Steel structure collaborative design method, device and equipment based on BIM (Building Information Modeling) driving

Abstract

The invention relates to the technical field of data processing, and discloses a BIM-driven steel structure collaborative design method, device and equipment. The method comprises the steps of performing parameterization processing on building information data to obtain intelligent BIM data; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data; performing intelligent structure analysis processing on structure information in the collaborative design data to obtain optimized structure data; performing adaptive component design processing on the optimized structure data to obtain customized component data; performing virtual construction simulation processing on the customized component data to obtain construction optimization data; intelligent operation and maintenance processing is carried out on the construction optimization data and the actual construction data, full-life-cycle management data are obtained, and the full-life-cycle management data comprise real-time monitoring data, performance evaluation results and predictive maintenance suggestions. The efficiency and accuracy of steel structure collaborative design based on BIM driving are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a BIM-driven collaborative design method, device and equipment for steel structures. Background Art

[0002] The collaborative design method of steel structure has been widely used in the field of construction engineering. The traditional method mainly relies on two-dimensional drawings and empirical design. With the development of computer technology, three-dimensional modeling and finite element analysis have begun to be introduced into the steel structure design process. In recent years, the rise of building information modeling (BIM) technology has brought new opportunities for steel structure design, enabling various links such as design, analysis, construction and operation and maintenance to be coordinated on the same digital platform. At the same time, the advancement of intelligent algorithms and data analysis technology has also provided new tools for optimizing design and decision-making.

[0003] However, the existing collaborative design methods for steel structures still have some shortcomings. First, there is often an information gap in the data exchange between different disciplines, which makes it difficult to synchronize design changes in a timely manner. Secondly, the structural optimization process lacks sufficient consideration of the feasibility of manufacturing and construction, resulting in frequent adjustments to the design plan during actual implementation. Furthermore, construction simulation and risk warning mostly remain in the static analysis stage, making it difficult to cope with dynamic changes in complex projects. Finally, the full life cycle management of the structure lacks an effective data-driven mechanism, making it difficult to achieve accurate predictive maintenance. These problems limit the overall efficiency and quality of steel structure projects. Summary of the invention

[0004] The present application provides a BIM-driven collaborative design method, device and equipment for steel structures, which are used to improve the efficiency and accuracy of BIM-driven collaborative design of steel structures.

[0005] In the first aspect, the present application provides a BIM-driven collaborative design method for steel structures, the BIM-driven collaborative design method for steel structures comprising: performing parameterized processing on building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data, wherein the collaborative design data includes real-time updated professional data, conflict detection results and design change information; performing intelligent structural analysis processing on the structural information in the collaborative design data to obtain optimized structural data, wherein the The optimized structural data includes stress distribution, deformation and performance indicators; the optimized structural data is subjected to adaptive component design processing to obtain customized component data, wherein the customized component data includes component geometry, internal structure and manufacturing process parameters; the customized component data is subjected to virtual construction simulation processing to obtain construction optimization data, wherein the construction optimization data includes construction sequence, resource allocation plan and risk warning information; the construction optimization data and actual construction data are subjected to intelligent operation and maintenance processing to obtain full life cycle management data, wherein the full life cycle management data includes real-time monitoring data, performance evaluation results and predictive maintenance recommendations.

[0006] In a second aspect, the present application provides a BIM-driven steel structure collaborative design device, the BIM-driven steel structure collaborative design device comprising:

[0007] A processing module, used for performing parameterization processing on the building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details;

[0008] An exchange module, used for performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data, wherein the collaborative design data includes real-time updated professional data, conflict detection results and design change information;

[0009] An analysis module, used for performing intelligent structural analysis on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation and performance index;

[0010] A design module, used for performing adaptive component design processing on the optimized structural data to obtain customized component data, wherein the customized component data includes component geometry, internal structure and manufacturing process parameters;

[0011] A simulation module, used to perform virtual construction simulation processing on the customized component data to obtain construction optimization data, wherein the construction optimization data includes construction sequence, resource allocation plan and risk warning information;

[0012] The operation and maintenance module is used to perform intelligent operation and maintenance processing on the construction optimization data and the actual construction data to obtain full life cycle management data, wherein the full life cycle management data includes real-time monitoring data, performance evaluation results and predictive maintenance recommendations.

[0013] The third aspect of the present application provides a computer device, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate via a bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned BIM-driven collaborative design method for steel structures are performed.

[0014] In the technical solution provided by this application, the building information data is parametrically processed to obtain intelligent BIM data, which makes the design process more flexible, can quickly respond to design changes, and improves design efficiency. Intelligent BIM data contains geometric parameters, material properties, load information and connection details, providing a comprehensive and accurate data basis for subsequent analysis. Secondly, the collaborative design data obtained through multidisciplinary data exchange processing realizes seamless data integration between different professions, greatly reduces information islands and design conflicts, and improves the coordination and accuracy of the design. The integration of real-time updated professional data, conflict detection results and design change information enables the design team to discover and solve problems in a timely manner, reducing rework and delays. Furthermore, the structural information in the collaborative design data is subjected to intelligent structural analysis processing, and the optimized structural data obtained includes stress distribution, deformation and performance indicators, which provides a scientific basis for structural optimization and ensures the safety and economy of the structure. The customized component data obtained through adaptive component design processing, including component geometry, internal structure and manufacturing process parameters, not only optimizes component performance, but also considers manufacturing feasibility and reduces conflicts between design and manufacturing. In addition, the construction optimization data obtained through virtual construction simulation, including construction sequence, resource allocation plan and risk warning information, greatly improves construction efficiency and reduces construction risks. Finally, the full life cycle management data obtained through intelligent operation and maintenance, including real-time monitoring data, performance evaluation results and predictive maintenance recommendations, provides data support for the long-term reliable operation of the structure, realizes predictive maintenance, extends the service life of the structure and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 A schematic diagram of an embodiment of a steel structure collaborative design method driven by BIM in an embodiment of the present application;

[0017] Figure 2 A schematic diagram of an embodiment of a BIM-driven steel structure collaborative design device in an embodiment of the present application;

[0018] Figure 3 It is a schematic diagram of the structure of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application provide a method, device and equipment for collaborative design of steel structures driven by BIM. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the steel structure collaborative design method based on BIM drive includes:

[0021] Step S101: Parameterize the building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details;

[0022] Step S102: performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data, wherein the collaborative design data includes real-time updated professional data, conflict detection results, and design change information;

[0023] Step S103, performing intelligent structural analysis and processing on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation and performance indicators;

[0024] Step S104, performing adaptive component design processing on the optimized structural data to obtain customized component data, wherein the customized component data includes component geometry, internal structure and manufacturing process parameters;

[0025] Step S105: Perform virtual construction simulation processing on the customized component data to obtain construction optimization data, wherein the construction optimization data includes construction sequence, resource allocation plan and risk warning information;

[0026] Step S106: Perform intelligent operation and maintenance processing on the construction optimization data and the actual construction data to obtain full life cycle management data, wherein the full life cycle management data includes real-time monitoring data, performance evaluation results and predictive maintenance recommendations.

