BIM-driven steel structure collaborative design method, device and equipment

Abstract

This application relates to the field of data processing technology, and discloses a BIM-driven collaborative design method, apparatus, and equipment for steel structures. The method includes: parametric processing of building information data to obtain intelligent BIM data; multidisciplinary data exchange processing of the intelligent BIM data to obtain collaborative design data; intelligent structural analysis processing of the structural information in the collaborative design data to obtain optimized structural data; adaptive component design processing of the optimized structural data to obtain customized component data; virtual construction simulation processing of the customized component data to obtain construction optimization data; and intelligent operation and maintenance processing of the construction optimization data and actual construction data to obtain full lifecycle management data, which includes real-time monitoring data, performance evaluation results, and predictive maintenance recommendations. This application improves the efficiency and accuracy of BIM-driven collaborative design of steel structures.

Description

Technical Field

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

[0002] Collaborative design methods for steel structures have been widely applied in the field of architectural engineering. Traditional methods primarily rely 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 to steel structure design, enabling collaboration among various stages such as design, analysis, construction, and operation and maintenance on the same digital platform. Simultaneously, advancements in intelligent algorithms and data analysis technologies have provided new tools for optimizing design and decision-making.

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

[0004] This application provides a BIM-driven collaborative design method, apparatus, and equipment for steel structures, which improves the efficiency and accuracy of BIM-driven collaborative design of steel structures.

[0005] Firstly, this application provides a BIM-driven collaborative design method for steel structures. The BIM-driven collaborative design method for steel structures includes: parametric processing of 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; and performing intelligent structural analysis processing on the structural information in the collaborative design data to obtain optimized structural data. The optimized structural data includes stress distribution, deformation, and performance indicators. Adaptive component design processing is applied to the optimized structural data to obtain customized component data, which includes component geometry, internal structure, and manufacturing process parameters. Virtual construction simulation processing is performed on the customized component data to obtain construction optimization data, which includes construction sequence, resource allocation plan, and risk warning information. Intelligent operation and maintenance processing is applied to the construction optimization data and actual construction data to obtain full lifecycle management data, which includes real-time monitoring data, performance evaluation results, and predictive maintenance recommendations.

[0006] Secondly, this application provides a BIM-driven collaborative design device for steel structures, the BIM-driven collaborative design device for steel structures comprising:

[0007] The processing module is used to parametrically process building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details;

[0008] The 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;

[0009] The analysis module is used to perform intelligent structural analysis processing on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation amount and performance index;

[0010] The design module is used 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;

[0011] The simulation module 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;

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

[0013] A third aspect of this application provides a computer device in which the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via a bus, and when the machine-readable instructions are executed by the processor, the steps of the BIM-driven collaborative design method for steel structures described above are performed.

[0014] The technical solution provided in this application parametrically processes building information data to obtain intelligent BIM data, making the design process more flexible, enabling rapid response to design changes, and improving design efficiency. Intelligent BIM data includes geometric parameters, material properties, load information, and connection details, providing a comprehensive and accurate data foundation for subsequent analysis. Secondly, collaborative design data obtained through multidisciplinary data exchange processing achieves seamless data integration between different disciplines, significantly reducing information silos and design conflicts, and improving design coordination and accuracy. The integration of real-time updated professional data, conflict detection results, and design change information allows the design team to promptly identify and resolve problems, reducing rework and delays. Furthermore, intelligent structural analysis processing of the structural information in the collaborative design data yields optimized structural data, including stress distribution, deformation, and performance indicators, providing a scientific basis for structural optimization and ensuring structural safety and economy. Customized component data obtained through adaptive component design processing includes component geometry, internal structure, and manufacturing process parameters, optimizing component performance and considering manufacturing feasibility, reducing conflicts between design and manufacturing. Furthermore, the construction optimization data obtained from virtual construction simulation, including construction sequence, resource allocation plans, and risk warning information, significantly improves construction efficiency and reduces construction risks. Finally, the full lifecycle management data obtained from intelligent operation and maintenance processing, including real-time monitoring data, performance evaluation results, and predictive maintenance recommendations, provides data support for the long-term reliable operation of the structure, enables predictive maintenance, extends the structural service life, and reduces maintenance costs. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of an embodiment of the BIM-driven collaborative design method for steel structures in this application.

[0017] Figure 2 This is a schematic diagram of one embodiment of the BIM-driven collaborative design device for steel structures in this application.

[0018] Figure 3 This is a schematic diagram of the structure of the computer device in the embodiments of this application. Detailed Implementation

[0019] This application provides a BIM-driven collaborative design method, apparatus, and device for steel structures. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the BIM-driven collaborative design method for steel structures in this application includes:

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

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

[0023] Step S103: Perform intelligent structural analysis on the structural information in the collaborative design data to obtain optimized structural data, which includes stress distribution, deformation and performance indicators.

[0024] Step S104: 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;

[0025] Step S105: Perform virtual construction simulation processing on the customized component data to obtain construction optimization data, which 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 actual construction data to obtain full life cycle management data, which includes real-time monitoring data, performance evaluation results and predictive maintenance suggestions.

[0027] It is understood that the executing entity of this application can be a BIM-driven collaborative design device for steel structures, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as the executing entity for illustration.

