, it determines whether its new solution meets the set constraints and calculates the fitness function Fitness_k.

[0070] Pheromone update phase: Local pheromone update: After each ant completes a search, it performs pheromone update on the path (i, j) it has passed: Global pheromone update: After each iteration, the optimal solution of this iteration is used to update the pheromone of the entire solution space:

[0071] τ ij (t+1)=(1-ρ)×τ ij (t)+ρ×ΔτΔτ ij (t). Termination condition judgment: If the current number of iterations reaches the maximum number of iterations N_max, or the optimal solution has not been improved after 30 consecutive iterations, the algorithm terminates and outputs the current optimal solution as the final pipeline layout solution; otherwise, it returns to continue the iterative search.

[0072] Specifically, three pipelines need to be arranged in a certain building. The pipeline materials are stainless steel, galvanized steel and copper, the design pressures are 1.0MPa, 0.6MPa and 0.4MPa, the pipe diameters are 50mm, 40mm and 25mm, and the fluids are water, compressed air and oil. The building structure consists of 20 nodes and 30 edges, and the connection relationship between the node coordinates and the edges is known. Initialize the parameters of the ant colony algorithm: the number of ants m = 50; the maximum number of iterations N_max = 200; the pheromone importance factor α = 1; the heuristic function importance factor β = 5; the pheromone volatility coefficient ρ = 0.1; the global pheromone update intensity Q = 100. Construct the fitness function: according to the pipeline layout target and the penalty factor, determine the weight coefficients: ω_1 = 0.5, ω_2 = 0.2, ω_3 = 0.1, ω_4 = 0.1, ω_5 = 0.1.

[0073] Construct pipeline layout solution space: Extract the geometric parameters of 3 pipelines: P_line = {50, 40, 25}, D_line = {1.0, 0.6, 0.4}, L_line = {20, 15, 10}; Construct a three-dimensional solution space based on the 20 nodes and 30 edges of the building structure; Randomly generate 50 feasible solutions that meet the constraints as the starting point. Ant search stage: Set the collision point penalty coefficient λ = 10. For each ant k (k = 1, 2, ..., 50), select the next node according to the state transition probability function: When j∈allow k ; Heuristic function Turning factor γ ij and the height change rate factor δ ij According to the actual calculation; after the ant moves to the new node, it determines whether the new solution meets the constraints and calculates the fitness function Fitness_k.

[0074] Pheromone update phase: Local pheromone update: τ ij (t+1)=0.9×τ ij (t)+0.1×τ0; global pheromone update: (i, j)∈best solution. Termination condition judgment: Set the maximum number of iterations N_max=200, and the number of times the optimal solution is not improved continuously is 30; when the number of iterations reaches 200 or the optimal solution is not improved for 30 consecutive times, the algorithm terminates and outputs the current optimal pipeline layout solution.

[0075] The algorithm terminated at the 150th iteration, and the optimal pipeline layout plan was obtained as follows: Pipeline 1 (stainless steel): the node sequence is 1-5-11-15-20, the total length of the pipeline is 18m, and the number of supports and hangers is 4; Pipeline 2 (galvanized steel): the node sequence is 2-6-10-14-18, the total length of the pipeline is 16m, and the number of supports and hangers is 4; Pipeline 3 (copper): the node sequence is 3-7-12-16-19, the total length of the pipeline is 12m, and the number of supports and hangers is 3; the minimum distance between the pipeline and the building structure is 120mm, which meets the distance constraint; there are 3 collision points in the pipeline layout plan, and the penalty value is small; the pipeline stress meets the allowable stress requirements, and the ratio of the maximum stress to the allowable stress is 0.85.

[0076] Figure 3 is an exemplary flow chart of updating constraint conditions according to some embodiments of the present application. According to the optimal pipeline layout plan, the spatial position and connection relationship of the pipeline are adjusted in the BIM software to generate an optimized pipeline BIM model: the geometric parameters of each pipeline segment in the optimal layout plan are extracted: the starting coordinates (x start-opt ,y start-opt ,z start-opt )、end point coordinates (x end-opt ,y end-opt ,z end-opt ) and pipe diameter d opt , forming the geometric parameter set P of the optimized pipeline segment line-opt Adjust the spatial position and parameters of pipeline segments in BIM software: Select the pipeline segment that matches the P in the initial pipeline BIM model through Revit API. line-opt The pipeline family instance corresponding to the middle pipeline segment. Modify the start and end position parameters of the pipeline family instance and set them to (x start-opt ,y start-opt ,z start-opt ) and (x end-opt ,y end-opt ,z end-opt ). Modify the pipe diameter parameter of the pipeline family instance and set it to d opt Repeat until P line-optAll pipeline segments in the pipeline have completed parameter modification. Reconnect pipeline segments: According to the pipeline topology in the optimal layout plan, reconnect the adjusted pipeline segments in the BIM software through Revit API to form a complete pipeline system model. Output the optimized pipeline BIM model: Save the adjusted pipeline BIM model in Revit to complete the implementation of the pipeline layout optimization plan in the BIM model.

