BIM-based support hanger intelligent type selection and real-time collaborative design method

Abstract

The invention relates to the technical field of building information models, in particular to a BIM (Building Information Modeling)-based intelligent model selection and real-time collaborative design method for supports and hangers. Comprising the following steps that S1, building, structure and electromechanical multi-professional BIM models and external data are integrated, and a foundation database of support and hanger model selection is constructed; and S2, based on a rule engine and a machine learning algorithm, automatically matching an optimal support hanger type and generating a parameterized model. Through multi-physics field coupling simulation, stress, displacement and other core indexes calculation, collision check and mechanical checking calculation are combined, layout and component specifications are dynamically adjusted, the reliability of the support hanger under the extreme working condition is ensured, the later reworking risk is reduced, and the anti-seismic indexes such as base shear force and interlayer displacement angle and the bearing indexes such as axial stress and bending stress are adopted, so that the reliability of the support hanger is improved. The performance of the support hanger is comprehensively evaluated, the failure problem caused by a single factor is avoided, and the safety and stability of engineering are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and specifically to an intelligent selection and real-time collaborative design method for support and hanger based on BIM. Background Art

[0002] In the field of construction engineering, with the increasing complexity of construction projects, the traditional support and hanger design selection and collaborative working methods face many challenges, and a more efficient, intelligent and collaborative method is needed to address these issues. Specifically, the traditional support and hanger design selection often relies on manual experience, resulting in problems such as inaccurate selection and low efficiency. Moreover, during the multi-disciplinary collaborative design process, there is poor information communication and coordination difficulties among different disciplines, prone to collision conflicts and design changes, leading to construction schedule delays and cost increases. In addition, during the construction and operation and maintenance stages, due to the lack of effective data management and collaborative mechanisms, it is also difficult to achieve efficient construction and precise operation and maintenance of support and hangers. The emergence of BIM technology provides an opportunity to solve these problems. It can integrate multi-disciplinary information such as architecture, structure, and mechanical and electrical, and achieve information sharing and collaboration. Against this background, the intelligent selection and real-time collaborative design method for support and hanger based on BIM emerges as the times require, aiming to utilize BIM technology and related advanced algorithms and tools to achieve intelligent selection, optimization design of support and hangers, and real-time collaboration among multiple roles, improve design quality and efficiency, reduce project costs, and ensure the smooth implementation and later operation and maintenance of the project. In the prior art, the reliability of support and hangers under extreme working conditions is insufficient, they are prone to failure due to a single factor, and the risk of later rework is high, making it difficult to guarantee the engineering safety and stability.

[0003] Based on this, the present invention provides an intelligent selection and real-time collaborative design method for support and hanger based on BIM to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent selection and real-time collaborative design method for support and hanger based on BIM. Through multi-physical field coupling simulation, the present invention calculates core indicators such as stress and displacement, combines collision checking and mechanical calculation, dynamically adjusts the layout and component specifications, ensures the reliability of support and hangers under extreme working conditions, reduces the risk of later rework, and uses seismic indicators such as base shear force and inter-story drift angle and bearing indicators such as axial stress and bending stress to comprehensively evaluate the performance of support and hangers, avoiding failure problems caused by a single factor, and greatly improving the engineering safety and stability.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] The present invention provides an intelligent selection and real-time collaborative design method for support and hanger based on BIM, including the following steps:

[0007] S1: Integrate the multi-disciplinary BIM models of architecture, structure, and MEP, as well as external data, to construct a basic database for hanger selection.

[0008] S2: Based on the rule engine and machine learning algorithms, automatically match the optimal hanger type and generate a parametric model.

[0009] S3: Introduce multi-physics field coupling simulation technology to verify the seismic resistance and bearing capacity of hangers, and combine collision checking and mechanical calculation to dynamically optimize the layout of hangers and the specifications of components.

[0010] S4: Utilize the cloud platform to achieve real-time collaboration among multiple roles and synchronous update of model data.

[0011] S5: Based on the finally determined hanger model, automatically generate construction drawings, material lists, and prefabrication data to support modular construction and operation and maintenance requirements.

[0012] S6: Iteratively update the selection rule library through construction feedback and historical project data.

[0013] The specific steps in S1 are as follows:

[0014] S1.1: Collect the BIM models of architecture, structure, and MEP provided by each professional design team.

[0015] S1.2: Extensively collect external data closely related to hanger selection.