[0028] Specifically, building information data is parametrically processed to obtain intelligent BIM data. This involves transforming raw building information into a set of calculable and adjustable parameters. For example, beam dimensions are converted from fixed values ​​into span-related functional expressions, making the design more flexible. Intelligent BIM data contains geometric parameters, material properties, load information, and connection details, which together form the foundation of steel structure design. Subsequently, multidisciplinary data exchange processing is performed on the intelligent BIM data 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. Data mapping and synchronization technologies ensure data consistency across architecture, structure, MEP, and other disciplines. Simultaneously, spatial analysis algorithms are used for conflict detection, identifying issues such as collisions between steel structure components and MEP pipelines. Design change information is generated through version control and difference analysis technologies, recording the content and impact of each modification. Intelligent structural analysis processing is performed on the structural information in the collaborative design data to obtain optimized structural data. This involves finite element analysis, simplifying complex steel structures into calculable mesh models. By setting boundary conditions and applying loads, the stress distribution and deformation are calculated. Performance indicators, such as deflection ratio and stress utilization rate, are obtained by comparing the calculation results with design specifications.

[0029] Based on optimized structural data, adaptive component design is performed to generate customized component data. A topology optimization algorithm is used to optimize the geometry and internal structure of the components according to stress paths, minimizing material usage while ensuring strength. Manufacturing process parameters are optimized by considering factors such as processing equipment capabilities and material properties, determining parameters for cutting, welding, and surface treatment. Then, virtual construction simulation is performed on the customized component data to obtain optimized construction data. Discrete event simulation technology is used to simulate the entire construction process. Optimal construction sequences are determined through optimization algorithms, such as installing main beams before secondary beams. Resource allocation plans are generated based on the critical path method and resource balance techniques to ensure the rational allocation of manpower, equipment, and materials. Risk warning information is derived by comparing simulation results with safety thresholds, such as stability analysis during hoisting.

[0030] Finally, intelligent operation and maintenance processing is applied to construction optimization data and actual construction data to obtain full lifecycle management data. Real-time monitoring data, such as strain and displacement, is collected using IoT technology. Data mining and machine learning algorithms are used to analyze structural performance trends and assess the current state. Predictive maintenance recommendations are generated based on historical data and predictive models, such as predicting potential fatigue issues at a node in the future and scheduling maintenance in advance.

[0031] For example, in a large steel structure project, the initial building information data contained basic information on 1,000 steel components. Through parametric processing, these components were associated with 45 key design parameters to form intelligent BIM data. During the multidisciplinary collaboration phase, the system identified 78 interdisciplinary conflicts and recorded 23 significant design changes. In the structural analysis, the finite element model contained over 1 million mesh elements, calculating that the maximum stress concentration area was located at a connection node with a stress value of 235 MPa. After adaptive component design optimization, the material usage of key components was reduced by 12% while meeting all strength requirements. Virtual construction simulation determined an optimal construction sequence that could shorten the overall project duration by 15 days and identified five potentially high-risk construction phases. In the operation and maintenance phase, the system analyzed three years of monitoring data and predicted two structural components that might require reinforcement within the next five years, providing a basis for preventative maintenance.

[0032] In this embodiment, intelligent BIM data is obtained by parametric processing of building information data, making the design process more flexible, enabling rapid response to design changes, and improving design efficiency. Intelligent BIM data includes geometric parameters, material properties, load information, and connection details, providing a comprehensive and accurate data foundation for subsequent analysis. Secondly, collaborative design data obtained through multidisciplinary data exchange processing achieves seamless data integration between different disciplines, greatly reducing information silos and design conflicts, and improving design coordination and accuracy. The integration of real-time updated professional data, conflict detection results, and design change information enables the design team to promptly identify and resolve problems, reducing rework and delays. Furthermore, intelligent structural analysis processing of the structural information in the collaborative design data yields optimized structural data including stress distribution, deformation, and performance indicators, providing a scientific basis for structural optimization and ensuring structural safety and economy. Customized component data obtained through adaptive component design processing includes component geometry, internal structure, and manufacturing process parameters, optimizing component performance and considering manufacturing feasibility, reducing conflicts between design and manufacturing. Furthermore, the construction optimization data obtained from virtual construction simulation, including construction sequence, resource allocation plans, and risk warning information, significantly improves construction efficiency and reduces construction risks. Finally, the full lifecycle management data obtained from intelligent operation and maintenance processing, including real-time monitoring data, performance evaluation results, and predictive maintenance recommendations, provides data support for the long-term reliable operation of the structure, enables predictive maintenance, extends the structural service life, and reduces maintenance costs.

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

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

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

[0036] (3) The geometric parameters and material properties are analyzed and processed by the load calculation algorithm to obtain the initial load data, and the initial load data is combined and optimized to obtain the 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 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, geometric analysis is performed on building information data to obtain initial geometric data. This involves extracting basic geometric features of the building, such as spatial layout, component location, and dimensions. Subsequently, the initial geometric data is parametrically processed to obtain geometric parameters. Parametric processing transforms fixed geometric data into an adjustable set of parameters; for example, the length of a beam is changed from a fixed value to a span-related variable, making the design more flexible. Material property analysis is then 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 considered. Then, attribute mapping is performed on the material property data to obtain material properties. Attribute mapping associates material properties with specific components, ensuring that each component is assigned the correct material properties.