[0077] Perform collision check on the optimized pipeline BIM model: Extract the pipeline family instances, support and hanger family instances, and building structure family instances in the optimized pipeline BIM model through Revit API to form a collision check set C_set. Perform collision detection: For any two family instances A and B in C_set, use the collision detection algorithm based on AABB bounding box to perform collision calculation. Calculate the bounding boxes AABB_A and AABB_B of A and B, and determine whether AABB_A and AABB_B intersect. If they intersect, record the coordinates of the collision point (x col ,y col ,z col ) and collision element types type_A and type_B. Repeat until all family instance pairs in C_set complete collision detection. Output collision report Col_Rep, including the coordinates of all collision points and collision element type information.

[0078] Analyze the collision report: filter and classify Col_Rep, extract the collision information between pipelines and between pipelines and building structures, and record them as Col_pipe and Col_stru respectively. Update the ant colony algorithm model: if there is collision information in Col_pipe or Col_stru, update the collision point coordinates (x col ,y col ,z col ) as a new constraint and add it to the constraint set. According to the updated constraint set, update the objective function and search space of the ant colony algorithm. Re-execute pipeline layout optimization: input the updated ant colony algorithm model parameters and constraints, re-execute pipeline layout optimization, and generate a new optimal layout plan. According to the new optimal layout plan, repeat step S23 to generate a new optimized pipeline BIM model. Iterative optimization: repeat until there is no collision information in Col_pipe and Col_stru. Output the final optimized collision-free pipeline BIM model.

[0079] Figure 4It is an exemplary flow chart for obtaining an optimized support and hanger BIM model shown in some embodiments of the present application, constructing a support and hanger BIM model, and establishing a support and hanger parametric family file in the BIM software based on the pipeline geometric parameters and attribute parameters in the obtained pipeline BIM model. First, based on the common design parameters of the support and hanger, such as material, cross-sectional size, length and connection method, the basic framework of the support and hanger parametric family file is created. In the family file, the geometric shapes of the support and hanger components are defined, such as straight, L-shaped, U-shaped, etc., and the dimensional parameters of the geometric shapes are associated with the design parameters of the support and hanger to achieve parametric control of the support and hanger components. On the basis of the support and hanger parametric family file, a series of parametric instances of support and hanger components are generated by adjusting different design parameter combinations, such as material, cross-sectional size and length. These instances are saved and organized to build a support and hanger parametric model library, so that the required support and hanger components can be quickly matched and generated according to the pipeline parameters in the future.

[0080] Extract the geometric parameters and attribute parameters of each section of pipeline in the pipeline BIM model, including the starting coordinates, end coordinates, pipe diameter, material, etc. of the pipeline. Use these parameters as query conditions to search for matching support and hanger components in the support and hanger parametric model library. The matching principle can be set according to design specifications and engineering experience, such as selecting appropriate support and hanger cross-sectional dimensions and materials according to pipe diameter and material, and determining the layout spacing of supports and hangers according to pipeline length. Based on the matching results, automatically generate a support and hanger component family instance that matches the pipeline layout. For each section of pipeline, select a suitable support and hanger component according to its parameters, and adjust the length, connection method and other parameters of the support and hanger to adapt to the actual layout of the pipeline. The generated support and hanger component family instance contains geometric parameters and attribute parameters that match the pipeline, ensuring the coordination between the support and hanger and the pipeline.

[0081] In the BIM software, insert the generated support and hanger member family instance into the pipeline BIM model. Use the automatic capture function of the BIM software to identify the starting and ending coordinates of the pipeline, and match and connect the connection points of the support and hanger members with the endpoints of the pipeline. According to the spacing requirements of the support and hanger arrangement, evenly arrange multiple support and hanger members on the pipeline, and make necessary fine-tuning to avoid collision with other components. After completing the connection between the support and hanger members and the pipeline, a complete support and hanger BIM model is formed. The support and hanger BIM model contains support and hanger members that match the pipeline, reflecting the coordinated relationship between the pipeline layout and the support and hanger layout. The support and hanger BIM model can be adjusted and optimized in detail as needed, such as adjusting the position and angle of the support and hanger to meet the force requirements and aesthetic requirements of the pipeline system.

[0082] Generate an optimized support and hanger BIM model, including: input the support and hanger BIM model into the structural finite element analysis software, mesh the support and hanger BIM model, and generate a support and hanger finite element model. First, import the support and hanger BIM model into the structural finite element analysis software, such as ANSYS, ABAQUS, etc., through the data interface. Then, according to the geometric shape and size of the support and hanger components, select the appropriate mesh type and size to mesh the support and hanger BIM model, and generate a support and hanger finite element model consisting of a finite number of nodes and units. When meshing, it is necessary to take into account both calculation accuracy and efficiency, and appropriately encrypt the mesh in key parts of the support and hanger, such as the connection area.