[0016] S1.3: Carefully check and correct the errors and inconsistencies in the BIM model, and at the same time screen and clean the external data to eliminate the invalid or incorrect parts.

[0017] S1.4: Use data conversion tools to uniformly convert the BIM models in different formats and establish the association relationships between various data to complete data integration.

[0018] S1.5: Carefully design the database structure, clarify the data table definitions, field settings, and data relationships.

[0019] S1.6: Enter the cleaned and integrated data into the database and reasonably establish indexes.

[0020] The external data includes equipment operation parameters, material performance parameters, and building code standards.

[0021] The specific steps in S2 are as follows:

[0022] S2.1: Based on building codes, design standards, and engineering experience, clarify the hanger selection rules, covering the rules for corresponding appropriate hanger types for factors such as pipe diameter, medium weight, and seismic grade.

[0023] S2.2: Select a suitable rule engine software and deploy the defined selection rules therein;

[0024] S2.3: Extract data related to the selection of supports and hangers from the basic database as the training set, and perform preprocessing operations such as normalization and encoding;

[0025] S2.4: Select the decision tree machine learning algorithm to train the preprocessed training set, and adjust the algorithm parameters to improve the model performance at the same time;

[0026] S2.5: Input the data related to the supports and hangers to be selected into the rule engine and the machine learning model;

[0027] S2.6: The rule engine and the machine learning model perform matching evaluation according to the input data, and determine the optimal type of supports and hangers based on the score or probability;

[0028] S2.7: Use the parametric function of the BIM software to generate a 3D model of the supports and hangers according to the selection result, and the model parameters can be adjusted as needed.

[0029] In the S2.4, the decision tree machine learning algorithm trains the preprocessed training set, selects the features for constructing the decision tree by calculating the information gain, and the specific formula for the information gain is:

[0030] IG(D,a) = H(D) - H(D|a)

[0031] where, is the information entropy of the dataset D, k is the number of categories in the dataset; C k is the sample set of the k-th category, |C k | and |D| respectively represent the number of samples in the corresponding sets;

[0032] where, is the information entropy of the dataset D under the condition of the feature a, v is the number of values of the feature a; D v is the dataset corresponding to the value v of the feature a; H(D v ) is the information entropy of this subset.

[0033] The specific steps in the S3 are as follows:

[0034] S3.1: Import the parametric model of the supports and hangers and the related building, structural, and mechanical and electrical models into the multi-physics field coupling simulation software, set the boundary and initial conditions of the mechanical, thermal, and dynamic fields as the physical fields, and clarify the coupling relationship of each physical field;

[0035] S3.2: Run the multi-physics field coupling simulation software, calculate the mechanical indexes of stress, strain, and displacement of the supports and hangers under different working conditions, and evaluate whether their seismic and bearing performances meet the standards;

[0036] S3.3: Conduct collision detection using BIM software, mark the collision positions and types between pipe supports and hangers and other models, and simultaneously perform mechanical calculations on the strength and stability based on the stress conditions of the pipe supports and hangers;

[0037] S3.4: Based on the results of simulation calculations, collision detection, and mechanical calculations, formulate strategies for adjusting the layout of pipe supports and hangers, increasing or decreasing the quantity, or replacing the component specifications, and implement them in the BIM model. Then, conduct various inspections and calculations again until the design requirements are met.

[0038] The specific steps in S3.2 are as follows:

[0039] S3.2.1: Input the seismic acceleration spectrum, temperature load, and mechanical vibration parameters into the multi-physics field coupling simulation software;

[0040] S3.2.2: Solve the following core indicators through the finite element method:

[0041] Seismic performance indicators: Base shear force V b = V b (T)·W, equivalent stress σ vm ≤0.8f y , inter-story drift angle where, V b (T) is the design acceleration spectrum value, W is the total gravity load of the system supported by the pipe supports and hangers, f y is the material yield strength, Δu is the relative displacement, and h is the floor height;

[0042] Bearing performance indicators: Axial stress Bending stress where, f is the design tensile / compressive strength of the material, N is the axial force, A is the cross-sectional area, M y is the bending moment, W y is the moment of inertia of the cross-section, and z is the distance from the neutral axis;

[0043] S3.2.3: When the combined stress criterion is satisfied, it is determined to be qualified.