[0027] It is understandable that the execution subject of the present application can be a BIM-driven steel structure collaborative design device, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0028] Specifically, the building information data is parametrically processed to obtain intelligent BIM data. It involves converting the original building information into a computable and adjustable parameter set. For example, converting the size of the beam from a fixed value to a function expression related to the span makes the design more flexible. Intelligent BIM data contains geometric parameters, material properties, load information and connection details, which together form the basis of steel structure design. Subsequently, the intelligent BIM data is processed for multidisciplinary data exchange to generate collaborative design data. Standardized data exchange formats, such as IFC (Industry Foundation Classes), are used to achieve data sharing and real-time updates between different disciplines. Through data mapping and synchronization technology, the data of various disciplines such as architecture, structure, and electromechanical are ensured to remain consistent. At the same time, spatial analysis algorithms are used for conflict detection to identify collision problems such as steel structure components and electromechanical pipelines. Design change information is generated through version control and difference analysis technology to record the content of each modification and its scope of influence. Intelligent structural analysis is performed on the structural information in the collaborative design data to obtain optimized structural data. It involves finite element analysis to simplify complex steel structures into computable mesh models. By setting boundary conditions and applying loads, the stress distribution and deformation are calculated. Performance indicators are obtained by comparing the calculation results with the design specification requirements, such as deflection ratio, stress utilization rate, etc.

[0029] Based on the optimized structural data, adaptive component design processing is performed to generate customized component data. Using the topological optimization algorithm, the geometric shape and internal structure of the component are optimized according to the stress path, minimizing the material consumption while ensuring strength. The manufacturing process parameters are optimized by considering factors such as processing equipment capacity and material characteristics to determine the process parameters such as cutting, welding, and surface treatment. Then, the customized component data is processed by virtual construction simulation to obtain construction optimization data. The entire construction process is simulated using discrete event simulation technology. The optimal construction sequence is determined by the optimization algorithm, such as installing the main beam first and then the secondary beam. The resource allocation plan is generated based on the critical path method and resource balancing technology to ensure the rational allocation of manpower, equipment, and materials. Risk warning information is obtained by comparing the simulation results with the safety threshold, such as stability analysis during the lifting process.

[0030] Finally, the construction optimization data and actual construction data are processed through intelligent operation and maintenance to obtain full life cycle management data. The Internet of Things technology is used to collect real-time monitoring data, such as strain and displacement. Through data mining and machine learning algorithms, the trend of structural performance changes is analyzed and the current status is evaluated. Predictive maintenance recommendations are generated based on historical data and prediction models. For example, if fatigue problems may occur at a certain node in the future, maintenance can be arranged in advance.

[0031] For example, in a large steel structure project, the initial building information data contains the basic information of 1,000 steel components. Through parametric processing, these components are associated with 45 key design parameters to form intelligent BIM data. In the multidisciplinary collaboration stage, the system identified 78 conflicts between disciplines and recorded 23 important design changes. During the structural analysis process, the finite element model contained more than 1 million grid elements, and it was calculated that the maximum stress concentration area was located at a certain connection node with a stress value of 235MPa. After the adaptive component design optimization, the material consumption of key components was reduced by 12%, while meeting all strength requirements. Virtual construction simulation determined an optimal construction sequence that can shorten the total construction period by 15 days, and identified 5 potential high-risk construction links. In the operation and maintenance stage, the system predicted 2 structural parts that may need to be strengthened in the next 5 years by analyzing 3 years of monitoring data, providing a basis for preventive maintenance.

[0032] In the embodiment of the present application, the building information data is parametrically processed to obtain intelligent BIM data, which makes the design process more flexible, can quickly respond to design changes, and improves design efficiency. Intelligent BIM data contains geometric parameters, material properties, load information and connection details, providing a comprehensive and accurate data basis for subsequent analysis. Secondly, the collaborative design data obtained through multidisciplinary data exchange processing realizes seamless data integration between different professions, greatly reduces information islands and design conflicts, and improves the coordination and accuracy of the design. The integration of real-time updated professional data, conflict detection results and design change information enables the design team to discover and solve problems in a timely manner, reducing rework and delays. Furthermore, the structural information in the collaborative design data is subjected to intelligent structural analysis processing, and the optimized structural data obtained includes stress distribution, deformation and performance indicators, which provides a scientific basis for structural optimization and ensures the safety and economy of the structure. The customized component data obtained by adaptive component design processing, including component geometry, internal structure and manufacturing process parameters, not only optimizes component performance, but also considers manufacturing feasibility, and reduces the conflict between design and manufacturing. In addition, the construction optimization data obtained through virtual construction simulation, including construction sequence, resource allocation plan and risk warning information, greatly improves construction efficiency and reduces construction risks. Finally, the full life cycle management data obtained through intelligent operation and maintenance, including real-time monitoring data, performance evaluation results and predictive maintenance recommendations, provides data support for the long-term reliable operation of the structure, realizes predictive maintenance, extends the service life of the structure and reduces maintenance costs.

[0033] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0034] (1) Performing geometric analysis on the building information data to obtain initial geometric data, and performing parameterization on the initial geometric data to obtain geometric parameters;

[0035] (2) performing material property analysis on the geometric parameters to obtain material property data, and performing attribute mapping on the material property data to obtain material properties;

[0036] (3) Perform load analysis on geometric parameters and material properties through load calculation algorithms to obtain initial load data, and perform combined optimization processing on the initial load data to obtain load information;

[0037] (4) Perform node identification processing on geometric parameters, material properties and load information to obtain node feature data, and perform connection generation processing on the node feature data through the connection design algorithm to obtain connection details;

[0038] (5) Perform data association processing on geometric parameters, material properties, load information and connection details to obtain associated structural data, and perform intelligent integration processing on the associated structural data to obtain intelligent BIM data.