[0083] Extract the load information of the supports and hangers from the supports and hangers BIM model. First, read the load-related parameters and properties in the supports and hangers BIM model through the API interface or data exchange format (such as IFC) provided by the BIM software. For the pipeline deadweight load, it can be calculated from the geometric parameters (such as length, diameter) and material properties (such as density) of the pipeline. For the medium weight load, the volume and weight of the medium can be estimated according to the type and filling rate of the medium carried by the pipeline. For thermal stress loads, the thermal deformation of the pipeline can be calculated according to the temperature change and thermal expansion coefficient of the pipeline. The extracted load information is classified and sorted according to type, size and distribution location.

[0084] Convert the extracted load information into the nodal force or unit body force of the finite element model. Select the appropriate load application method according to the type and distribution characteristics of the load. For concentrated loads (such as the weight at the pipeline flange), it can be converted into a nodal force acting on the corresponding node. For distributed loads (such as the dead weight of the pipeline), it can be converted into a unit body force acting on the corresponding unit, and the equivalent nodal force is obtained by integrating over the unit. For thermal stress loads, it can be converted into the thermal strain of the unit by defining the temperature field and thermal expansion coefficient, and then the corresponding thermal stress is calculated. In the process of load conversion, it is necessary to consider the direction, size and distribution area of ​​the load to ensure that the converted nodal force and unit body force can accurately reflect the actual load conditions.

[0085] Extract the material properties of the support and hanger components from the support and hanger BIM model. Obtain the material type of the component (such as steel, aluminum alloy, etc.) by accessing the material properties of the support and hanger components in the BIM model. According to the material type, query the corresponding material parameters from the material property database, such as elastic modulus, Poisson's ratio, density, etc. Assign the queried material parameters to the corresponding units in the finite element model to ensure the accuracy of the material properties in the finite element analysis. Define the boundary conditions of the finite element model according to the actual installation conditions of the support and hanger. Extract the connection mode and position information between the support and hanger and the building structure from the support and hanger BIM model. For fixed connections, constrain the corresponding node degrees of freedom to zero in the finite element model to simulate the fixed support effect. For hinged connections, release the corresponding node rotational degrees of freedom in the finite element model to simulate the hinge support effect. For sliding connections, release the displacement degrees of freedom of the corresponding nodes along the sliding direction in the finite element model to simulate the sliding support effect. Define the boundary conditions reasonably to accurately reflect the actual stress state of the support and hanger.

[0086] After completing the definition of loads, material properties and boundary conditions, the finite element analysis software is used to perform structural static analysis. The finite element model of the support and hanger is imported into the finite element analysis software, and the defined loads, material properties and boundary conditions are assigned to the corresponding units and nodes. Set the solution parameters, such as solution method, convergence conditions, etc., and start the finite element solver for calculation. During the solution process, the finite element analysis software uses numerical methods (such as Gaussian elimination method) based on the equilibrium equation, geometric equation and constitutive equation to calculate the node displacement, and calculates the unit stress and strain according to the stress-strain relationship. Output the stress-strain cloud map and displacement cloud map of the support and hanger components. After the finite element analysis is completed, the post-processing module visualizes the calculation results and generates an intuitive cloud map representation. The stress cloud map represents the stress level of each part of the support and hanger components through different colors or contour lines, revealing the stress concentration area and potential weak parts. The strain cloud map represents the strain distribution of the support and hanger components through colors or contour lines, reflecting the deformation state of the components. The displacement cloud diagram displays the displacement distribution of the support frame components under load through color or deformation scaling, and intuitively shows the overall deformation trend of the components. By analyzing the stress-strain cloud diagram and the displacement cloud diagram, the stress performance of the support frame components can be evaluated, and areas with excessively high stress levels or excessive deformation can be identified, providing a basis for subsequent optimization design.

[0087] The mathematical model of topological optimization of the support and hanger components is constructed based on the SIMP interpolation method, including: setting the relative density of each unit of the support and hanger components as the design variable and establishing the SIMP interpolation relationship. The support and hanger components are divided into a finite number of units, each of which corresponds to a design variable, namely, the relative density ρ of the unit e . Relative density ρ e The value range of is [0, 1], which indicates the filling degree of the unit material. e= 0, indicating that the unit material is completely removed; when ρ e =1, indicating that the unit material is completely retained; when 0<ρ e When <1, it means that the unit material is partially retained. Select an appropriate penalty factor p, usually greater than 1. The role of the penalty factor p is to make the value of the unit relative density tend to 0 or 1, avoiding a large number of intermediate density values ​​between 0 and 1. The larger the p value, the more obvious the penalty effect on the intermediate density value, and the more the optimization result tends to a clear "solid-void" structure.

[0088] Establish the unit stiffness matrix Ke and relative density ρ e SIMP interpolation relationship: Among them, K0 is the stiffness matrix of the solid material unit, which can be calculated based on the material properties of the support frame components. The material properties of the unit are related to the relative density through the SIMP interpolation formula. When ρ e When it approaches 0, the element stiffness matrix approaches the zero matrix, indicating that the element material is removed; when ρ e When it approaches 1, the element stiffness matrix approaches the solid material stiffness matrix, indicating that the element material is retained.