[0044] The specific steps in S4 are as follows:

[0045] S4.1: Select a suitable cloud platform according to the project requirements and complete the configuration work of creating a project space, setting user permissions, and planning the data storage structure;

[0046] S4.2: Upload the optimized BIM model of the pipe supports and hangers and related data to the cloud platform, and reasonably set the data sharing permissions according to the needs of different roles;

[0047] S4.3: Facilitate real-time communication and collaboration among multiple roles such as designers and construction workers by means of the communication tools of chat, comment, and annotation built in the cloud platform, and use the platform version management function to trace model changes;

[0048] S4.4: Rely on the automatic synchronization mechanism of the cloud platform to ensure that the model and data are updated in real time on each user side. When conflicts occur during simultaneous modification by multiple people, use the platform conflict resolution mechanism to handle them properly.

[0049] The specific steps in S5 are as follows:

[0050] S5.1: Set up the construction drawing template in the BIM software according to the building codes and standards, automatically generate construction drawings of floor plans and sectional views containing information such as detailed dimensions and installation positions based on the final support and hanger model, and conduct reviews;

[0051] S5.2: Accurately extract material information from the determined support and hanger model, and organize, classify, and summarize it into a material list;

[0052] S5.3: Obtain the information required for prefabrication processing such as component dimensions, shapes, and processing techniques from the support and hanger model, convert it into a format suitable for prefabrication processing equipment in CNC machining codes, and then output;

[0053] S5.4: Organize the generated construction drawings, material list, and prefabrication processing data into a complete document package, and deliver it to the construction team and operation and maintenance department through the cloud platform or other means.

[0054] The specific steps in S6 are as follows:

[0055] S6.1: Collect information on the installation difficulty, actual stress, and cooperation with other components of the support and hanger feedback by construction workers through on-site investigations, meeting discussions, and report submissions, and record in detail the problems that occur during construction;

[0056] S6.2: Comprehensively collect various types of data related to support and hanger selection in historical projects. After cleaning, use data analysis means to mine useful information and rules from them;

[0057] S6.3: Evaluate the existing support and hanger selection rules based on the construction feedback and the rules summarized from historical project data, and determine the rule clauses that need to be adjusted and optimized;

[0058] S6.4: Modify the rules that need to be optimized, and verify the effectiveness and accuracy of the modified rules using historical data or simulation data.

[0059] S6.5: Regularly update the selection rule library according to new construction feedback and industry development, and do a good job in version management, record the history and reasons of rule modifications for subsequent traceability and query.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] 1. Through multi-physical field coupling simulation, the present invention calculates core indexes such as stress and displacement, combines collision checking and mechanical calculation, dynamically adjusts the layout and component specifications, ensures the reliability of the support and hanger under extreme working conditions, reduces the risk of later rework, and uses seismic indexes such as base shear force and inter-story drift angle and bearing indexes such as axial stress and bending stress to comprehensively evaluate the performance of the support and hanger, avoiding failure problems caused by single factors, and greatly improving the safety and stability of the project.

[0062] 2. By integrating the rule engine and machine learning algorithms, the present invention establishes an intelligent selection mechanism for dynamic optimization, can quickly match the optimal type of support and hanger, reduces human errors, and improves the selection efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is the overall method flowchart of the intelligent support and hanger selection and real-time collaborative design method based on BIM of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0065] Embodiment:

[0066] As Figure 1 shown, this embodiment provides an intelligent support and hanger selection and real-time collaborative design method based on BIM, including the following steps:

[0067] S1: Integrate multi-disciplinary BIM models of architecture, structure, and mechanical and electrical, as well as external data, to construct a basic database for support and hanger selection;

[0068] S2: Based on the rule engine and machine learning algorithms, automatically match the optimal type of support and hanger and generate a parametric model;

[0069] S3: Introduce multi-physical field coupling simulation technology to verify the seismic and bearing performance of the support and hanger, and combine collision checking and mechanical calculation to dynamically optimize the layout and component specifications of the support and hanger;

[0070] S4: Use the cloud platform to achieve real-time collaboration among multiple roles and synchronous update of model data;

[0071] S5: Based on the finally determined support and hanger model, automatically generate construction drawings, material lists, and prefabrication processing data to support modular construction and operation and maintenance requirements;

[0072] S6: Iteratively update the selection rule library through construction feedback and historical project data.

