Intelligent modeling tool for building structure and MEP system and working method of intelligent modeling tool

Abstract

The invention discloses an intelligent modeling tool for a building structure and an MEP system and a working method of the intelligent modeling tool. The tool comprises an information extraction and storage module, an intelligent layout and optimization module, a modeling and simulation module, a model output and application module and an NLP and user interaction module. The invention belongs to the technical field of digitization and informatization management, and particularly provides an intelligent modeling tool for a building structure and an MEP system, which realizes full-process intelligentization from building drawing input to BIM model output through cooperative work of all functional modules and is used for a building structure and an MEP system.

Description

Technical Field

[0001] The present invention belongs to the technical field of digitalization and information management, and specifically relates to an intelligent modeling tool for building structures and MEP systems and a working method thereof. Background Art

[0002] With the continuous development of the construction industry, the design and construction of building structures and MEP systems have become more and more complex. Traditional Building Information Modeling (BIM) technology has been widely used in the design, construction and management of construction projects. However, the existing BIM technology still has some shortcomings when dealing with the integration of complex building structures and MEP systems:

[0003] First, existing BIM tools lack sufficient intelligent means in the process of integrating building structures and MEP systems. These tools often cannot automatically identify and resolve conflicts between structures and MEP systems, resulting in more rework and design modifications during the construction phase, increasing project time and cost.

[0004] Secondly, in the existing technology, the modeling of building structure and MEP system is often carried out independently, lacking a unified intelligent modeling method. This independence leads to low efficiency of collaborative work between disciplines and untimely information transmission, further increasing the difficulty of project management.

[0005] In addition, current BIM tools are insufficient in simulating and optimizing complex building structures and MEP systems. When faced with complex building designs and highly integrated MEP systems, existing tools lack sufficient intelligent means to perform global optimization and dynamic adjustments, making it difficult to meet the high standards of modern construction projects.

[0006] In summary, the existing technologies have problems such as insufficient intelligence, coordination difficulties, and insufficient simulation optimization capabilities in the integrated modeling of building structures and MEP systems, which cannot fully meet the needs of modern construction projects for efficient design and construction management. Therefore, there is an urgent need for an intelligent modeling method and its implementation tools that can solve the above problems to improve the efficiency and quality of the integration of building structures and MEP systems. Summary of the invention

[0007] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an intelligent modeling tool for building structures and MEP systems and a working method thereof, so as to at least partially solve the problems raised in the above background technology.

[0008] The present invention provides the following technical solution: An intelligent modeling tool for building structure and MEP system proposed by the present invention comprises:

[0009] Information extraction and storage module, used to achieve efficient and accurate extraction and storage of digital information from architectural drawings;

[0010] Intelligent layout and optimization module, used to generate building layout and MEP system layout plans that meet design specifications and optimization goals;

[0011] Modeling and simulation module, used to convert the optimized layout into a 3D model and perform simulation verification to ensure the feasibility of the design;

[0012] Model output and application module, used to realize the final output of the model and interaction with users;

[0013] The NLP (natural language processing) and user interaction module is used to achieve natural language interaction with users, automatically generate design suggestions, and provide intelligent technical support and feedback through ChatGPT technology.

[0014] Furthermore, the information extraction and storage module includes:

[0015] a. Input interface:

[0016] The input interface supports and is compatible with the input of different types of architectural design files, such as DWG, PDF, IFC, DGN, DXF, etc. It supports emerging file formats through plug-ins or extension mechanisms, and converts input DWG or other format drawings into PDF or PNG formats;

[0017] The input interface integrates a pre-processing unit, which has the functions of identifying the input file, converting drawings, correcting drawings, filtering noise, etc., to ensure the clarity and accuracy of the input drawings;

[0018] b. Information extraction engine:

[0019] ODA SDK parsing: By integrating ODA SDK, the system can directly read a wide range of architectural design file formats, such as DWG, DXF, DGN, etc., and can not only accurately parse the graphic data in these files, but also extract the detailed architectural information contained therein, such as geometric structure, hierarchical relationship, annotations, etc.;

[0020] Image recognition module: uses deep learning technology to identify building elements (such as walls, columns, beams, doors and windows, etc.) and specific components of MEP (mechanical, electrical, and piping) systems in drawings;

[0021] Text and symbol parser: can accurately extract notes, identifiers, dimensioning and other information on drawings and convert them into digital data;

[0022] Data verification mechanism: Verify the accuracy of extracted data by comparing it with pre-defined building specifications to ensure the reliability of subsequent processing;

[0023] c. Data storage unit:

[0024] Structured database: stores information about building structures and MEP systems, using a combination of relational databases (MySQL) and NoSQL databases (MongoDB) to optimize data query and retrieval efficiency;

[0025] Metadata management: records metadata during the data extraction process (data source, extraction time, extraction accuracy, etc.) and supports data version control and traceability.