[0089] The optimization objective is to minimize the total volume of the support and hanger components and establish the optimization objective function. The total volume V of the support and hanger components is defined as the volume of all units V e The weighted sum of e That is, V = Σ(ρ e ×V e ), where Σ represents the sum of all units. Establish the optimization objective function: minV = ∑(ρ e ×V e ), which means that the optimization goal is to minimize the total volume of the support and hanger components. By minimizing the total volume, the optimal material distribution scheme is found under the premise of satisfying the constraints, and the lightweight support and hanger component design is obtained.

[0090] Establish the constraints of the optimization model, including strength constraints and stiffness constraints. Strength constraints: VonMises equivalent stress σ of the support frame components in the structural statics analysis e The allowable stress [σ] requirement must be met, that is, σ e ≤[σ]. Where [σ] is determined according to the material properties and design specifications of the support and hanger components. Stiffness constraint: The maximum displacement δ of the support and hanger components in the structural statics analysis max The allowable displacement [δ] requirement must be met, that is, δ max≤[δ]. [δ] is determined according to the use requirements and design specifications of the support and hanger components. By introducing strength constraints and stiffness constraints, it is ensured that the optimized support and hanger components can withstand the actual load and meet the use requirements, avoiding the situation of insufficient strength or insufficient stiffness.

[0091] Combining the optimization objective function and constraint conditions, a mathematical model for topological optimization of the support and hanger components is established. Optimization design variables: relative density ρ of each unit of the support and hanger components e , the value range is [ρmin, 1], where ρmin is the lower limit of the relative density, usually a small value close to 0 to avoid the occurrence of singular stiffness matrix during the optimization process. Optimization objective function: minV = Σ(ρ e ×V e ), minimize the total volume of the support frame components. Constraints: SIMP interpolation relationship: Establish the relationship between the unit stiffness matrix and relative density; strength constraint: σ e ≤[σ], the VonMises equivalent stress of the support frame components must meet the allowable stress requirements; stiffness constraint: δ max ≤[δ], the maximum displacement of the support frame member must meet the allowable displacement requirements; relative density value range: 0<ρ min ≤ρ e <1, ensuring that the relative density is within a reasonable range. The optimization objective function, constraints and design variables are combined into a complete mathematical model to form a standard form of the topology optimization problem of the support and hanger components.

[0092] Specifically, in the topology optimization of a support frame component of a certain project, a support frame component made of Q235 steel is to be optimized, with an elastic modulus E = 210GPa, Poisson's ratio ν = 0.3, and a density ρ = 7850kg / m 3 The support frame is divided into 1000 hexahedral units, each unit size is 10mm×10mm×10mm, and the unit volume Ve=1000mm 3 During use, the support and hanger components are subjected to a uniformly distributed load of q = 1 MPa, an allowable stress of [σ] = 200 MPa, and an allowable displacement of [δ] = 1 mm. The relative density ρe of each unit of the support and hanger components is set as the design variable, and the SIMP interpolation relationship is established.

[0093] Taking the penalty factor p = 3, the solid material unit stiffness matrix K0 is as follows:

[0094]

[0095] The SIMP interpolation relationship between the unit stiffness matrix Ke of the support and hanger member and the relative density ρe is established: The optimization objective is to minimize the total volume of the support frame components and establish the optimization objective function. The total volume of the support frame components V = Σ(ρe·Ve) = 1000×Σρemm 3 , optimization objective function: minV = 1000 × Σρe. Establish the constraints of the optimization model, including strength constraints and stiffness constraints. Through structural statics analysis, the unit stress σe and node displacement δ of the support frame are obtained. Strength constraint: σe≤200MPa, stiffness constraint: δmax≤1mm. Combined with the optimization objective function and constraints, a mathematical model for topological optimization of the support frame is established. Taking the lower limit of relative density ρmin = 0.001, the optimization mathematical model is: minV = 1000 × Σρe; stKe = ρe 3 ·K0, σe≤200MPa, δmax≤1mm, 0.001≤ρe≤1.

[0096] The stress-strain cloud diagram and displacement cloud diagram of the support and hanger components are converted into the constraints of the optimization mathematical model. Extraction of stress constraints: The stress-strain cloud diagram of the support and hanger components under a given load is obtained through finite element analysis. Different colors in the cloud diagram represent different stress levels. Extract the Von Mises equivalent stress value σ of each unit of the support and hanger components from the stress cloud diagram. e VonMises equivalent stress is a commonly used structural strength evaluation index, which comprehensively considers the normal stress and shear stress of the material. e Compared with the material allowable stress [σ], the stress constraint inequality is formed: σ e ≤[σ]. If the unit stress exceeds the allowable stress, it indicates that the unit may be damaged and needs to be adjusted during optimization. Extraction of displacement constraint conditions: The displacement cloud map of the support frame components under a given load is obtained through finite element analysis. Different colors in the cloud map represent different displacement levels. Extract the maximum displacement value δ of the support frame components from the displacement cloud map max , the maximum displacement reflects the overall stiffness of the structure under load. max Compared with the allowable displacement limit [δ], the displacement constraint inequality is formed: δ max ≤[δ]. If the maximum displacement exceeds the allowable displacement, it indicates that the structural stiffness is insufficient and needs to be improved during optimization. e ≤[σ] and the displacement constraint inequality δ max ≤[δ] is substituted into the established optimization mathematical model as the optimization constraint to ensure that the optimized support and hanger components meet the strength and stiffness requirements.