[0073] The specific steps in S1 are as follows:

[0074] S1.1: Collect the BIM models of architecture, structure, and mechanical and electrical provided by each professional design team;

[0075] S1.2: Extensively collect external data closely related to the selection of supports and hangers;

[0076] S1.3: Carefully check and correct the errors and inconsistencies in the BIM model, and at the same time screen and clean the external data to eliminate the invalid or incorrect parts;

[0077] S1.4: Use data conversion tools to uniformly convert the BIM models in different formats, and establish the association relationships between various data to complete data integration;

[0078] S1.5: Carefully design the database structure, clarify the data table definitions, field settings, and data relationships;

[0079] S1.6: Enter the cleaned and integrated data into the database and reasonably establish indexes

[0080] The external data includes equipment operation parameters, material performance parameters, and building code standards.

[0081] The specific steps in S2 are as follows:

[0082] S2.1: Based on building codes, design standards, and engineering experience, clarify the support and hanger selection rules, covering the rules for corresponding appropriate support and hanger types for factors such as pipe diameter, medium weight, and seismic grade;

[0083] S2.2: Select a suitable rule engine software and deploy the defined selection rules therein;

[0084] S2.3: Extract the data related to the selection of supports and hangers from the basic database as the training set, and perform preprocessing operations such as normalization and encoding;

[0085] S2.4: Select the decision tree machine learning algorithm to train the preprocessed training set, and at the same time adjust the algorithm parameters to improve the model performance;

[0086] S2.5: Input the data related to the supports and hangers to be selected into the rule engine and the machine learning model;

[0087] S2.6: The rule engine and machine learning model perform matching evaluation based on the input data, and determine the optimal type of support hanger according to the score or probability.

[0088] S2.7: Utilize the parametric function of BIM software to generate a 3D model of the support hanger according to the selection result, and the model parameters can be adjusted as needed.

[0089] In the above S2.4, the decision tree machine learning algorithm trains the preprocessed training set, and selects the features for constructing the decision tree by calculating the information gain. The specific formula for the information gain is:

[0090] IG(D,a) = H(D) - H(D|a)

[0091] Where, is the information entropy of the dataset D, k is the number of categories in the dataset; C k is the set of samples of the k-th category, |C k | and |D| respectively represent the number of samples in the corresponding sets.

[0092] Where, is the information entropy of the dataset D under the condition of the feature a, v is the number of values of the feature a; D v is the dataset corresponding to the value v of the feature a; H(D v ) is the information entropy of this subset.

[0093] The specific steps in the above S3 are as follows:

[0094] S3.1: Import the parametric model of the support hanger and related building, structural, and mechanical and electrical models into the multi-physics field coupling simulation software, set the boundaries and initial conditions of the mechanical, thermal, and dynamic fields as the physical fields, and clarify the coupling relationship of each physical field.

[0095] S3.2: Run the multi-physics field coupling simulation software, calculate the mechanical indexes of stress, strain, and displacement of the support hanger under different working conditions, and evaluate whether its seismic and bearing performance meets the standards.

[0096] S3.3: Conduct collision detection using BIM software, mark the collision positions and types between the support hanger and other models, and at the same time perform mechanical calculations of strength and stability based on the force conditions of the support hanger.

[0097] S3.4: Based on the simulation calculation, collision detection, and mechanical calculation results, formulate strategies to adjust the layout of the support hanger, increase or decrease the quantity, or replace the component specifications, and implement them in the BIM model. Then, conduct various inspections and calculations again until the design requirements are met.

[0098] The specific steps in the above S3.2 are as follows:

[0099] S3.2.1: Input the earthquake acceleration spectrum, temperature load and mechanical vibration parameters into the multi-physics coupling simulation software;

[0100] S3.2.2: Use the finite element method to solve the following core indicators:

[0101] Seismic performance index: base shear force V b =V b (T)·W, equivalent stress σ vm ≤0.8f y , inter-story displacement angle Among them, V b (T) is the design acceleration spectrum value, W is the total gravity load of the system supported by the support bracket, f y is the material yield strength, Δu is the relative displacement, and h is the layer height;

[0102] Bearing performance index: axial stress Bending stress Where f is the tensile / compressive design strength of the material, N is the axial force, A is the cross-sectional area, M y is the bending moment, W y is the section moment of inertia, z is the neutral axis distance;

[0103] S3.2.3: When the combined stress criterion is satisfied It is judged as qualified.