[0039] Specifically, the building information data is processed by geometric analysis to obtain initial geometric data. This involves extracting the basic geometric features of the building, such as spatial layout, component location and size, etc. Subsequently, the initial geometric data is processed by parameterization to obtain geometric parameters. Parametric processing converts fixed geometric data into an adjustable parameter set, such as converting the length of a beam from a fixed value to a variable related to the span, making the design more flexible. Material property analysis is performed on the geometric parameters to obtain material property data. The physical and mechanical properties of steel, such as elastic modulus, yield strength, and Poisson's ratio, are taken into account. Then, the material property data is processed by attribute mapping to obtain material properties. Attribute mapping associates material properties with specific components to ensure that each component is assigned the correct material properties.

[0040] Subsequently, the load calculation algorithm is used to perform load analysis on the geometric parameters and material properties to obtain the initial load data. The load calculation algorithm takes into account a variety of load types, including dead load, live load, wind load and seismic load. The initial load data is subjected to combined optimization processing to obtain load information. The combined optimization processing takes into account various load combinations to ensure that the structure can still meet the design requirements under the most unfavorable load conditions. After that, the geometric parameters, material properties and load information are subjected to node identification processing to obtain node characteristic data. The node identification processing identifies the key connection points in the structure, such as beam-column connection and column foot connection. The node characteristic data is subjected to connection generation processing through the connection design algorithm to obtain the connection details. The connection design algorithm automatically generates suitable connection methods, such as welding and bolted connections, based on factors such as node force conditions and component size.

[0041] Finally, the geometric parameters, material properties, load information and connection details are processed for data association to obtain the associated structural data. Data association processing integrates all the aforementioned information and establishes the logical relationship between the data. Intelligent integration processing is performed on the associated structural data to obtain intelligent BIM data. Intelligent integration processing further optimizes the data structure and improves the availability and intelligence of the data through data mining and machine learning technology.

[0042] For example, in a large steel structure office building project, the initial building information data contains the basic information of 3,000 steel components. Through geometric analysis and processing, the spatial coordinates, length, cross-sectional dimensions and other information of each component are extracted to form the initial geometric data. Parametric processing converts these data into adjustable parameters, such as setting the standard floor height as variable H and the floor thickness as variable T to form a geometric parameter set. In the material property analysis, Q345 steel is selected, with an elastic modulus E of 206GPa and a yield strength fy of 345MPa. Through attribute mapping, these material properties are associated with each steel component. The load analysis takes into account dead loads (such as dead weight, equipment weight), live loads (such as office area load 5kN / m 2 ), wind load (basic wind pressure 0.35kN / m 2 ) and earthquake action (design basic acceleration 0.15g). After load combination optimization, the controlling combination is 1.2 dead load + 1.4 live load + 1.4 wind load.

[0043] The node identification process identified 150 key nodes, including 80 beam-column connections, 40 column-base connections, and 30 beam-beam connections. The connection design algorithm designed high-strength bolt connections for beam-column connections based on the node stress conditions, with a bolt grade of 10.9 and a diameter of 20 mm; and designed embedded anchor bolt connections for column-base connections with an anchor diameter of 30 mm. The data association process integrated geometric parameters, material properties, load information, and connection details to establish a complete steel structure data set containing 3,000 components and 150 nodes. The intelligent integration process identified 20 typical node types through data mining technology, and established parametric templates for each type, greatly improving the efficiency of subsequent design. The resulting intelligent BIM data not only contains complete steel structure information, but also has the ability of parametric adjustment, automatic update, and intelligent optimization.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] (1) Perform data format conversion on intelligent BIM data to obtain standardized exchange data, and perform multi-professional mapping on the standardized exchange data to obtain multi-professional related data;

[0046] (2) Real-time synchronization processing of multi-professional related data is performed to obtain real-time updated professional data, and the real-time updated professional data is spatially analyzed and processed through a conflict detection algorithm to obtain conflict detection results;

[0047] (3) Perform design change analysis on the conflict detection results to obtain change requirement data, and perform change impact assessment on the change requirement data to obtain design change information;

[0048] (4) Perform data integration processing on the real-time updated professional data, conflict detection results and design change information to obtain integrated collaborative data, and perform data consistency check processing on the integrated collaborative data to obtain collaborative design data.

[0049] Specifically, the intelligent BIM data is processed for data format conversion to obtain standardized exchange data. It involves converting BIM data in a proprietary format into a universal exchange format, such as Industry Foundation Classes (IFC). IFC is an open data model standard used to describe data in the fields of architecture and civil engineering. The conversion process includes the mapping of geometric information, attribute information, and relationship information to ensure the integrity and accuracy of the data. The standardized exchange data is subjected to multi-professional mapping processing to obtain multi-professional related data. Multi-professional mapping processing establishes the relationship between different professional data, such as the spatial relationship between structural components and electromechanical pipelines, and the connection relationship between the building envelope and the structural frame. Semantic mapping technology is used to correspond the terms and concepts of different professions to form a unified data model.

[0050] Next, the multi-professional related data is synchronized in real time to obtain real-time updated professional data. Real-time synchronization uses distributed database technology and version control mechanism to ensure that relevant data can be updated and pushed to other professionals in a timely manner when professionals make design modifications. Incremental update strategy is used in the synchronization process to only transmit changed data to improve synchronization efficiency. Then, the real-time updated professional data is spatially analyzed and processed through the conflict detection algorithm to obtain conflict detection results. The conflict detection algorithm is based on spatial geometry calculation and mainly includes two methods: interference check and gap check. Interference check is used to identify spatial overlaps between different components, and gap check is used to ensure that necessary installation and maintenance space is maintained between components. The algorithm uses octree space partitioning technology to improve the detection efficiency of large-scale models.

[0051] The conflict detection results are analyzed for design changes to obtain change requirement data. This involves conflict classification, prioritization, and solution generation. Similar conflict issues are classified through graph pattern recognition technology, and are prioritized based on the scope and severity of impact. Solution generation uses a rule-based reasoning system to provide possible solutions for each type of conflict based on preset design rules and past experience. Change requirement data is evaluated for change impact to obtain design change information. The change impact assessment uses a graph traversal algorithm to identify all affected related components and systems along the data association relationship, starting from the initial change point. The assessment process takes into account direct and indirect impacts and calculates the cost, duration, and performance impact of the change.