[0097] Solve the mathematical model of topological optimization of the support and hanger components and output the optimal topological structure and dimensional parameters. Select a suitable optimization algorithm, such as MMA, OC, etc., to solve the established mathematical model of topological optimization. The MMA algorithm constructs a simplified local approximate subproblem and uses the idea of ​​asymptotes to improve the optimization efficiency while ensuring global convergence. The OC algorithm is based on the optimality criterion and iteratively updates the design variables so that the structure satisfies the constraints while the objective function value continues to decrease until the optimal solution is reached. The optimization algorithm iteratively solves the mathematical model to obtain the optimal relative density value ρ of each unit of the support and hanger components. e . The relative density value reflects the distribution of the material in the unit. ρe=0 means that the material is completely removed, and ρe=1 means that the material is completely retained. According to the optimal relative density distribution, the topology of the support frame components is reconstructed. The units with small relative density values ​​(such as ρe<0.3) are regarded as non-essential units and deleted, and the units with large relative density values ​​(such as ρe>0.7) are retained to generate the optimal topological structure of the support frame components. According to the optimal topological structure, the key dimensional parameters of the support frame components, such as the cross-sectional size and length of the rods, are extracted as the basis for subsequent detailed design and manufacturing.

[0098] Specifically, a certain support frame component consists of 100 units, each unit has a size of 10mm×10mm×10mm, the material is Q235 steel, the elastic modulus E=206GPa, Poisson's ratio ν=0.3, the allowable stress [σ]=235MPa, and the allowable displacement [δ]=2mm. The MMA algorithm is selected to solve the established mathematical model of topological optimization of the support frame component. Objective function: minimize the structural flexibility, that is, maximize the structural stiffness. Design variables: the relative density ρe of each unit, with a value range of [0, 1]. Constraints: volume constraint V(ρ)≤0.5V0, V0 is the initial design domain volume; stress constraint σe(ρ)≤235MPa; displacement constraint δmax(ρ)≤2mm. The MMA algorithm iteratively solves the mathematical model of topological optimization, sets the maximum number of iterations to 50, and the convergence error to 0.01. Initial design: the relative density ρe of all units is 1, that is, fills the entire design domain.

[0099] Iteration process: Step 1: Calculate the objective function value and constraint value of the initial design, and construct an approximate subproblem according to the MMA algorithm. Step 2: Solve the approximate subproblem, update the unit relative density ρe, and obtain a new topological configuration. Step 3: Calculate the objective function value and constraint value of the new configuration, and check whether the convergence condition is met. Step 4: If the convergence condition is not met, return to step 2 and continue iterating; if the convergence condition is met, output the optimal solution.

[0100] The MMA algorithm converges after 30 iterations, and the optimal relative density distribution of each unit of the support and hanger member is obtained as follows: 20 units with a relative density ρe>0.9 are regarded as key load-bearing units, and the material is completely retained. 30 units with a relative density of 0.3≤ρe≤0.9 are regarded as transition zone units, and the material is partially retained. 50 units with a relative density ρe<0.3 are regarded as non-essential units, and the material is completely removed. According to the optimal relative density distribution, the support and hanger member is topologically reconstructed. 50 non-essential units with a relative density ρe<0.3 are deleted, and 20 key load-bearing units with a relative density ρe>0.9 and 30 transition zone units with a relative density of 0.3≤ρe≤0.9 are retained. The optimal topological structure of the support and hanger member is generated, consisting of 50 units, and the volume is 50% of the original design. According to the optimal topological structure, the key size parameters of the support and hanger member are extracted. The cross-sectional size of the load-bearing unit is 10mm×10mm, and the length is 50mm. The cross-sectional dimensions of the transition zone unit are 8 mm × 8 mm and the length is 40 mm. The spatial layout and connection mode of each unit are determined according to the optimal topological structure.

[0101] Input the optimal topological structure and dimensional parameters of the support and hanger components into the support and hanger BIM model to update the BIM model. Compare the optimal topological structure and dimensional parameters of the support and hanger components with the original support and hanger BIM model to identify the support and hanger component family that needs to be updated. Use the parametric modeling function provided by the BIM software, such as the family editor of Revit, to modify the topological structure and dimensional parameters of the corresponding support and hanger component family to generate an optimized support and hanger component family. In the family editor, adjust the geometric form of the component family according to the optimal topological structure, delete unnecessary components, and retain key components. According to the optimized dimensional parameters, modify the parametric dimensions of the component family, such as cross-sectional size, length, etc. Load the optimized support and hanger component family into the original support and hanger BIM model, replace the corresponding components, and complete the update of the support and hanger BIM model. Check and verify the updated support and hanger BIM model to ensure the integrity and accuracy of the model and obtain a lightweight and efficient support and hanger design solution.