[0104] The specific steps in S4 are:

[0105] S4.1: Select a suitable cloud platform based on project requirements, and complete the configuration work of creating project space, setting user permissions, and planning data storage structure;

[0106] S4.2: Upload the optimized support and hanger BIM model and related data to the cloud platform, and reasonably set data sharing permissions according to the needs of different roles;

[0107] S4.3: Use the chat, comment, and annotation tools built into the cloud platform to promote real-time communication and collaboration between designers and construction personnel, and use the platform version management function to trace model changes;

[0108] S4.4: Rely on the automatic synchronization mechanism of the cloud platform to ensure that the model and data are updated in real time on each user end. When conflicts arise due to multiple people making changes at the same time, use the platform conflict resolution mechanism to properly handle them.

[0109] The specific steps in S5 are:

[0110] S5.1: Set up the construction drawing template in the BIM software according to building codes and standards, automatically generate construction drawings including floor plans and sectional views with detailed dimensions and installation location information based on the final support and hanger model, and conduct reviews.

[0111] S5.2: Accurately extract material information from the determined support and hanger model, and organize, classify, and summarize it into a material list.

[0112] S5.3: Obtain the information required for prefabrication processing such as component dimensions, shapes, and processing techniques from the support and hanger model, convert it into a format suitable for prefabrication processing equipment in CNC machining codes, and then output.

[0113] S5.4: Organize the generated construction drawings, material list, and prefabrication processing data into a complete document package, and deliver it to the construction team and operation and maintenance department through a cloud platform or other means.

[0114] The specific steps in S6 are as follows:

[0115] S6.1: Collect information on the installation difficulty, actual stress, and coordination with other components of the support and hanger feedback by construction personnel through on-site investigations, meeting discussions, and report submissions, and carefully record the problems that occur during construction.

[0116] S6.2: Comprehensively collect various types of data related to support and hanger selection in historical projects. After cleaning, use data analysis methods to mine useful information and patterns from them.

[0117] S6.3: Evaluate the existing support and hanger selection rules based on construction feedback and the patterns summarized from historical project data, and determine the rule clauses that need to be adjusted and optimized.

[0118] S6.4: Modify the rules that need to be optimized, and verify the effectiveness and accuracy of the modified rules using historical data or simulation data.

[0119] S6.5: Regularly update the selection rule library according to new construction feedback and industry development, and do a good job in version management, recording the modification history and reasons of the rules for subsequent traceability and query.

[0120] Such as Figure 1As shown in the figure, this embodiment provides a BIM-based intelligent support and hanger selection and real-time collaborative design method. The specific method is as follows: First, S1: Integrate the BIM models of multiple disciplines including architecture, structure, and mechanical and electrical, as well as external data, to construct a basic database for support and hanger selection. The specific steps are as follows: S1.1: Collect the BIM models of architecture, structure, and mechanical and electrical provided by each professional design team. S1.2: Extensively collect external data closely related to support and hanger selection. The external data includes equipment operation parameters, material performance parameters, and building code standards. S1.3: Carefully check and correct the errors and inconsistencies in the BIM model, and at the same time screen and clean the external data to eliminate invalid or incorrect parts. S1.4: Use data conversion tools to uniformly convert the BIM models in different formats and establish the association relationships between various data to complete data integration. S1.5: Carefully design the database structure, clarify the data table definitions, field settings, and data relationships. S1.6: Enter the cleaned and integrated data into the database and reasonably establish indexes. S2: Based on the rule engine and machine learning algorithm, automatically match the optimal support and hanger type and generate a parametric model. The specific steps are as follows: S2.1: Define the support and hanger selection rules based on building codes, design standards, and engineering experience, covering the rules for corresponding appropriate support and hanger types for factors such as pipe diameter, medium weight, and seismic grade. S2.2: Select a suitable rule engine software and deploy the defined selection rules therein. S2.3: Extract the data related to support and hanger selection from the basic database as the training set and perform preprocessing operations such as normalization and encoding. S2.4: Select the decision tree machine learning algorithm to train the preprocessed training set, and at the same time adjust the algorithm parameters to improve the model performance. The decision tree machine learning algorithm trains the preprocessed training set, selects the features for constructing the decision tree by calculating the information gain. The specific formula for the information gain is:

[0121] IG(D,a)=H(D)-H(D|a)

[0122] Where, is the information entropy of the dataset D, k is the number of categories in the dataset; C k is the sample set of the k-th category, |C k | and |D| respectively represent the number of samples in the corresponding sets;