[0052] Finally, the real-time updated professional data, conflict detection results and design change information are integrated to obtain integrated collaborative data. Data integration uses semantic network technology to connect various types of information with semantic relationships to form a complete knowledge graph. Data consistency check is performed on the integrated collaborative data to obtain collaborative design data. Consistency check uses formal methods, such as predicate logic, to verify whether the logical relationship between data is contradictory, ensuring the correctness and integrity of the final collaborative design data.

[0053] For example, in a large steel structure stadium project, the intelligent BIM data contains information on 5,000 steel components, 3,000 electromechanical equipment, and 2,000 building components. The data format conversion process converts the original Revit format data into IFC format, and the size of the converted standardized exchange data is 2.5GB. The multi-disciplinary mapping process establishes 15,000 associations, including 4,000 structure-electromechanical associations, 6,000 structure-architecture associations, and 5,000 electromechanical-architecture associations. In real-time synchronization processing, when the structural engineer modified the cross-sectional size of a main beam from H600x300x12x20 to H700x300x13x24, the system synchronized this change to all relevant disciplines within 0.5 seconds. The conflict detection algorithm completed the spatial analysis of the entire model within 10 minutes and identified 150 potential conflicts, including 30 severe conflicts (direct intersection of components), 70 moderate conflicts (insufficient clearance), and 50 minor conflicts (insufficient reserved space).

[0054] The design change analysis classified these 150 conflicts into 10 typical problems, such as "intersection of steel beams and air conditioning ducts" and "collision between columns and curtain wall columns". The change impact assessment showed that solving these conflicts was expected to require the adjustment of 200 components, affecting the construction period by 7 days and increasing costs by about 500,000 yuan. Data integration processing linked all information into a 4GB integrated collaborative data set. The consistency check found and resolved 25 data inconsistencies, and the collaborative design data finally generated ensured the integrity and consistency of each professional data.

[0055] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0056] (1) Simplifying the structure information in the collaborative design data to obtain simplified structure data, and performing mesh generation on the simplified structure data using a finite element meshing algorithm to obtain structure mesh data;

[0057] (2) performing boundary condition setting processing on the structural grid data to obtain constrained structural data, and performing force analysis processing on the constrained structural data through a structural mechanics analysis algorithm to obtain stress distribution;

[0058] (3) Perform deformation calculation processing on the stress distribution to obtain the deformation amount, and perform structural stability analysis processing on the deformation amount to obtain stability data;

[0059] (4) Performing performance evaluation processing on the stress distribution, deformation and stability data to obtain performance indicators, and performing data integration processing on the stress distribution, deformation and performance indicators to obtain optimized structural data.

[0060] Specifically, the structural information in the collaborative design data is geometrically simplified to obtain simplified structural data. Geometric simplification includes operations such as deleting non-structural components, merging similar nodes, and simplifying complex sections, with the aim of reducing computational complexity without losing key structural features. Subsequently, the simplified structural data is meshed by a finite element meshing algorithm to obtain structural mesh data. The finite element meshing algorithm uses adaptive meshing technology to dynamically adjust the mesh density according to the complexity of the structure, optimizing computational efficiency while ensuring computational accuracy. The structural mesh data is processed by boundary condition setting to obtain constrained structural data. Boundary condition setting includes defining support points, applying external loads, and setting connection relationships between components. Actual engineering conditions need to be considered, such as foundation conditions, wind load distribution, and seismic effects. Then, the constrained structural data is subjected to force analysis by a structural mechanics analysis algorithm to obtain stress distribution. The structural mechanics analysis algorithm is based on the finite element method, solves a large-scale linear equation system, and calculates the displacement of each node and the internal force of each unit.

[0061] The stress distribution is processed by deformation calculation to obtain the deformation. The deformation calculation is based on the node displacement interpolation to obtain the deformation field of the entire structure. Subsequently, the deformation is processed by structural stability analysis to obtain stability data. Stability analysis includes linear buckling analysis and nonlinear large deformation analysis to evaluate the stability performance of the structure under various load conditions. Finally, the stress distribution, deformation and stability data are subjected to performance evaluation to obtain performance indicators. The performance evaluation involves strength verification, stiffness verification, stability verification and other aspects, and various performance indicators are calculated according to the design specifications. Data integration processing is performed on the stress distribution, deformation and performance indicators to obtain optimized structural data. Data integration processing integrates the results of various analyses into the BIM model to provide a basis for subsequent optimization design.

[0062] For example, in a large steel structure exhibition center project, the collaborative design data contained detailed information on 10,000 steel components. Through geometric simplification, complex node connections were simplified to rigid connections, and minor components were merged into major components. Ultimately, simplified structural data was obtained, and the number of components was reduced to 7,500. The finite element meshing algorithm generated structural mesh data of approximately 500,000 tetrahedral units and 800,000 nodes based on the importance and force characteristics of the components. In the boundary condition setting process, 100 foundation support points were defined, and a roof dead load of 2.5 kN / m was applied. 2 , Live load 0.5kN / m 2 , and wind load (basic wind pressure 0.45kN / m 2 ). The structural mechanics analysis algorithm uses a sparse matrix direct solution method. It takes 2 hours to complete the calculation on a 64-core high-performance computer to obtain stress distribution data. The maximum von Mises stress appears at the main truss node, with a value of 235MPa.

[0063] The maximum deflection obtained by deformation calculation is 1 / 350 of the span, which occurs at the center of the roof. The structural stability analysis shows that the critical load factor corresponding to the first buckling mode is 4.2, which meets the requirements of the specification. The performance evaluation process calculates that 98% of the components have a stress ratio of less than 0.8, and the maximum stress ratio is 0.92, which occurs in the bottom chord of the main truss of the roof. In terms of stiffness indicators, the deflection-span ratio of all components is less than 1 / 250, which meets the usability requirements. Data integration processing maps these analysis results back to the BIM model and generates optimized structural data including stress cloud diagrams, deformation animations, and performance reports. These data intuitively show the stress state and performance of the structure, providing a scientific basis for the design team to conduct local optimization and scheme comparison. For example, based on the stress distribution data, 5 stress concentration areas were identified, and the design was optimized by adding stiffeners or adjusting the node structure. At the same time, according to the deformation analysis results, 2 prestressed cables were added in the area with a larger span to effectively control the structural deformation. This intelligent structural analysis method driven by BIM improves the analysis efficiency and accuracy.