[0040] Subsequently, load calculation algorithms are used to analyze and process geometric parameters and material properties to obtain initial load data. These algorithms consider various load types, including dead load, live load, wind load, and seismic load. The initial load data is then combined and optimized to obtain load information. This optimization process considers various load combinations to ensure the structure meets design requirements even under the most unfavorable load conditions. Next, node identification processing is performed on the geometric parameters, material properties, and load information to obtain node feature data. This node identification process identifies key connection points in the structure, such as beam-column connections and column base connections. A connection design algorithm is then used to generate connections from the node feature data, obtaining connection details. Based on factors such as node stress and component dimensions, the connection design algorithm automatically generates suitable connection methods, such as welding or bolted connections.

[0041] Finally, data association processing is performed on geometric parameters, material properties, load information, and connection details to obtain associated structural data. This data association processing integrates all the aforementioned information and establishes logical relationships between the data. Intelligent integration processing is then applied to the associated structural data to obtain intelligent BIM data. This intelligent integration processing further optimizes the data structure and improves data usability and intelligence through data mining and machine learning techniques.

[0042] For example, in a large steel structure office building project, the initial building information data contained basic information on 3,000 steel components. Through geometric analysis, the spatial coordinates, length, and cross-sectional dimensions of each component were extracted to form initial geometric data. Parametric processing converted this data into adjustable parameters, such as setting the standard floor height as variable H and the floor slab thickness as variable T, forming a set of geometric parameters. In the material property analysis, Q345 steel was selected, with an elastic modulus E of 206 GPa and a yield strength fy of 345 MPa. Attribute mapping associated these material properties with each steel component. Load analysis considered dead loads (such as self-weight and equipment weight) and live loads (such as the office area load of 5 kN / m). 2 Wind load (basic wind pressure 0.35kN / m) 2 The load combination was optimized to include seismic load (design basic acceleration 0.15g) and wind load. The controlling combination was 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-to-beam connections. Based on the stress conditions of the nodes, the connection design algorithm designed high-strength bolt connections for beam-column connections (bolt grade 10.9, diameter 20mm) and pre-embedded anchor bolt connections for column base connections (anchor bolt diameter 30mm). Data association processing integrated geometric parameters, material properties, load information, and connection details to establish a complete steel structure dataset containing 3000 components and 150 nodes. Intelligent integration processing, through data mining technology, identified 20 typical node types and created parametric templates for each type, significantly improving the efficiency of subsequent design. The resulting intelligent BIM data not only contains complete steel structure information but also possesses the capabilities for parametric adjustment, automatic updates, and intelligent optimization.

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

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

[0046] (2) Real-time synchronization processing of multi-disciplinary related data is performed to obtain real-time updated professional data, and spatial analysis processing of the real-time updated professional data is performed 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) Data integration processing is performed on the real-time updated professional data, conflict detection results and design change information to obtain integrated collaborative data, and data consistency check processing is performed on the integrated collaborative data to obtain collaborative design data.

[0049] Specifically, intelligent BIM data undergoes data format conversion to obtain standardized exchange data. This involves converting proprietary BIM data into a common exchange format, such as Industry Foundation Classes (IFC). IFC is an open data modeling standard used to describe data in the fields of architecture and civil engineering. The conversion process includes mapping geometric, attribute, and relational information to ensure data integrity and accuracy. Multi-disciplinary mapping processing is then performed on the standardized exchange data to obtain multi-disciplinary related data. This multi-disciplinary mapping process establishes the relationships between data from different disciplines, such as the spatial relationship between structural components and MEP pipelines, and the connection relationship between the building envelope and the structural frame. Semantic mapping technology is used to map the terms and concepts of different disciplines together, forming a unified data model.

[0050] Next, multi-disciplinary related data is synchronized in real time to obtain dynamically updated professional data. Real-time synchronization employs distributed database technology and version control mechanisms to ensure that relevant data is promptly updated and pushed to other disciplines when design modifications are made. An incremental update strategy is used during synchronization, transmitting only changed data to improve efficiency. Then, a conflict detection algorithm is used to perform spatial analysis on the dynamically updated professional data to obtain conflict detection results. The conflict detection algorithm is based on spatial geometric calculations and mainly includes two methods: interference checking and gap checking. Interference checking is used to identify spatial overlap between different components, while gap checking ensures that necessary installation and maintenance space is maintained between components. The algorithm uses octree spatial partitioning technology to improve the detection efficiency of large-scale models.

[0051] The conflict detection results are processed through design change analysis to obtain change request data. This involves conflict classification, prioritization, and solution generation. Graph pattern recognition technology is used to categorize similar conflict issues and prioritize them based on their impact scope and severity. Solution generation employs a rule-based reasoning system, providing possible solutions for each type of conflict based on pre-defined design rules and past experience. The change request data is then processed for change impact assessment to obtain design change information. The change impact assessment uses a graph traversal algorithm, starting from the initial change point and following data relationships to identify all affected related components and systems. The assessment process considers both direct and indirect impacts, calculating the cost, schedule, 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 employs semantic network technology, connecting various types of information through semantic relationships to form a complete knowledge graph. The integrated collaborative data undergoes a data consistency check to obtain collaborative design data. The consistency check uses formal methods, such as predicate logic, to verify whether the logical relationships between the data are contradictory, ensuring the correctness and completeness of the final collaborative design data.