[0123] Where, is the information entropy of the dataset D under the condition of the feature a, v is the number of values of the feature a; D v is the dataset corresponding to the value v of the feature a; H(D v) is the information entropy of the subset. S2.5: Input the data related to the hanger to be selected into the rule engine and the machine learning model; S2.6: The rule engine and the machine learning model perform matching evaluation based on the input data, and determine the optimal hanger type according to the score or probability; S2.7: Use the parametric function of the BIM software to generate the 3D model of the hanger according to the selection result, and the model parameters can be adjusted as needed. S3: Introduce the multi-physical field coupling simulation technology to verify the seismic resistance and bearing capacity of the hanger, and combine collision detection and mechanical calculation to dynamically optimize the layout and component specifications of the hanger; the specific steps are as follows: S3.1: Import the parametric model of the hanger and the related building, structure, and mechanical and electrical models into the multi-physical field coupling simulation software, set the mechanical, thermal, and dynamic fields as the boundaries and initial conditions of the physical fields, and clarify the coupling relationship between the physical fields; S3.2: Run the multi-physical field coupling simulation software, calculate the mechanical indexes of stress, strain, and displacement of the hanger under different working conditions, and evaluate whether its seismic resistance and bearing capacity meet the standards; the specific steps are as follows: S3.2.1: Input the seismic acceleration spectrum, temperature load, and mechanical vibration parameters into the multi-physical field coupling simulation software; S3.2.2: Solve the following core indexes by the finite element method: Seismic performance index: Base shear force V b =V b (T)·W, equivalent stress σ vm ≤0.8f y , inter-story drift angle where, V b (T) is the design acceleration spectrum value, W is the total gravity load of the system supported by the hanger, f y is the yield strength of the material, Δu is the relative displacement, h is the floor height; Bearing performance index: Axial stress Bending stress where, f is the design tensile / compressive strength of the material, N is the axial force, A is the cross-sectional area, M y is the bending moment, W y is the moment of inertia of the cross-section, z is the distance of the neutral axis; S3.2.3: When the combined stress criterion It is determined to be qualified at that time. S3.3: Use BIM software to carry out collision checks, mark the collision positions and types between the support and hanger and other models, and at the same time conduct mechanical calculations of strength and stability based on the stress conditions of the support and hanger; S3.4: Based on the simulation calculations, collision check results and mechanical calculation results, formulate strategies to adjust the layout of the support and hanger, increase or decrease the quantity, or replace the component specifications, and implement them in the BIM model. Then conduct various inspections and calculations again until the design requirements are met. S4: Use the cloud platform to achieve real-time collaboration among multiple roles and synchronous update of model data; the specific steps are as follows: S4.1: Select a suitable cloud platform according to project requirements, and complete the configuration work of creating a project space, setting user permissions, and planning the data storage structure; cloud platforms such as BIM360 and Tencent Cloud BIM. S4.2: Upload the optimized support and hanger BIM model and related data to the cloud platform, and reasonably set data sharing permissions according to the needs of different roles; S4.3: Use the built-in communication tools of the cloud platform, such as chat, comments, and annotations, to promote real-time communication and collaboration among multiple roles of designers, constructors, and manufacturers, and use the platform version management function to trace model changes; S4.4: Rely on the automatic synchronization mechanism of the cloud platform to ensure that the model and data are updated in real time on each user terminal. When conflicts occur during simultaneous modification by multiple people, use the platform conflict resolution mechanism to handle them properly. S5: Based on the finally determined support and hanger model, automatically generate construction drawings, material lists, and prefabrication processing data to support modular construction and operation and maintenance requirements; the specific steps are as follows: S5.1: Set up the construction drawing template in the BIM software according to building codes and standards, and automatically generate construction drawings including floor plans and section views with detailed dimensions and installation position information based on the final support and hanger model and conduct reviews; S5.2: Accurately extract material information from the determined support and hanger model. The material information includes component names, specifications, quantities, and materials, and organize, classify, and summarize them into a material list; S5.3: Obtain the information required for prefabrication processing of component dimensions, shapes, and processing techniques from the support and hanger model, convert it into a format suitable for prefabrication processing equipment in CNC machining codes, and then output; S5.4: Organize the generated construction drawings, material lists, and prefabrication processing data into a complete document package and deliver it to the construction team and operation and maintenance department through the cloud platform or other means. S6: Iteratively update the selection rule library through construction feedback and historical project data.The specific steps are as follows: S6.1: Collect information on the installation difficulty, actual stress, and coordination with other components of the support and hanger feedback by construction workers through on-site investigations, meeting discussions, and report submissions, and record in detail the problems that occur during construction; S6.2: Comprehensively collect various types of data related to the selection of support and hangers in historical projects. After cleaning, use data analysis methods to mine useful information and rules from them; S6.3: Evaluate the existing support and hanger selection rules based on construction feedback and the rules summarized from historical project data, and determine the rule clauses that need to be adjusted and optimized; S6.4: Modify the rules that need to be optimized, including adding or deleting rule content, adjusting parameters and weights, and verify the effectiveness and accuracy of the modified rules using historical data or simulation data. S6.5: Regularly update the selection rule library according to new construction feedback and industry development, and do a good job in version management, record the history and reasons for rule modifications for subsequent traceability and query.