[0064] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0065] (1) Performing component division processing on the optimized structural data to obtain initial component data, and performing morphological optimization processing on the initial component data through a topology optimization algorithm to obtain the component geometry;

[0066] (2) performing internal structure generation processing on the geometric shape of the component to obtain preliminary internal structure data, and performing structural optimization processing on the preliminary internal structure data through a stress path analysis algorithm to obtain the internal structure;

[0067] (3) Performing manufacturing feasibility analysis on the geometric shape and internal structure of the component to obtain manufacturability data, and adjusting the parameters of the manufacturability data through a process parameter optimization algorithm to obtain manufacturing process parameters;

[0068] (4) Data association processing is performed on the component geometry, internal structure and manufacturing process parameters to obtain associated component data, and data integration processing is performed on the associated component data to obtain customized component data.

[0069] Specifically, the optimized structural data is processed by component division to obtain initial component data. Component division is based on structural function and force characteristics, and the overall structure is decomposed into independent component units, such as beams, columns, nodes, etc. Subsequently, the initial component data is morphologically optimized by the topology optimization algorithm to obtain the component geometry. The topology optimization algorithm is based on the finite element analysis results. Under the premise of meeting the strength and stiffness requirements, the material in the low stress area is removed through iterative calculation to form a lightweight component shape. The component geometry is processed by internal structure generation to obtain preliminary internal structure data. The internal structure generation takes into account the force characteristics of the component and generates internal support structures such as ribs, partitions, and stiffening ribs. Then, the preliminary internal structure data is structurally optimized by the stress path analysis algorithm to obtain the internal structure. The stress path analysis algorithm optimizes the layout of the internal structure by tracking the direction of the principal stress, ensures that the material distribution is consistent with the force path, and improves the structural efficiency of the component.

[0070] Subsequently, the manufacturing feasibility analysis of the component geometry and internal structure is carried out to obtain the manufacturability data. The manufacturing feasibility analysis considers factors such as processing technology limitations, material properties, and equipment capabilities to evaluate the manufacturability of the component. Next, the parameters of the manufacturability data are adjusted through the process parameter optimization algorithm to obtain the manufacturing process parameters. The process parameter optimization algorithm optimizes the process parameters such as cutting, welding, and surface treatment based on the manufacturing cost model and quality requirements to balance manufacturing efficiency and product quality. Finally, the component geometry, internal structure, and manufacturing process parameters are processed for data association to obtain the associated component data. The data association process establishes the mapping relationship between the component geometry, structural performance, and manufacturing process to form a complete digital model of the component. The associated component data is processed for data integration to obtain customized component data. Data integration integrates all relevant information into the BIM model to provide comprehensive digital guidance for subsequent manufacturing and assembly.

[0071] For example, in a large-span steel structure roof project, the optimized structural data includes a key main truss member. The main truss is divided into components and decomposed into initial component data such as upper chord, lower chord and web. The topology optimization algorithm optimizes the shape of the upper chord. The initial cross-section is a rectangular cross-section of 500mm×300mm. After 50 iterative calculations, a variable cross-section component geometry is finally obtained. The cross-section size of the middle section is optimized to 400mm×250mm, and the end remains the original size, achieving a 15% weight reduction. The internal structure generation process adds longitudinal stiffeners and transverse partitions to the upper chord to form preliminary internal structure data. The stress path analysis algorithm optimizes the arrangement of stiffeners by analyzing the main stress distribution, and adjusts the original uniformly distributed 6 longitudinal stiffeners to increase to 8 in the stress concentration area and reduce to 4 in the non-stress concentration area, so that the internal structure is more in line with the force characteristics.

[0072] The manufacturing feasibility analysis process takes into account the capacity limitations of existing processing equipment, such as the maximum cutting thickness of 30mm and the maximum welding thickness of 25mm. The process parameter optimization algorithm adjusts the welding process accordingly, changing the originally planned one-time full penetration welding to two weldings, with each welding thickness not exceeding 20mm, which not only ensures welding quality but also improves production efficiency. At the same time, the optimization algorithm also calculates the optimal preheating temperature of 120℃ to reduce welding deformation. Data association processing integrates the optimized geometry, internal structure and manufacturing process parameters into a unified data model. For example, the variable cross-section information is associated with the processing technology to ensure that the cutting equipment can accurately process the variable cross-sectional dimensions. The data integration process generates a complete customized component data package containing a 3D geometric model, a bill of materials, a processing process description and an assembly guide. This data package not only contains the component's geometric information, but also includes detailed manufacturing instructions, such as cutting paths, welding sequences, quality control points, etc.

[0073] Through this adaptive component design method, the design of the main truss upper chord not only optimizes the structural performance and reduces the weight, but also fully considers the limitations and requirements of the manufacturing process. The final customized component data provides directly available processing instructions for CNC machining equipment, greatly improving manufacturing accuracy and efficiency. At the same time, this method also provides detailed digital guidance for subsequent assembly and quality control, ensuring quality control of the entire process from design to manufacturing to installation.

[0074] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0075] (1) Performing spatiotemporal relationship analysis on customized component data to obtain component installation sequence data, and adjusting the component installation sequence data using a construction sequence optimization algorithm to obtain a construction sequence;

[0076] (2) Analyze and process the resource demand of the construction sequence to obtain resource demand data, and optimize and allocate the resource demand data through a resource allocation algorithm to obtain a resource allocation plan;

[0077] (3) Perform construction process simulation on the construction sequence and resource allocation plan to obtain construction simulation data, and perform risk analysis on the construction simulation data through risk identification algorithms to obtain risk warning information;

[0078] (4) Integrate and process the construction sequence, resource allocation plan, and risk warning information to obtain comprehensive construction data, and optimize and evaluate the comprehensive construction data to obtain construction optimization data.

[0079] Specifically, the customized component data is analyzed and processed based on the spatiotemporal relationship to obtain the component installation sequence data. The spatiotemporal relationship analysis considers the spatial position relationship between components and the installation timing constraints to generate a preliminary installation sequence. Subsequently, the component installation sequence data is sequentially adjusted through the construction sequence optimization algorithm to obtain the construction sequence. The construction sequence optimization algorithm is based on the critical path method and constraint satisfaction problem solving technology, and considers multiple factors such as structural stability, hoisting difficulty, and construction period requirements to generate the optimal construction sequence. The construction sequence is analyzed and processed based on resource requirements to obtain resource requirement data. The resource requirement analysis includes the quantity and time distribution of various resources such as manpower, equipment, and materials. Then, the resource requirement data is optimized and allocated through the resource allocation algorithm to obtain a resource allocation plan. The resource allocation algorithm uses heuristic algorithms and linear programming methods to balance resource usage and minimize resource conflicts and idleness under the premise of satisfying the construction sequence constraints.