[0053] For example, in a large steel structure stadium project, the intelligent BIM data contained information on 5,000 steel components, 3,000 mechanical and electrical (M&E) devices, and 2,000 building components. The data format conversion process transformed the original Revit format data into IFC format, resulting in a standardized exchange data size of 2.5GB. Multi-disciplinary mapping established 15,000 relationships, including 4,000 structural-M&E relationships, 6,000 structural-architectural relationships, and 5,000 M&E-architectural relationships. In real-time synchronization, when a structural engineer modified the cross-sectional dimensions 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 a spatial analysis of the entire model within 10 minutes, identifying 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 categorized these 150 conflicts into 10 typical issues, such as "steel beams intersecting with air conditioning ducts" and "columns colliding with curtain wall mullions." The impact assessment showed that resolving these conflicts was expected to require adjustments to 200 components, impacting the construction period by 7 days and increasing costs by approximately 500,000 yuan. Data integration processing linked all information into a 4GB integrated collaborative dataset. Consistency checks identified and resolved 25 data inconsistencies, and the final collaborative design data ensured the integrity and consistency of data across all disciplines.

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

[0056] (1) Perform geometric simplification on the structural information in the collaborative design data to obtain simplified structural data, and perform mesh generation on the simplified structural data using the finite element mesh generation algorithm to obtain structural mesh data;

[0057] (2) Set boundary conditions for the structural mesh data to obtain constrained structural data, and perform stress analysis on the constrained structural data using structural mechanics analysis algorithms to obtain stress distribution;

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

[0059] (4) The stress distribution, deformation and stability data are processed for performance evaluation to obtain performance indicators. The stress distribution, deformation and performance indicators are then processed for data integration to obtain optimized structural data.

[0060] Specifically, the structural information in the collaborative design data undergoes geometric simplification to obtain simplified structural data. Geometric simplification includes operations such as deleting non-structural components, merging adjacent nodes, and simplifying complex sections, aiming to reduce computational complexity without sacrificing key structural features. Subsequently, a finite element mesh generation algorithm is used to generate a mesh from the simplified structural data, resulting in structural mesh data. This algorithm employs adaptive meshing technology, dynamically adjusting the mesh density based on structural complexity to optimize computational efficiency while maintaining accuracy. Boundary conditions are then set on the structural mesh data to obtain constrained structural data. Boundary condition settings include defining support points, applying external loads, and setting connection relationships between components. Actual engineering conditions, such as foundation conditions, wind load distribution, and seismic effects, need to be considered. Finally, a structural mechanics analysis algorithm is used to perform stress analysis on the constrained structural data to obtain stress distribution. This algorithm, based on the finite element method, solves a large-scale linear equation system, calculating the displacement of each node and the internal forces of each element.

[0061] The stress distribution is processed by deformation calculation to obtain the deformation amount. The deformation calculation is based on nodal displacement interpolation to obtain the deformation field of the entire structure. Subsequently, structural stability analysis is performed on the deformation amount to obtain stability data. The stability analysis includes linear buckling analysis and nonlinear large deformation analysis to evaluate the stability performance of the structure under various loading conditions. Finally, performance evaluation is performed on the stress distribution, deformation amount, and stability data to obtain performance indicators. The performance evaluation involves multiple aspects such as strength verification, stiffness calculation, and stability verification, and various performance indicators are calculated according to design specifications. The stress distribution, deformation amount, and performance indicators are then integrated to obtain optimized structural data. This data integration process integrates the analysis results into the BIM model, providing 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 reduced to rigid connections, and secondary components were merged into primary components, ultimately resulting in simplified structural data with the number of components reduced to 7,500. The finite element mesh generation algorithm, based on the importance and stress characteristics of the components, generated a structural mesh with approximately 500,000 tetrahedral elements and 800,000 nodes. 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 0.45 kN / m) 2 The structural mechanics analysis algorithm employed the sparse matrix direct solution method, completing the calculations on a 64-core high-performance computer in 2 hours, yielding stress distribution data. The maximum von Mises stress occurred at the main truss node, with a value of 235 MPa.

[0063] The maximum deflection obtained from deformation calculations was 1 / 350 of the span, occurring at the center of the roof. Structural stability analysis showed that the critical load factor corresponding to the first buckling mode was 4.2, meeting the code requirements. Performance evaluation calculations showed that 98% of the members had a stress ratio less than 0.8, with the maximum stress ratio of 0.92 occurring in the bottom chord of the main roof truss. Regarding stiffness, the deflection-to-span ratio of all members was less than 1 / 250, meeting serviceability requirements. Data integration mapping mapped these analysis results back to the BIM model, generating optimized structural data including stress contour maps, deformation animations, and performance reports. This data intuitively demonstrates the structural stress state and performance, providing a scientific basis for the design team to conduct local optimization and scheme comparison. For example, based on stress distribution data, five stress concentration areas were identified, and optimized designs were implemented by adding stiffeners or adjusting node construction. Simultaneously, based on deformation analysis results, two prestressed cables were added in areas with larger spans, effectively controlling structural deformation. This BIM-driven intelligent structural analysis method improves analysis efficiency and accuracy.