[0102] Generate construction drawings of supports and hangers based on the optimized and updated support and hanger BIM model, including: Open the optimized and updated support and hanger BIM model in the BIM software, which contains the optimized support and hanger component information. Use the information extraction function provided by the BIM software, such as Revit's material list and dimensioning tools, to automatically extract the material type, geometric dimensions and other information of the support and hanger components. Material information includes the material, specification, model, quantity, etc. of the component. The BIM software can automatically count and generate a material list based on the material properties in the model. Geometric dimension information includes the length, width, height, cross-sectional dimensions, etc. of the component. The BIM software can automatically extract and generate dimensioning based on the geometric parameters in the model. Based on the extracted material information, a support and hanger material list is automatically generated. The material list lists the material, specification, model, quantity, unit and other information of each component, which is convenient for material procurement and cost control.

[0103] Call the predefined 2D drawing template in BIM software, such as Revit's drawing template, to convert the support and hanger BIM model into the support and hanger plan layout and elevation layout. The plan layout shows the distribution of supports and hangers on the horizontal plane, including the plane position, size, number and other information of the support and hanger components. The elevation layout shows the distribution of supports and hangers on the vertical plane, including the height, spacing, connection method and other information of the support and hanger components. The drawing template contains common drawing elements such as drawing frames, title bars, and legends to ensure that the generated construction drawings meet industry standards and design specifications. Use the annotation tools of BIM software, such as Revit's dimension annotation and text annotation, to automatically annotate the number, size and other information of the support and hanger components. In the plan layout and elevation layout, number each support and hanger component to form a unique component identification to facilitate on-site installation and management.

[0104] According to the extracted geometric dimension information, key dimensions such as length, width, height, and cross-sectional dimensions of the support and hanger components are marked in the construction drawings to guide the construction. The marked position, font, size and other parameters can be set according to the design specifications and drawing templates to ensure the standardization and readability of the marking. Review and modify the generated support and hanger plan layout and elevation layout drawings to ensure the integrity, accuracy and constructability of the construction drawings. Check whether the component numbering and dimensioning in the construction drawings are accurate and consistent with the BIM model.

[0105] Review whether the layout, proportion, legend, etc. of the construction drawings meet the design specifications and drawing standards. Make necessary modifications and improvements to the construction drawings based on the review results to ensure the quality of the construction drawings. Output the final support and hanger construction drawings, including floor plans, elevations, material lists, etc., and submit them to the design review and construction units. Export the construction drawings to common image formats (such as PDF, DWG, etc.) for easy transmission and sharing. Export the material list to a spreadsheet format (such as Excel) to facilitate material procurement and cost accounting. According to the design review and construction requirements, make necessary modifications and optimizations to the construction drawings to form formal construction drawings.