[0026] Furthermore, the intelligent layout and optimization module includes:

[0027] a. Rule base:

[0028] Building regulations module: covers national and regional building regulations, industry standards, environmental protection requirements, etc., and supports dynamic updates to adapt to changes in regulations;

[0029] MEP layout rule library: stores MEP layout rules for different types of buildings, covering pipeline layout, cable routing, ventilation system, etc.;

[0030] Custom rule interface: allows users to add or modify rules according to specific project requirements, supports rule priority setting and conflict detection;

[0031] b.Layout generation engine:

[0032] Preliminary layout generator: Based on the rule base and extracted data, it uses heuristic algorithms to quickly generate preliminary layouts, covering structural arrangement and MEP system configuration;

[0033] Clash Detection Module: After the initial layout is generated, clash detection (collision between building structure and MEP system) is performed immediately and intelligent correction suggestions are provided;

[0034] c. Optimization algorithm module:

[0035] Multi-objective optimizer: Combine genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm, etc. to perform multi-objective optimization, including maximizing space utilization, minimizing material cost, minimizing construction difficulty, etc.;

[0036] Constraint processing unit: handles various constraints encountered during the optimization process, such as space constraints, structural strength requirements, channel priorities of MEP systems, etc.

[0037] Feedback loop mechanism: Combine simulation results and user feedback to continuously iterate and optimize layout solutions.

[0038] Furthermore, the modeling and simulation module includes:

[0039] a. 3D modeling engine:

[0040] Model generation core: Based on BIM (Building Information Modeling) technology, generate 3D models that meet industry standards (supporting the formats of BIM software such as Revit and Navisworks);

[0041] Component library interface: call building components and MEP equipment from the predefined component library and make parameterized adjustments according to specific project requirements;

[0042] LOD (level of detail) management: supports model generation with different levels of detail, and flexibly adjusts model accuracy from concept design to construction drawings;

[0043] b.Simulation Engine:

[0044] Structural stability simulation: Based on finite element analysis, stress and strain analysis of the building structure is performed to ensure its stability and safety;

[0045] MEP system simulation: Simulate pipeline fluid mechanics, electrical load, ventilation efficiency, etc. to ensure the rationality of system design;

[0046] Collision detection and repair module: performs collision detection between building structure and MEP system, and provides automatic repair suggestions to avoid problems during construction;

[0047] c.Simulation analysis report generator:

[0048] Automatic report generator: Generates detailed simulation analysis reports, including various charts, data statistics, key result summaries, etc., and supports export in multiple formats (PDF, Excel, HTML, etc.);

[0049] Decision support tools: Provide users with decision support through data visualization tools to help identify potential problems and develop optimization plans.

[0050] Furthermore, the model output and application module includes:

[0051] a.BIM file generator

[0052] Multi-format support: Generate files in multiple BIM formats, such as IFC, RVT, NWC, etc., to ensure compatibility with commonly used BIM tools in the industry;

[0053] File optimization tools: provide model lightweight options, optimize BIM file size, and improve transmission and loading efficiency, especially for mobile devices or cloud applications;

[0054] b. Model application:

[0055] Visualization tools: Provide a variety of 3D model viewing tools, support rotation, zooming, cutting, annotation and other functions, so that users can view the design results in detail;

[0056] User feedback system: Users can submit feedback in real time on the interface, and the system will automatically adjust the model or prompt the user to intervene manually based on the feedback;

[0057] Collaboration platform integration: Supports integration with mainstream project management and collaboration platforms (such as Autodesk BIM 360, TrimbleConnect) to achieve multi-person collaborative design and real-time data sharing.