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

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

[0066] (2) Perform internal structure generation processing on the component geometry to obtain preliminary internal structure data, and perform structural optimization processing on the preliminary internal structure data through stress path analysis algorithm to obtain the internal structure;

[0067] (3) Perform manufacturing feasibility analysis on the geometry and internal structure of the component to obtain manufacturability data, and then use the process parameter optimization algorithm to adjust the parameters of the manufacturability data to obtain manufacturing process parameters;

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

[0069] Specifically, the optimized structural data is divided into components to obtain initial component data. Component division is based on structural function and stress characteristics, decomposing the overall structure into independent component units, such as beams, columns, and nodes. Subsequently, a topology optimization algorithm is used to optimize the initial component data, resulting in the component geometry. Based on finite element analysis results, the topology optimization algorithm iteratively calculates and removes material from low-stress areas to create lightweight component shapes while meeting strength and stiffness requirements. The component geometry is then processed to generate internal structures, yielding preliminary internal structure data. This internal structure generation considers the component's stress characteristics, generating internal support structures such as ribs, partitions, and stiffeners. Finally, a stress path analysis algorithm is used to optimize the preliminary internal structure data, resulting in the final internal structure. This algorithm tracks the principal stress directions to optimize the internal structure layout, ensuring material distribution aligns with the stress path and improving the structural efficiency of the component.

[0070] Subsequently, a manufacturing feasibility analysis was performed on the component's geometry and internal structure to obtain manufacturability data. The manufacturing feasibility analysis considered factors such as processing technology limitations, material properties, and equipment capabilities to assess the component's manufacturability. Next, the manufacturability data was adjusted using a process parameter optimization algorithm to obtain manufacturing process parameters. Based on a manufacturing cost model and quality requirements, the algorithm optimized process parameters such as cutting, welding, and surface treatment to balance manufacturing efficiency and product quality. Finally, data association processing was performed on the component's geometry, internal structure, and manufacturing process parameters to obtain associated component data. This data association process established a mapping relationship between component geometry, structural performance, and manufacturing processes, forming a complete digital model of the component. The associated component data was then integrated to obtain customized component data. Data integration incorporated all relevant information into the BIM model, providing comprehensive digital guidance for subsequent manufacturing and assembly.

[0071] For example, in a large-span steel roof project, the optimized structural data included a key main truss component. This main truss was divided into initial component data, such as the top chord, bottom chord, and web members. A topology optimization algorithm optimized the shape of the top chord, starting with a rectangular cross-section of 500mm × 300mm. Through 50 iterations, a variable cross-section geometry was obtained, with the middle section optimized to 400mm × 250mm while the ends remained the same, achieving a 15% weight reduction. The internal structure generation process added longitudinal stiffeners and transverse diaphragms to the top chord, forming preliminary internal structural data. A stress path analysis algorithm, by analyzing the principal stress distribution, optimized the arrangement of the stiffeners, increasing the number of evenly distributed longitudinal stiffeners from six to eight in stress concentration areas and reducing them to four in non-stress concentration areas, making the internal structure more consistent with stress characteristics.

[0072] The manufacturing feasibility analysis considered the limitations of existing processing equipment, such as a maximum cutting thickness of 30mm and a maximum welding thickness of 25mm. Based on this, the process parameter optimization algorithm adjusted the welding process, changing the original plan of a single full-penetration weld to two welds, each no thicker than 20mm, ensuring welding quality while improving production efficiency. Simultaneously, the optimization algorithm calculated the optimal preheating temperature to be 120℃ to reduce welding deformation. Data association processing integrated the optimized geometry, internal structure, and manufacturing process parameters into a unified data model. For example, variable cross-section information was associated with the processing technology to ensure that the cutting equipment could accurately process varying cross-sectional dimensions. The data integration process generated a complete, customized component data package containing a 3D geometric model, a bill of materials, processing instructions, and assembly guidelines. This data package not only includes the component's geometric information but also detailed manufacturing guidelines, such as cutting paths, welding sequences, and quality control points.

[0073] This adaptive component design approach not only optimized structural performance and reduced weight in the design of the main truss upper chord, but also fully considered the limitations and requirements of the manufacturing process. The final customized component data provided directly usable machining instructions for CNC equipment, significantly improving manufacturing accuracy and efficiency. Simultaneously, this method also provided detailed digital guidance for subsequent assembly and quality control, ensuring quality control throughout the entire process from design to manufacturing to installation.

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

[0075] (1) Perform spatiotemporal relationship analysis on the customized component data to obtain component installation sequence data, and then use the construction sequence optimization algorithm to adjust the order of the component installation sequence data to obtain the construction sequence;

[0076] (2) Perform resource demand analysis on the construction sequence to obtain resource demand data, and optimize the allocation of resource demand data through resource allocation algorithm to obtain resource allocation plan;

[0077] (3) The construction sequence and resource allocation plan are simulated to obtain construction simulation data, and the construction simulation data are analyzed by risk identification algorithm to obtain risk warning information;

[0078] (4) Data integration and processing of construction sequence, resource allocation plan and risk warning information are carried out to obtain comprehensive construction data, and the comprehensive construction data is optimized and evaluated to obtain construction optimization data.