Claims

1. A support and hanger design method based on BIM, comprising: S1, obtain the geometric parameters and attribute parameters of the pipeline from the building structure and mechanical and electrical pipeline layout construction drawings, as input, and use BIM software to build the initial pipeline model; S2, based on the initial pipeline model, takes the geometric parameters, attribute parameters, optimization objectives and constraints of the pipeline as input, uses the ant colony algorithm to solve the optimal pipeline layout plan, uses the optimal pipeline layout plan to update the parameters in the initial pipeline model, and generates a pipeline BIM model; wherein the optimization objectives include the shortest total length of the pipeline and the least collision between the pipeline and the building structure; the constraints include the minimum spacing between pipelines and the minimum spacing between pipelines and the building structure; S3, according to the pipeline parameters in the pipeline BIM model, a support and hanger parametric model library is established through a parametric family file, the pipeline parameters in the pipeline BIM model are associated and matched with the parameters in the support and hanger parametric model library, a support and hanger component family is generated, and the generated support and hanger component family is added to the pipeline BIM model to obtain a support and hanger BIM model; S4, import the support and hanger BIM model into the structural finite element analysis software to perform structural force analysis, and use the force analysis results as input to perform parametric optimization on the support and hanger components through the topology optimization algorithm to obtain the optimal design parameters of the support and hanger, and use the optimal design parameters of the support and hanger to update the parameters of the support and hanger component family in the support and hanger BIM model; S5, generating the support and hanger drawings using BIM software according to the updated support and hanger BIM model; S1, build the initial pipeline model, including: Extract the geometric parameters and attribute parameters of pipelines from the construction drawings of building structures and electromechanical pipeline layouts; the geometric parameters include the starting coordinates, end coordinates, pipe diameter and length of the pipeline; the attribute parameters include the material, design pressure, design temperature and medium type of the pipeline; The extracted geometric parameters of the pipeline are input into the BIM software, and a three-dimensional solid pipeline equal to the length of the pipeline is generated based on the starting and ending coordinates of the pipeline and the pipe diameter as the cross-sectional size; In the BIM software, the extracted pipeline attribute parameters are associated with the generated three-dimensional solid pipeline, the pipeline material, design pressure, design temperature and medium type are assigned to the corresponding three-dimensional solid pipeline, and the initial pipeline BIM model containing pipeline geometric parameters and attribute parameters is constructed; S2, using ant colony algorithm to solve the optimal pipeline layout solution and generate pipeline BIM model, including: S21, the geometric parameters and attribute parameters of the pipelines extracted from the initial pipeline BIM model are used as the input of the ant colony algorithm; the shortest total length of the pipelines and the least collision between the pipelines and the building structure are taken as the optimization goals; the minimum spacing between pipelines and the minimum spacing between pipelines and the building structure are taken as the constraint conditions; S22, in the ant colony algorithm, ants search for the optimal path in the solution space, continuously update pheromones, iteratively search for pipeline layout solutions that meet constraints and optimization goals, obtain the optimal solution for pipeline layout, and output the optimal pipeline layout solution; S23, according to the optimal pipeline layout plan, adjusting the spatial position and connection relationship in the initial pipeline BIM model through BIM software to generate an optimized pipeline BIM model; S24, performing a collision check on the optimized pipeline BIM model; checking whether there is collision interference between pipelines and between pipelines and building structures; inputting the collision check result into the ant colony algorithm as a new constraint condition, repeating steps S22 to S24 until the pipeline BIM model no longer collides, and outputting the final optimized pipeline BIM model; S21, set optimization objectives and constraints, including: Extract the geometric parameters and attribute parameters of the pipeline from the initial pipeline BIM model; convert the extracted geometric parameters into the spatial search dimension and distance calculation parameters in the ant colony algorithm; convert the extracted attribute parameters into the pipeline layout constraint condition parameters in the ant colony algorithm; The pipeline layout optimization objective function is constructed as follows: ; in, Indicates the total length of the pipeline, Represents the total collision point penalty value between pipelines and building structures. Indicates the number of supports and hangers in the pipeline layout plan; , , Respectively represent the weight coefficients of pipeline length, collision point, and number of supports and hangers; According to the design specifications of pipeline layout, set the minimum spacing threshold between pipelines , the minimum spacing threshold between pipelines and building structures , Maximum spacing of pipeline supports and hangers , Maximum length of pipeline cantilever and allowable value of pipeline stress , construct the ant colony algorithm constraints: ; in, represents the minimum distance between pipeline i and pipeline j, represents the minimum distance between pipeline i and building structure k, is the support and hanger spacing of pipeline i, is the cantilever length of pipeline i, is the stress of pipeline i, is a pipeline collection, Assemble for building structures; S22, in the ant colony algorithm, ants search for the optimal path in the solution space and output the optimal pipeline layout solution, including: S221, initialize the ant colony algorithm parameters, including the number of ants m, the maximum number of iterations , pheromone importance factor , heuristic function importance factor , pheromone volatility coefficient and the global pheromone update intensity Q; S222, constructing a fitness function of the ant colony algorithm according to the objective function; S223, constructing a pipeline layout solution space according to the geometric parameters of the pipeline, and randomly generating m initial pipeline layout solutions satisfying the constraint conditions in the pipeline layout solution space as the initial positions of the ants; S224, calculating the next moving position of each ant according to the state transition probability function, and obtaining m new pipeline layout solutions; calculating the objective function value of each new pipeline layout solution according to the fitness function, and verifying whether the new pipeline layout solution satisfies the constraint conditions; S225, for the path (i, j) passed by the kth ant, update the formula according to the local pheromone: , update the pheromone concentration on the corresponding path (i, j); in, , represents the local pheromone increment, represents the length of the path traveled by the kth ant in this iteration; S226, according to the global pheromone update formula: , update the pheromone concentration on the path (i, j) in the entire solution space; where, , represents the global pheromone increment, L* represents the shortest path length of all ants in this iteration; S227, repeating steps S224 to S226, iteratively searching for the optimal pipeline layout solution with the highest fitness function value and satisfying the constraint conditions, and outputting it as the final pipeline layout solution; S4, using the optimal design parameters of the supports and hangers, updates the parameters of the support and hanger component family in the support and hanger BIM model, including: S41, inputting the support and hanger BIM model into the structural finite element analysis software, meshing the support and hanger BIM model, and generating a support and hanger finite element model; S42, extract the support and hanger load conditions, material properties and boundary conditions from the support and hanger BIM model, input the support and hanger finite element model, perform structural statics analysis, and output the stress-strain cloud map and displacement cloud map of the support and hanger components; S43, constructing a mathematical model for topological optimization of support and hanger components based on SIMP interpolation method; S44, converting the stress-strain cloud map and displacement cloud map of the support and hanger member into stress constraint conditions and displacement constraint conditions of the mathematical model of the topology optimization of the support and hanger member; S45, solving the mathematical model of topological optimization of the support and hanger components, outputting the optimal relative density distribution of the support and hanger components, and topologically reconstructing the support and hanger finite element model according to the optimal relative density distribution, and outputting the optimal topological structure and size parameters of the support and hanger components; S46, inputting the optimal topological structure and size parameters of the support and hanger frame components into the support and hanger frame BIM model, updating the parameters of the corresponding support and hanger frame component family in the support and hanger frame BIM model, and generating an optimized support and hanger frame BIM model.