[0058] Furthermore, the NLP (natural language processing) and user interaction module includes:

[0059] a. AI interaction enhances user interaction:

[0060] ChatGDP Intelligent Design Assistant: Provides intelligent assistant functions that can answer users' design-related questions, explain building specifications, material selection, construction techniques, etc., and can adjust the model according to user needs;

[0061] Instant technical support: When users encounter system operation problems or technical difficulties, ChatGPT can provide instant help, including system operation guides, FAQs and solutions to specific technical problems;

[0062] Training and guidance: Provide user training and guidance functions, guide new users to become familiar with system functions through dialogue, and provide operation examples and skills.

[0063] b. Intelligent design suggestion generation:

[0064] Intelligent design proposals: Based on existing building codes and extracted data, ChatGPT can generate preliminary design suggestions, including layout adjustments, material replacements, etc. Users can directly adjust or accept these suggestions through natural language;

[0065] Parametric adjustment support: When a user makes a modification request for a design element, ChatGPT can automatically adjust the model parameters based on the user's input and generate a new design solution;

[0066] c. Decision support and feedback loop:

[0067] Interpretation of simulation results: In the simulation analysis report generator section, ChatGPT can help users interpret complex simulation results, provide explanations and suggestions of key data in natural language, and help users better understand the analysis report;

[0068] Optimization suggestion feedback: Combined with the feedback loop mechanism, ChatGPT can provide optimization suggestions based on user feedback and make automated adjustments to better meet user needs.

[0069] A working method of an intelligent modeling tool for building structures and MEP systems comprises the following steps:

[0070] (1) ODA plug-in drawing analysis:

[0071] At the initial stage of the process, the system accepts architectural drawings provided by the user, which are usually uploaded in DWG or other common formats;

[0072] Using a plug-in developed based on the Open Design Alliance (ODA), the system can directly extract key structural information of the building from these files, such as the size, layout and other annotation data of walls, doors, windows, columns, etc.

[0073] This step ensures that the basic information of the building is accurately digitized, laying a solid foundation for subsequent steps;

[0074] (2) Drawing preprocessing:

[0075] To further optimize the processing of drawings, the system will convert DWG or other formats of drawings into PDF or PNG format. The converted drawings will undergo a series of pre-processing operations, including angle correction, noise removal, line enhancement, etc., to ensure that the drawings remain of high quality after conversion and are ready for the image recognition process;

[0076] The clarity and accuracy of the drawings are guaranteed to the greatest extent at this stage;

[0077] (3) Image recognition:

[0078] Using advanced deep learning algorithms, the system can identify architectural elements from pre-processed drawings; the recognition covers basic building components such as walls, columns, doors and windows, as well as complex mechanical, electrical and plumbing systems (MEP);

[0079] In addition, key information such as text annotations, identifiers and dimensioning in the drawings will also be extracted by the system and converted into usable digital data;

[0080] This step makes the architectural drawing information more systematic and structured, which is convenient for subsequent processing and analysis;

[0081] (4) Data integration and verification:

[0082] The system integrates all data extracted from different formats and steps into a complete digital data set. To ensure the accuracy of these data, the system automatically compares them with predefined building specifications;

[0083] If it detects that certain design elements (such as wall thickness) do not meet the specification requirements, the system will automatically mark them and prompt the user to make corrections;

[0084] This process effectively reduces human errors and ensures design consistency and compliance;

[0085] (5) Preliminary layout generation:

[0086] After data integration and verification, the system will automatically generate a preliminary building layout design based on the building design specifications and the extracted structured data; this layout not only covers the overall structure of the building, but also includes the preliminary layout of the mechanical, electrical, and plumbing systems (MEP); the system will automatically arrange the positions of major elements such as walls, doors, windows, and stairs according to the building's use function and space planning;

[0087] During the layout generation process, users can make specific design requests to the system through natural language. For example, users may want to adjust the room layout of a certain area, or change the use of certain building materials. ChatGPT will automatically adjust the model parameters based on the user's input and generate corresponding design suggestions. The system will update the layout design in real time to ensure that the user's requirements are met.

[0088] (6) Layout optimization

[0089] After the initial layout is generated, the system will immediately optimize the layout. The system uses optimization algorithms to analyze the configuration of each building element in the layout to ensure that the layout maximizes space utilization and optimizes building functionality while complying with building regulations.