[0079] Specifically, spatiotemporal relationship analysis is performed on customized component data to obtain component installation sequence data. This analysis considers the spatial relationships between components and installation timing constraints, generating a preliminary installation sequence. Subsequently, a construction sequence optimization algorithm is used to adjust the component installation sequence data to obtain the final construction sequence. This algorithm, based on the critical path method and constraint satisfaction problem-solving techniques, considers multiple factors such as structural stability, hoisting difficulty, and schedule requirements to generate the optimal construction sequence. Resource demand analysis is then performed on the construction sequence to obtain resource demand data. This analysis includes the quantity and temporal distribution of various resources such as manpower, equipment, and materials. Finally, a resource allocation algorithm is used to optimize the allocation of resource demand data, resulting in a resource allocation plan. This algorithm employs heuristics and linear programming to balance resource utilization and minimize resource conflicts and idleness while satisfying construction sequence constraints.

[0080] Subsequently, the construction sequence and resource allocation plan were simulated to obtain construction simulation data. The construction process simulation employed discrete event simulation technology to simulate the dynamic progress of the entire construction process, including details such as component installation, equipment operation, and personnel operations. Next, a risk identification algorithm was used to analyze the construction simulation data, generating risk warning information. This algorithm, based on pattern recognition and statistical analysis methods, identified potential safety hazards, quality problems, and schedule delay risks during construction. Finally, the construction sequence, resource allocation plan, and risk warning information were integrated to obtain comprehensive construction data. This data integration process linked various information elements into the BIM model, forming a complete virtual construction scenario. The comprehensive construction data was then optimized to obtain optimized construction data. This optimization evaluation process used a multi-objective optimization algorithm to find the optimal balance between safety, economy, and schedule, generating 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. Spatiotemporal relationship analysis considered the spatial relationships between components and structural stability requirements, initially generating component installation sequence data. The construction sequence optimization algorithm, through 500 iterations, optimized the original 120-day construction sequence to 105 days, while ensuring structural stability at each construction stage. Resource requirement analysis determined that the project required 20 large cranes, 200 skilled workers, and 30,000 tons of steel. The resource allocation algorithm, using dynamic programming, smoothed the resource demand curve, reducing resource fluctuations and ultimately developing an allocation plan that improved resource utilization by 15%. For instance, the demand for large cranes, originally concentrated in the mid-term, was distributed throughout the entire construction cycle, reducing equipment idle time.

[0082] The construction process simulation processed a 105-day construction period, generating simulation data containing 500,000 discrete events. A risk identification algorithm analyzed this data, identifying 30 potential high-risk points, including 10 risks related to high-altitude operations, 15 risks related to hoisting collisions, and 5 risks related to temporary structural instability. For example, the algorithm detected a safety hazard due to excessive wind during the hoisting of a large truss, recommending increased wind speed monitoring frequency and the development of contingency plans for this phase. Data integration processing incorporated the optimized construction sequence, resource allocation plan, and risk warning information into the BIM model, creating a 4D construction model with a time dimension. Optimization evaluation processing adjusted 30 key parameters, such as the installation sequence of certain components and the arrival time of key equipment, ultimately resulting in a construction optimization scheme with a 12% overall performance improvement.

[0083] This optimization scheme 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 supports was reduced, saving material costs and simplifying construction. Simultaneously, based on risk warning information, safety monitoring equipment was added to high-risk areas, such as a real-time monitoring system installed in areas with work at heights exceeding 10 meters, enabling timely detection and handling of 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 actual construction began, significantly improving construction efficiency and safety. Furthermore, this method provides precise guidance for on-site management, making the complex steel structure construction process more controllable and efficient.

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

[0085] (1) Compare the construction optimization data and the actual construction data to obtain construction deviation data, and use a sensor network to monitor and process the construction deviation data in real time to obtain real-time monitoring data.

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

[0087] (3) Perform historical trend analysis on the performance evaluation results to obtain performance change trend data, and perform predictive analysis on the performance change trend data through predictive maintenance algorithm to obtain predictive maintenance suggestions;

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

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

[0090] Subsequently, historical trend analysis was performed on the performance evaluation results to obtain performance change trend data. The historical trend analysis employed time series analysis to identify patterns in structural performance changes over time, including periodic variations and long-term trends. Next, predictive maintenance algorithms were used to predictively analyze the performance change trend data, yielding predictive maintenance recommendations. These algorithms, combined with machine learning techniques, predict future performance changes based on historical data and provide targeted maintenance suggestions. Finally, real-time monitoring data, performance evaluation results, and predictive maintenance recommendations were correlated to obtain associated management data. This data correlation process linked various information types with component information in the BIM model, establishing a mapping relationship between structural performance and spatial location. The associated management data was then integrated to obtain full lifecycle management data. This integration process consolidated all information into a unified data platform, providing data support for the full lifecycle management of the structure.

[0091] For example, in a large steel structure stadium roof project, data comparison after construction revealed a maximum deviation of 50mm between the actual installation location and the design location, occurring in the middle of the 100m span main truss. To monitor the impact of this deviation, 200 fiber optic strain sensors and 50 accelerometers were installed at key nodes, with a sampling frequency of 100Hz, generating approximately 1.7GB of real-time monitoring data daily. Data analysis and processing denoised and extracted features from this raw data to obtain structural performance data such as hourly deformation and daily average stress level. Based on this 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 indicate that the overall structural performance is good, but there is a slight loss of stiffness.