[0080] Subsequently, the construction sequence and resource allocation plan are simulated to obtain construction simulation data. The construction process simulation uses discrete event simulation technology to simulate the dynamic progress of the entire construction process, including details such as component installation, equipment operation, and personnel operation. Then, the construction simulation data is analyzed and processed by the risk identification algorithm to obtain risk warning information. The risk identification algorithm is based on pattern recognition and statistical analysis methods to identify potential safety hazards, quality problems, and schedule delay risks in the construction process. Finally, the construction sequence, resource allocation plan, and risk warning information are integrated to obtain comprehensive construction data. The data integration process associates various types of information with the BIM model to form a complete virtual construction scene. The comprehensive construction data is optimized and evaluated to obtain construction optimization data. The optimization evaluation process uses a multi-objective optimization algorithm to find the best balance between safety, economy, and construction period to generate the final construction plan.

[0081] For example, in a large steel structure stadium roof project, the customized component data included 1,000 main steel components. The spatiotemporal relationship analysis process takes into account the spatial relationship between components and the structural stability requirements, and preliminarily generates component installation sequence data. The construction sequence optimization algorithm optimizes the construction sequence that originally required 120 days to 105 days through 500 iterative calculations, while ensuring the structural stability of each construction stage. The resource demand analysis process concluded that the project required 20 large lifting equipment, 200 skilled workers and 30,000 tons of steel. The resource allocation algorithm smoothed the resource demand curve through a dynamic programming method, reduced resource fluctuations, and finally developed an allocation plan that increased resource utilization by 15%. For example, the demand for large lifting equipment that was originally concentrated in the medium term was dispersed throughout the construction cycle, reducing the idle time of equipment.

[0082] The construction process simulation processing simulated the construction process of 105 days and generated construction simulation data containing 500,000 discrete events. The risk identification algorithm analyzed this data and identified 30 potential high-risk points, including 10 high-altitude operation risks, 15 hoisting collision risks, and 5 temporary structural instability risks. For example, the algorithm found that there were safety hazards caused by excessive wind force during the hoisting of a large truss. It is recommended to increase the frequency of wind speed monitoring at this stage and formulate an emergency plan. The data integration processing integrates the optimized construction sequence, resource allocation plan and risk warning information into the BIM model to create a 4D construction model with a time dimension. The optimization evaluation processing finally obtained a construction optimization plan with a 12% improvement in comprehensive performance by adjusting 30 key parameters, such as the installation sequence of certain components and the arrival time of key equipment.

[0083] This optimization plan not only shortened the construction period, but also improved resource utilization and reduced safety risks. For example, by adjusting the installation sequence of a large roof truss, the need for temporary support was reduced, which not only saved material costs but also reduced the difficulty of construction. At the same time, based on risk warning information, safety monitoring equipment was added in high-risk areas, such as a real-time monitoring system installed in high-altitude working areas above 10 meters, which can promptly detect and deal with potential safety hazards. Through this BIM-driven virtual construction simulation method, the project team was able to comprehensively evaluate and optimize the construction plan before the actual construction began, greatly improving construction efficiency and safety. At the same time, this method also provides precise guidance for on-site management, making the complex steel structure construction process more controllable and efficient.

[0084] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0085] (1) Performing data comparison and processing on the construction optimization data and the actual construction data to obtain construction deviation data, and performing real-time monitoring and processing on the construction deviation data through a sensor network to obtain real-time monitoring data;

[0086] (2) Performing data analysis and processing on the real-time monitoring data to obtain structural performance data, and evaluating and processing the structural performance data through a performance evaluation algorithm to obtain a performance evaluation result;

[0087] (3) Performing historical trend analysis on the performance evaluation results to obtain performance change trend data, and performing predictive analysis on the performance change trend data through a predictive maintenance algorithm to obtain predictive maintenance recommendations;

[0088] (4) Perform data association processing on the real-time monitoring data, performance evaluation results, and predictive maintenance recommendations to obtain associated management data, and perform data integration processing on the associated management data to obtain full life cycle management data.

[0089] Specifically, the construction optimization data and the actual construction data are compared and processed to obtain the construction deviation data. This involves comparing the theoretical model with the actual construction results to identify deviations and errors in the construction process. Subsequently, the construction deviation data is monitored and processed in real time through the sensor network to obtain real-time monitoring data. The sensor network includes strain sensors, displacement sensors, acceleration sensors, etc., which are arranged at key parts of the structure to continuously collect dynamic response data of the structure. Next, the real-time monitoring data is analyzed and processed to obtain structural performance data. The data analysis and processing includes steps such as signal filtering, data normalization, and feature extraction to convert the original sensor data into indicators that can be used to evaluate structural performance. Then, the structural performance data is evaluated and processed through the performance evaluation algorithm to obtain performance evaluation results. The performance evaluation algorithm is based on the structural health monitoring theory, calculates key performance indicators such as stiffness, strength, and stability of the structure, and compares them with the design standards.

[0090] Subsequently, the performance evaluation results are analyzed and processed for historical trends to obtain performance change trend data. Historical trend analysis uses time series analysis methods to identify the changing patterns of structural performance over time, including periodic changes, long-term trends, etc. Next, the performance change trend data is predicted and analyzed by the predictive maintenance algorithm to obtain predictive maintenance recommendations. The predictive maintenance algorithm combines machine learning technology to predict future performance changes based on historical data and give targeted maintenance recommendations. Finally, data association processing is performed on real-time monitoring data, performance evaluation results, and predictive maintenance recommendations to obtain associated management data. Data association processing associates various types of information with component information in the BIM model to establish a mapping relationship between structural performance and spatial position. Data integration processing is performed on the associated management data to obtain full life cycle management data. Data integration processing integrates all information into a unified data platform to provide data support for the full life cycle management of the structure.