[0124] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0125] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate on all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A method for intelligent selection and real-time collaborative design of support and hanger based on BIM, characterized in that, It includes the following steps: S1: Integrate the multi-disciplinary BIM models of architecture, structure, and mechanical & electrical, as well as external data, to construct a basic database for selecting hanger types. S2: Based on the rule engine and machine learning algorithms, automatically match the optimal hanger type and generate a parametric model. S3: Introduce multi-physical field coupling simulation technology to verify the seismic resistance and bearing capacity of hangers, and combine collision detection and mechanical calculation to dynamically optimize the layout of hangers and component specifications. S4: Use the cloud platform to achieve real-time collaboration among multiple roles and synchronous update of model data. S5: Based on the finally determined hanger model, automatically generate construction drawings, material lists, and prefabrication processing data to support modular construction and operation & maintenance requirements. S6: Iteratively update the selection rule library through construction feedback and historical project data.

2. The intelligent selection and real-time collaborative design method of support and hanger based on BIM according to claim 1, characterized in that The specific steps in S1 are as follows: S1.1: Collect the BIM models of architecture, structure, and mechanical & electrical provided by each professional design team. S1.2: Extensively collect external data closely related to hanger selection. S1.3: Carefully check and correct the errors and inconsistencies in the BIM models, and at the same time screen and clean the external data to eliminate the invalid or incorrect parts. S1.4: Use data conversion tools to uniformly convert the BIM models in different formats, and establish the association relationships among various data to complete data integration. S1.5: Carefully design the database structure, clarify the data table definitions, field settings, and data relationships. S1.6: Enter the cleaned and integrated data into the database and reasonably establish indexes.

3. The intelligent selection and real-time collaborative design method of the support and hanger based on BIM according to claim 2, wherein, The external data includes equipment operation parameters, material performance parameters, and building code standards.

4. The intelligent selection and real-time collaborative design method of the support and hanger based on BIM according to claim 1, characterized in that The specific steps in S2 are as follows: S2.1: Based on building codes, design standards, and engineering experience, clarify the hanger selection rules, covering the rules for corresponding appropriate hanger types according to factors such as pipe diameter, medium weight, and seismic grade. S2.2: Select a suitable rule engine software and deploy the defined selection rules therein. S2.3: Extract the data related to hanger selection from the basic database as the training set, and perform preprocessing operations such as normalization and encoding. S2.4: Select the decision tree machine learning algorithm to train the preprocessed training set, and at the same time adjust the algorithm parameters to improve the model performance. S2.5: Input the data related to the hangers to be selected into the rule engine and machine learning model. S2.6: The rule engine and machine learning model perform matching evaluation according to the input data, and determine the optimal hanger type based on the score or probability. S2.7: Use the parametric function of BIM software to generate a 3D model of the hanger according to the selection result, and the model parameters can be adjusted as needed.

5. The intelligent selection and real-time collaborative design method of the support and hanger based on BIM according to claim 4, wherein, In S2.4, when the decision tree machine learning algorithm trains the preprocessed training set, the features for constructing the decision tree are selected by calculating the information gain. The specific formula for the information gain is: IG(D,a)=H(D)-H(D|a) Among them, is the information entropy of the dataset D, and k is the number of categories in the dataset; C k is the set of samples of the k-th category, and |C k | and |D| respectively represent the number of samples in the corresponding sets; Among them, is the information entropy of the data set D under the condition of feature a, v is the number of values of feature a; D v is the data set corresponding to the value v of feature a; H(D v ) is the information entropy of this subset.