[0092] Historical trend analysis, analyzing one year's performance evaluation results, revealed that roof deflection increased by approximately 5 mm in summer compared to winter, consistent with material thermal expansion due to temperature changes. Based on this trend and combined with a material fatigue model, the predictive maintenance algorithm predicted that fatigue cracks might appear at a critical node of the main truss within the next five years, recommending a detailed inspection and preventative reinforcement in the third year. Data association processing linked this information with component IDs in the BIM model; for example, it precisely located the predicted fatigue crack location to specific components and nodes in the BIM model. Data integration processing generated a comprehensive dataset of approximately 50 GB, containing spatial information, temporal dimensions, performance indicators, and maintenance recommendations. This dataset not only records the structure's historical performance but also includes future performance predictions and maintenance plans, providing comprehensive data support for the structure's full lifecycle management.

[0093] This BIM-driven intelligent operation and maintenance approach allows project management teams to monitor the structure's health status in real time, promptly identify potential problems, and take preventative measures. For example, based on predictive maintenance recommendations, a detailed inspection of critical nodes in the main truss was conducted in the third year, revealing the initiation of minute cracks. Timely reinforcement prevented further crack propagation and extended the structure's lifespan. Furthermore, this method provides valuable data and experience for future similar projects, contributing to the continuous improvement of steel structure design and construction methods.

[0094] The BIM-driven collaborative design method for steel structures in this application has been described above. The BIM-driven collaborative design device for steel structures in this application is described below. Please refer to [link to relevant documentation]. Figure 2 One embodiment of the BIM-driven collaborative design device for steel structures in this application includes:

[0095] The processing module 201 is used to perform parametric 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;

[0096] The 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 processing on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation amount and performance index;

[0098] Design module 204 is used 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 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 suggestions.

[0101] Through the collaborative efforts of the aforementioned components, building information data is parametrically processed to obtain intelligent BIM data, making the design process more flexible, enabling rapid response to design changes, and improving design efficiency. Intelligent BIM data includes geometric parameters, material properties, load information, and connection details, providing a comprehensive and accurate data foundation for subsequent analysis. Secondly, collaborative design data obtained through multidisciplinary data exchange processing achieves seamless data integration between different disciplines, significantly reducing information silos and design conflicts, and improving design coordination and accuracy. The integration of real-time updated professional data, conflict detection results, and design change information allows the design team to promptly identify and resolve problems, reducing rework and delays. Furthermore, intelligent structural analysis processing of the structural information in the collaborative design data yields optimized structural data, including stress distribution, deformation, and performance indicators, providing a scientific basis for structural optimization and ensuring structural safety and economy. Customized component data obtained through adaptive component design processing includes component geometry, internal structure, and manufacturing process parameters, optimizing component performance and considering manufacturing feasibility, reducing conflicts between design and manufacturing. Furthermore, the construction optimization data obtained from virtual construction simulation, including construction sequence, resource allocation plans, and risk warning information, significantly improves construction efficiency and reduces construction risks. Finally, the full lifecycle management data obtained from intelligent operation and maintenance processing, including real-time monitoring data, performance evaluation results, and predictive maintenance recommendations, provides data support for the long-term reliable operation of the structure, enables predictive maintenance, extends the structural service life, and reduces maintenance costs.

[0102] Based on the same technical concept, embodiments of this application also provide an electronic device. (Refer to...) Figure 3 The diagram shown is a structural schematic of an electronic device 300 provided in an embodiment of this application, including a processor 301, a memory 302, and a bus 303. The memory 302 is used to store execution instructions and includes a main memory 3021 and an external memory 3022. The main memory 3021, also called internal memory, is used to temporarily store computational data in the processor 301 and data exchanged with external memory 3022 such as a hard disk. The processor 301 exchanges data with the external memory 3022 through the main memory 3021. When the electronic device 300 is running, the processor 301 and the memory 302 communicate through the bus 303.

[0103] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0104] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A BIM-driven collaborative design method for steel structures, characterized in that, The BIM-driven collaborative design method for steel structures includes: parametric processing of building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details; The intelligent BIM data is processed through multidisciplinary data exchange to obtain collaborative design data, which includes real-time updated professional data, conflict detection results, and design change information. The structural information in the collaborative design data is subjected to intelligent structural analysis and processing to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation amount and performance index; 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 processed by virtual construction simulation to obtain construction optimization data, which includes construction sequence, resource allocation plan and risk warning information. The construction optimization data and actual construction data are processed intelligently to obtain full life cycle management data, which includes real-time monitoring data, performance evaluation results and predictive maintenance suggestions. The adaptive component design process of the optimized structural data is used 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 geometry; The geometry of the component is processed to generate an internal structure, resulting in preliminary internal structure data. Then, the preliminary internal structure data is optimized using a stress path analysis algorithm to obtain the final internal structure. The manufacturing feasibility analysis of the component geometry and internal structure is performed to obtain manufacturability data, and the manufacturability data is then adjusted using a process parameter optimization algorithm to obtain the manufacturing process parameters. The geometry, internal structure, and manufacturing process parameters of the component are correlated to obtain correlated component data. The correlated component data is then integrated to obtain the customized component data.