[0091] For example, in a large steel structure stadium roof project, after the construction was completed, data comparison and processing were carried out, and it was found that the maximum deviation between the actual installation position and the design position was 50mm, which appeared in the middle of the main truss with a span of 100m. To monitor the impact of this deviation, 200 fiber optic strain sensors and 50 acceleration sensors were installed at key nodes with a sampling frequency of 100Hz, generating about 1.7GB of real-time monitoring data every day. Data analysis and processing denoised and extracted features from these raw data to obtain structural performance data such as hourly deformation and daily average stress level. Based on these data, the performance evaluation algorithm calculated that the actual stiffness of the main truss was 95% of the design value and the maximum stress level was 85% of the design allowable stress. These results show that the overall performance of the structure is good, but there is a slight loss of stiffness.

[0092] The historical trend analysis process analyzed the performance evaluation results for one year and found that the deflection of the roof increased by about 5mm in summer compared to winter, which is consistent with the thermal expansion of the material caused by temperature changes. Based on this trend and combined with the material fatigue model, the predictive maintenance algorithm predicts that fatigue cracks may occur at a key node of the main truss in the next five years, and recommends detailed inspection and preventive reinforcement in the third year. Data association processing associates this information with the component ID in the BIM model, for example, accurately locating the predicted fatigue crack location to specific components and nodes in the BIM model. Data integration processing generates a comprehensive data set of about 50GB containing spatial information, time dimensions, performance indicators and maintenance recommendations. This data set not only records the historical performance of the structure, but also includes future performance predictions and maintenance plans, providing comprehensive data support for the full life cycle management of the structure.

[0093] Through this BIM-driven intelligent operation and maintenance method, the project management team can grasp the health status of the structure in real time, detect potential problems in time, and take preventive measures. For example, based on predictive maintenance recommendations, a detailed inspection of the key nodes of the main truss was carried out in the third year, and tiny cracks were found. Through timely reinforcement, further expansion of the cracks was avoided, extending the service life of the structure. At the same time, this method also provides valuable data and experience for similar projects in the future, which helps to continuously improve the design and construction methods of steel structures.

[0094] The above describes the BIM-driven steel structure collaborative design method in the embodiment of the present application. The following describes the BIM-driven steel structure collaborative design device in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a BIM-driven steel structure collaborative design device includes:

[0095] The processing module 201 is used to perform parameterization processing on the building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details;

[0096] An exchange module 202 is used to perform multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data, wherein the collaborative design data includes real-time updated professional data, conflict detection results and design change information;

[0097] The analysis module 203 is used to perform intelligent structural analysis on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation and performance indicators;

[0098] A design module 204, configured to perform adaptive component design processing on the optimized structural data to obtain customized component data, wherein the customized component data includes component geometry, internal structure and manufacturing process parameters;

[0099] The simulation module 205 is used to perform virtual construction simulation processing on the customized component data to obtain construction optimization data, wherein the construction optimization data includes construction sequence, resource allocation plan and risk warning information;

[0100] The operation and maintenance module 206 is used to perform intelligent operation and maintenance processing on the construction optimization data and the actual construction data to obtain full life cycle management data, wherein the full life cycle management data includes real-time monitoring data, performance evaluation results and predictive maintenance recommendations.

[0101] Through the collaborative cooperation of the above components, the building information data is parametrically processed to obtain intelligent BIM data, making the design process more flexible, able to quickly respond to design changes, and improving design efficiency. Intelligent BIM data contains geometric parameters, material properties, load information and connection details, providing a comprehensive and accurate data basis for subsequent analysis. Secondly, the collaborative design data obtained through multidisciplinary data exchange processing realizes seamless data integration between different disciplines, greatly reduces information islands and design conflicts, and improves the coordination and accuracy of the design. The integration of real-time updated professional data, conflict detection results and design change information enables the design team to discover and solve problems in a timely manner, reducing rework and delays. Furthermore, the structural information in the collaborative design data is subjected to intelligent structural analysis processing, and the optimized structural data obtained includes stress distribution, deformation and performance indicators, which provides a scientific basis for structural optimization and ensures the safety and economy of the structure. The customized component data obtained through adaptive component design processing, including component geometry, internal structure and manufacturing process parameters, not only optimizes component performance, but also considers manufacturing feasibility and reduces conflicts between design and manufacturing. In addition, the construction optimization data obtained through virtual construction simulation, including construction sequence, resource allocation plan and risk warning information, greatly improves construction efficiency and reduces construction risks. Finally, the full life cycle management data obtained through intelligent operation and maintenance, including real-time monitoring data, performance evaluation results and predictive maintenance recommendations, provides data support for the long-term reliable operation of the structure, realizes predictive maintenance, extends the service life of the structure and reduces maintenance costs.

[0102] Based on the same technical concept, the embodiment of the present application also provides an electronic device. Figure 3 As shown, it is a schematic diagram of the structure of the electronic device 300 provided in the embodiment of the present application, including a processor 301, a memory 302, and a bus 303. Among them, the memory 302 is used to store execution instructions, including a memory 3021 and an external memory 3022; the memory 3021 here is also called an internal memory, which is used to temporarily store the operation data in the processor 301, and the data exchanged with the external memory 3022 such as a hard disk. The processor 301 exchanges data with the external memory 3022 through the memory 3021. When the electronic device 300 is running, the processor 301 communicates with the memory 302 through the bus 303.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0104] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0105] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A BIM-driven steel structure collaborative design method, characterized in that: The BIM-driven steel structure collaborative design method includes: Parametrically processing the building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details; Performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data, wherein the collaborative design data includes real-time updated professional data, conflict detection results and design change information; Performing intelligent structural analysis and processing on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation and performance indicators; Performing adaptive component design processing on the optimized structural data to obtain customized component data, wherein the customized component data includes component geometry, internal structure and manufacturing process parameters; Performing virtual construction simulation processing on the customized component data to obtain construction optimization data, wherein the construction optimization data includes construction sequence, resource allocation plan and risk warning information; The construction optimization data and actual construction data are subjected to intelligent operation and maintenance processing to obtain full life cycle management data, wherein the full life cycle management data includes real-time monitoring data, performance evaluation results and predictive maintenance recommendations.

2. The BIM-driven steel structure collaborative design method according to claim 1 is characterized in that: The parameterization of the building information data is performed to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details, including: Performing geometric analysis processing on the building information data to obtain initial geometric data, and performing parameterization processing on the initial geometric data to obtain the geometric parameters; Performing material property analysis processing on the geometric parameters to obtain material property data, and performing property mapping processing on the material property data to obtain the material properties; Performing load analysis processing on the geometric parameters and the material properties through a load calculation algorithm to obtain initial load data, and performing combined optimization processing on the initial load data to obtain the load information; Performing node identification processing on the geometric parameters, the material properties and the load information to obtain node feature data, and performing connection generation processing on the node feature data through a connection design algorithm to obtain the connection details; Data association processing is performed on the geometric parameters, the material properties, the load information and the connection details to obtain associated structural data, and intelligent integration processing is performed on the associated structural data to obtain the intelligent BIM data.