6. The intelligent selection and real-time collaborative design method of support and hanger based on BIM according to claim 1, characterized in that The specific steps in S3 are as follows: S3.1: Import the hanger parametric model and related architecture, structure, and mechanical & electrical models into the multi-physical field coupling simulation software, set the boundaries and initial conditions of mechanics, thermal field, and dynamics as the physical fields, and clarify the coupling relationships among the physical fields. S3.2: Run the multi - physical - field coupling simulation software, calculate the mechanical indexes of stress, strain and displacement of the supports and hangers under different working conditions, and evaluate whether their seismic resistance and load - bearing performance meet the standards; S3.3: Use BIM software to conduct collision checks, mark the collision positions and types between the supports and hangers and other models, and meanwhile conduct mechanical calculations of strength and stability according to the force conditions of the supports and hangers; S3.4: Based on the results of simulation calculations, collision checks and mechanical calculations, formulate strategies to adjust the layout of the supports and hangers, increase or decrease the quantity, or replace the component specifications, and implement them in the BIM model. Then conduct various checks and calculations again until the design requirements are met.

7. The intelligent selection and real-time collaborative design method of support and hanger based on BIM according to claim 1, characterized in that, The specific steps in S3.2 are as follows: S3.2.1: Input the seismic acceleration spectrum, temperature load and mechanical vibration parameters into the multi - physical - field coupling simulation software; S3.2.2: Solve the following core indexes by the finite element method: Seismic performance index: Base shear force V b = V b (T)·W, equivalent stress σ vm ≤0.8f y , inter-story drift angle Among them, V b (T) is the design acceleration spectrum value, W is the total gravity load of the system supported by the support and hanger, f y is the material yield strength, Δu is the relative displacement, and h is the story height; Bearing performance index: axial stress Bending stress Among them, f is the design strength of the material in tension / compression, N is the axial force, A is the cross-sectional area, M y is the bending moment, W y is the moment of inertia of the cross-section, and z is the distance from the neutral axis; S3.2.3: It is determined to be qualified when the combined stress criterion is met ​ 8. The intelligent selection and real-time collaborative design method of the support and hanger based on BIM according to claim 1, characterized in that, The specific steps in S4 are as follows: S4.1: Select a suitable cloud platform according to the project requirements, and complete the configuration work of creating a project space, setting user permissions, and planning the data storage structure; S4.2: Upload the optimized BIM model of the supports and hangers and related data to the cloud platform, and reasonably set data sharing permissions according to the needs of different roles; S4.3: Rely on the built - in communication tools of the cloud platform, such as chat, comment, and annotation, to promote real - time communication and collaboration among multiple roles of designers and constructors, and use the platform version management function to trace model changes; S4.4: Rely on the automatic synchronization mechanism of the cloud platform to ensure that the model and data are updated in real time on each user terminal. When conflicts occur during simultaneous modification by multiple people, use the platform conflict resolution mechanism to handle them properly.

9. The intelligent selection and real-time collaborative design method of support and hanger based on BIM according to claim 1, characterized in that, The specific steps in S5 are as follows: S5.1: Set the construction drawing template in the BIM software according to the building codes and standards, and automatically generate construction drawings such as floor plans and sectional views containing detailed dimensions and installation position information based on the final supports and hangers model, and conduct reviews; S5.2: Accurately extract material information from the determined supports and hangers model, and sort, classify and summarize it into a material list; S5.3: Obtain the information required for prefabrication processing such as component dimensions, shapes and processing techniques from the supports and hangers model, convert it into a format suitable for prefabrication processing equipment in CNC machining codes, and then output; S5.4: Organize the generated construction drawings, material lists and prefabrication processing data into a complete document package, and deliver it to the construction team and operation and maintenance department through the cloud platform or other means.

10. The intelligent selection and real-time collaborative design method of the support and hanger based on BIM according to claim 1, wherein, The specific steps in S6 are as follows: S6.1: Collect information on the installation difficulty, actual stress, and cooperation with other components of the supports and hangers feedback by construction workers through on - site investigations, meeting discussions, and report submissions, and record in detail the problems that occur during construction; S6.2: Comprehensively collect various types of data related to the selection of supports and hangers in historical projects. After cleaning, use data analysis means to mine useful information and rules from them; S6.3: Evaluate the existing selection rules of the supports and hangers based on the construction feedback and the rules summarized from historical project data, and determine the rule clauses that need to be adjusted and optimized; S6.4: Modify the rules to be optimized, and verify the effectiveness and accuracy of the modified rules using historical data or simulated data. S6.5: Regularly update the selection rule library according to new construction feedback and industry development, and perform version management well, recording the history and reasons for rule modification for subsequent traceability and query.

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