2. The support and hanger design method based on BIM according to claim 1, characterized in that: Fitness function The expression is as follows: ; in represents the pipeline layout solution of the kth ant at the tth iteration The fitness function value of represents the length of the i-th pipeline in the layout solution; Indicates the reference value of the total length of the pipeline; represents the penalty value of the jth collision point in the layout solution; Indicates the reference value of the collision point penalty value; Indicates the number of supports and hangers in the layout solution; Indicates the reference value of the number of supports and hangers; represents the minimum distance between pipeline i and building structure k in the layout solution; It indicates the reference value of the minimum distance between pipelines and building structures; represents the maximum stress value of pipeline i in the layout solution; Indicates the reference value of pipeline stress; They represent the weight coefficients of pipeline length, collision point, number of supports and hangers, minimum spacing and stress factor respectively.

3. The support and hanger design method based on BIM according to claim 2 is characterized in that: State transition probability function The expression is as follows: ; in, represents the probability that the kth ant moves from path i to path j in the tth iteration; represents the pheromone concentration on path (i, j); Represents the heuristic function of path (i, j), and the pipeline length and the number of collision points related; Represents the turning factor of path (i, j), which is related to the turning angle and standard corner related; Represents the height change rate factor of path (i, j), which is related to the height change Standard height difference with pipeline related; Respectively represent the importance coefficients of pheromone, heuristic function, turning factor and height change rate factor; represents the set of optional branch paths at the current location of ant k; represents the pheromone concentration between node i and the allowed access node u in the tth iteration; Represents the heuristic information between node i and the allowed access node u; represents the turning factor from node i to the allowed access node u; Represents the height change rate factor from node i to the allowed access node u; and The calculation formula is: ， ; in, Represents small positive numbers to prevent the denominator from being zero.

4. The support and hanger design method based on BIM according to claim 1, characterized in that: S24, perform collision check on the optimized pipeline BIM model, including: S241, extract the pipeline family, support and hanger family and building structure family in the optimized pipeline BIM model through the collision check function in the BIM software to form a collision check set, and use the collision detection algorithm based on the axis-aligned bounding box AABB to perform geometric collision calculation on any two elements in the collision check set. If there is a geometric collision, output a collision report containing the three-dimensional spatial coordinates of the collision point and the type of the collision element; S242, screening and classifying the collision report, extracting the collision element type information of each collision point, and determining whether the collision type is a collision between pipelines or between pipelines and building structures; S243, if there is a collision between pipelines or between pipelines and building structures, the three-dimensional spatial coordinate position of the corresponding collision point and the collision type information are input into the ant colony algorithm as a new constraint condition, and the new constraint condition is added on the basis of the original pipeline layout constraint condition, and the constraint condition parameters in the ant colony algorithm are updated; S244, taking the updated pipeline geometric parameters, attribute parameters, optimization objective function and constraint conditions as input, returning to step S22, re-executing the ant colony algorithm using the updated model parameters and constraint conditions, and obtaining a new optimal solution for pipeline layout; using the parametric family API based on Revit secondary development to map the optimal solution to the corresponding pipeline family instance in the BIM model, and generating a pipeline BIM model; S245, repeating steps S22 to S24 until the collision check shows that no collisions occur between pipelines or between pipelines and building structures, and outputting the corresponding pipeline BIM model as the final optimized pipeline BIM model.

5. The support and hanger design method based on BIM according to claim 1 is characterized in that: S43, based on SIMP interpolation method, the mathematical model of topology optimization of support and hanger components is constructed, including: S431, set the relative density of each unit of the support frame As a design variable, establish the unit stiffness matrix of the support frame member With relative density SIMP interpolation relationship: ; in, is the relative density design variable, with a value range of [0, 1]; p is the penalty factor, which is usually greater than 1; is the solid material element stiffness matrix; S432, taking the minimization of the total volume of the support and hanger components as the support and hanger optimization goal, establish the support and hanger optimization objective function: ,in is the unit volume of the support and hanger components; S433, Von Mises equivalent stress of support and hanger components in structural static analysis Satisfy the strength constraint condition and use the maximum displacement of the support frame member in the structural static analysis Satisfy the stiffness constraints and establish the constraints of the optimization model: , ,in and are the allowable stress and displacement respectively; S434, combined with the optimization objective function and constraints, a mathematical model for topological optimization of the support and hanger components is established: ; Constraints: 。

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