2. The BIM-driven collaborative design method for steel structures according to claim 1, characterized in that, The process of parametrically processing building information data to obtain intelligent BIM data includes geometric parameters, material properties, load information, and connection details. This includes: performing geometric analysis on the building information data to obtain initial geometric data, and parametrically processing the initial geometric data to obtain the geometric parameters. The geometric parameters are subjected to material property analysis to obtain material property data, and the material property data is subjected to attribute mapping to obtain the material properties. The geometric parameters and material properties are analyzed and processed by a load calculation algorithm to obtain initial load data. The initial load data is then combined and optimized to obtain the load information. The geometric parameters, material properties, and load information are subjected to node identification processing to obtain node feature data. Then, the node feature data is processed by a connection design algorithm to generate connection details. The geometric parameters, material properties, load information, and connection details are correlated to obtain correlated structural data. The correlated structural data is then intelligently integrated to obtain intelligent BIM data.

3. The BIM-driven collaborative design method for steel structures according to claim 1, characterized in that, The process of performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data includes real-time updated professional data, conflict detection results, and design change information. This includes: performing data format conversion processing on the intelligent BIM data to obtain standardized exchange data, and performing multidisciplinary mapping processing on the standardized exchange data to obtain multidisciplinary related data. The multi-disciplinary related data is synchronized in real time to obtain real-time updated professional data, and spatial analysis is performed on the real-time updated professional data through a conflict detection algorithm to obtain conflict detection results. The conflict detection results are subjected to design change analysis to obtain change requirement data, and the change requirement data is subjected to change impact assessment to obtain design change information. The real-time updated professional data, the conflict detection results, and the design change information are integrated and processed to obtain integrated collaborative data. The integrated collaborative data is then subjected to a data consistency check to obtain the collaborative design data.

4. The BIM-driven collaborative design method for steel structures according to claim 1, characterized in that, The intelligent structural analysis and processing of the structural information in the collaborative design data to obtain optimized structural data includes stress distribution, deformation and performance indicators. This includes: 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 using a finite element mesh generation algorithm to obtain structural mesh data. The structural mesh data is processed by setting boundary conditions to obtain constrained structural data, and the constrained structural data is then processed by a structural mechanics analysis algorithm to obtain the stress distribution. The stress distribution is subjected to deformation calculation to obtain the deformation amount, and the deformation amount is subjected to structural stability analysis to obtain stability data. The stress distribution, deformation, and stability data are subjected to performance evaluation processing to obtain the performance index. The stress distribution, deformation, and performance index are then integrated to obtain the optimized structural data.

5. The BIM-driven collaborative design method for steel structures according to claim 1, characterized in that, The process of performing virtual construction simulation on the customized component data to obtain construction optimization data includes construction sequence, resource allocation plan and risk warning information. This includes: performing spatiotemporal relationship analysis on the customized component data to obtain component installation sequence data, and adjusting the order of the component installation sequence data through a construction sequence optimization algorithm to obtain the construction sequence. The construction sequence is subjected to resource demand analysis to obtain resource demand data, and the resource allocation data is optimized and allocated using a resource allocation algorithm to obtain the resource allocation plan. The construction process is simulated based on the construction sequence and the resource allocation plan to obtain construction simulation data. The construction simulation data is then analyzed using a risk identification algorithm to obtain the risk warning information. The construction sequence, the resource allocation plan, and the risk warning information are integrated and processed to obtain comprehensive construction data. The comprehensive construction data is then optimized and evaluated to obtain optimized construction data.

6. The BIM-driven collaborative design method for steel structures according to claim 1, characterized in that, The intelligent operation and maintenance processing of the construction optimization data and actual construction data yields full lifecycle management data, which includes real-time monitoring data, performance evaluation results, and predictive maintenance suggestions. This includes: comparing the construction optimization data and the actual construction data to obtain construction deviation data, and then monitoring the construction deviation data in real time through a sensor network to obtain the real-time monitoring data. The real-time monitoring data is analyzed and processed to obtain structural performance data, and the structural performance data is evaluated and processed using a performance evaluation algorithm to obtain the performance evaluation result. The performance evaluation results are subjected to historical trend analysis to obtain performance change trend data, and the performance change trend data is then subjected to predictive analysis using a predictive maintenance algorithm to obtain the predictive maintenance recommendations. The real-time monitoring data, the performance evaluation results, and the predictive maintenance suggestions are correlated to obtain correlated management data, and the correlated management data is integrated to obtain the full lifecycle management data.

7. A BIM-driven collaborative design device for steel structures, used to implement the BIM-driven collaborative design method for steel structures as described in any one of claims 1 to 6, characterized in that, The BIM-driven collaborative design device for steel structures includes: a processing module for parametric processing of building information data to obtain intelligent BIM data, wherein the intelligent BIM data includes geometric parameters, material properties, load information and connection details; The 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; The analysis module is used to perform intelligent structural analysis processing on the structural information in the collaborative design data to obtain optimized structural data, wherein the optimized structural data includes stress distribution, deformation amount and performance index; The design module is used 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; The simulation module 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; The operation and maintenance module is used to perform intelligent operation and maintenance processing on the construction optimization data and actual construction data to obtain full life cycle management data, which includes real-time monitoring data, performance evaluation results and predictive maintenance suggestions.

8. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the BIM-driven collaborative design method for steel structures as described in any one of claims 1 to 6.

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