3. The BIM-driven steel structure collaborative design method according to claim 1 is characterized in that: The multidisciplinary data exchange processing is performed on the intelligent BIM data to obtain collaborative design data, wherein the collaborative design data includes real-time updated professional data, conflict detection results and design change information, including: Performing data format conversion processing on the intelligent BIM data to obtain standardized exchange data, and performing multi-professional mapping processing on the standardized exchange data to obtain multi-professional associated data; Performing real-time synchronization processing on the multi-professional related data to obtain real-time updated professional data, and performing spatial analysis processing on the real-time updated professional data through a conflict detection algorithm to obtain a conflict detection result; Performing design change analysis processing on the conflict detection results to obtain change requirement data, and performing change impact assessment processing on the change requirement data to obtain design change information; The real-time updated professional data, the conflict detection results and the design change information are subjected to data integration processing to obtain integrated collaborative data, and the integrated collaborative data is subjected to data consistency check processing to obtain the collaborative design data.

4. The BIM-driven steel structure collaborative design method according to claim 1 is characterized in that: The intelligent structural analysis and processing of the structural information in the collaborative design data is performed to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation and performance indicators, including: Performing geometric simplification processing on the structural information in the collaborative design data to obtain simplified structural data, and performing mesh generation processing on the simplified structural data through a finite element meshing algorithm to obtain structural mesh data; Performing boundary condition setting processing on the structural grid data to obtain constrained structural data, and performing force analysis processing on the constrained structural data through a structural mechanics analysis algorithm to obtain the stress distribution; Performing deformation calculation processing on the stress distribution to obtain the deformation amount, and performing structural stability analysis processing on the deformation amount to obtain stability data; The stress distribution, the deformation and the stability data are subjected to performance evaluation processing to obtain the performance index, and the stress distribution, the deformation and the performance index are subjected to data integration processing to obtain the optimized structure data.

5. The BIM-driven steel structure collaborative design method according to claim 1 is characterized in that: The adaptive component design processing is performed on the optimized structure data to obtain customized component data, wherein the customized component data includes component geometry, internal structure and manufacturing process parameters, including: Performing component division processing on the optimized structural data to obtain initial component data, and performing morphological optimization processing on the initial component data through a topology optimization algorithm to obtain the component geometric shape; Performing internal structure generation processing on the geometric shape of the component to obtain preliminary internal structure data, and performing structural optimization processing on the preliminary internal structure data through a stress path analysis algorithm to obtain the internal structure; Performing manufacturing feasibility analysis on the geometric shape of the component and the internal structure to obtain manufacturability data, and performing parameter adjustment on the manufacturability data through a process parameter optimization algorithm to obtain the manufacturing process parameters; Data association processing is performed on the component geometry, the internal structure and the manufacturing process parameters to obtain associated component data, and data integration processing is performed on the associated component data to obtain the customized component data.

6. The BIM-driven steel structure collaborative design method according to claim 1 is characterized in that: The customized component data is subjected to virtual construction simulation processing to obtain construction optimization data, wherein the construction optimization data includes a construction sequence, a resource allocation plan, and risk warning information, including: Performing spatiotemporal relationship analysis processing on the customized component data to obtain component installation sequence data, and performing sequence adjustment processing on the component installation sequence data through a construction sequence optimization algorithm to obtain the construction sequence; Performing resource demand analysis on the construction sequence to obtain resource demand data, and optimizing and allocating the resource demand data through a resource allocation algorithm to obtain the resource allocation plan; Performing construction process simulation processing on the construction sequence and the resource allocation plan to obtain construction simulation data, and performing risk analysis processing on the construction simulation data through a risk identification algorithm to obtain the risk warning information; The construction sequence, the resource allocation plan and the risk warning information are subjected to data integration processing to obtain comprehensive construction data, and the comprehensive construction data are subjected to optimization evaluation processing to obtain the construction optimization data.

7. The BIM-driven steel structure collaborative design method according to claim 1 is characterized in that: The intelligent operation and maintenance processing of the construction optimization data and the actual construction data is performed to obtain full life cycle management data, wherein the full life cycle management data includes real-time monitoring data, performance evaluation results and predictive maintenance suggestions, including: Performing data comparison processing on the construction optimization data and the actual construction data to obtain construction deviation data, and performing real-time monitoring processing on the construction deviation data through a sensor network to obtain the real-time monitoring data; Performing data analysis and processing on the real-time monitoring data to obtain structural performance data, and evaluating and processing the structural performance data using a performance evaluation algorithm to obtain the performance evaluation result; Performing historical trend analysis on the performance evaluation results to obtain performance change trend data, and performing predictive analysis on the performance change trend data using a predictive maintenance algorithm to obtain the predictive maintenance recommendation; The real-time monitoring data, the performance evaluation results and the predictive maintenance recommendations are subjected to data association processing to obtain associated management data, and the associated management data are subjected to data integration processing to obtain the full life cycle management data.

8. A BIM-driven steel structure collaborative design device, used to implement the BIM-driven steel structure collaborative design method as described in any one of claims 1 to 7, characterized in that: The BIM-driven steel structure collaborative design device includes: A processing module, used for performing parameterization processing on the building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details; An exchange module is used to perform multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data, wherein the collaborative design data includes real-time updated professional data, conflict detection results and design change information; An analysis module, used for performing intelligent structural analysis on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation and performance index; A design module, used for performing adaptive component design processing on the optimized structural data to obtain customized component data, wherein the customized component data includes component geometry, internal structure and manufacturing process parameters; A simulation module, used to perform virtual construction simulation processing on the customized component data to obtain construction optimization data, wherein the construction optimization data includes construction sequence, resource allocation plan and risk warning information; The operation and maintenance module is used to perform intelligent operation and maintenance processing on the construction optimization data and the actual construction data to obtain full life cycle management data, wherein the full life cycle management data includes real-time monitoring data, performance evaluation results and predictive maintenance recommendations.

9. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the BIM-driven collaborative design method for steel structures are performed.

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