[0090] Genetic algorithms can be used to find the optimal spatial layout configuration. For example, among multiple possible room layouts, genetic algorithms can select the best layout solution in terms of functionality, space utilization, and aesthetics through continuous iteration. It is particularly suitable for solving optimization problems with multiple objectives and complex constraints.

[0091] The application of simulated annealing algorithm in layout optimization is mainly reflected in solving conflict detection and local adjustment problems. For example, after detecting the collision between the pipeline and the wall in the layout, the simulated annealing algorithm can find a solution that can both resolve the conflict and minimize the impact through random perturbation and step-by-step optimization. This algorithm is suitable for dealing with complex layout problems with multiple local optimal solutions.

[0092] Particle swarm optimization algorithm is often used in layout optimization for multi-objective optimization problems. For example, when considering multiple factors such as space utilization, construction cost, and energy consumption at the same time, the particle swarm optimization algorithm can quickly find an optimal layout solution that balances various objectives through a group search strategy. It is particularly suitable for handling multi-dimensional and multi-objective optimization tasks.

[0093] (7) Conflict Detection

[0094] After the layout optimization is completed, the system will automatically start the conflict detection module to identify various conflicts that may occur in the design. These conflicts may include the collision of pipes and walls, the intersection of cables and ventilation systems, and insufficient space for equipment installation. The system will locate each conflict point in detail through precise geometric analysis, functional evaluation and space verification. After detecting the conflict, the system will not only generate detailed correction suggestions, but also automatically adjust the layout design to eliminate the conflict. The adjusted layout will be tested again to ensure that all conflicts are completely resolved while maintaining the functionality, feasibility and compliance with building code requirements of the design;

[0095] (8) 3D modeling and simulation:

[0096] The optimized layout is converted into a detailed 3D model. Based on BIM (Building Information Modeling) technology, these models can show every architectural detail. The system then performs simulation tests on the model to ensure the stability of the structure and the rationality of the MEP system. Through methods such as finite element analysis and fluid dynamics simulation, the system can discover and automatically correct potential problems in the design to ensure that the final building design can be smoothly implemented in actual construction.

[0097] (9) Simulation results and feedback loop:

[0098] After the simulation is completed, the system will generate a detailed analysis report, including key information such as simulation results and data statistics. For complex simulation results, ChatGPT can provide natural language explanations and suggestions to help users better understand the report content. Combined with the feedback loop mechanism, users can propose new requirements or adjustments based on the simulation results. The system will automatically optimize the design and regenerate the layout and model, thereby achieving continuous improvement and optimization of the design.

[0099] (10) Model output and application:

[0100] After multiple optimizations and verifications, the 3D model will be exported to various BIM file formats, such as IFC or RVT, which are convenient for use in different architectural design software. Through the 3D model viewing tool, the design team can check every detail of the model in detail. If any areas for improvement are found at this stage, the system can automatically adjust the model based on the feedback to ensure the best design.

[0101] (11) Enhanced user interaction:

[0102] The system provides an intelligent assistant function that can answer users' questions about design in real time, explain building specifications, material selection, construction techniques, etc., and adjust the model according to user needs;

[0103] When users encounter operational problems or technical difficulties when using the system, ChatGPT can provide instant help, including operation guides, FAQs and solutions to specific technical problems. At the same time, the system can also guide new users to familiarize themselves with its functions through dialogue, and provide operation examples and practical tips.

[0104] (12) Collaboration and project implementation:

[0105] To ensure the smooth implementation of the project, the final 3D model will be uploaded to a collaborative platform, such as Autodesk BIM360, which allows team members to view, discuss and jointly modify the design in real time, ensuring that everyone is working on the same version and avoiding communication and coordination problems caused by version differences. Ultimately, this design, which has been optimized many times, will be used for actual construction to ensure that the construction of the building complies with all specifications and requirements and successfully achieves the expected goals of the project.

[0106] The beneficial effects achieved by the present invention using the above structure are as follows: The present invention proposes an intelligent modeling tool for building structures and MEP systems and a working method thereof, which specifically have the following advantages:

[0107] (1) The intelligent modeling tool provided by this solution realizes the intelligence of the entire process from inputting architectural drawings to outputting BIM models through the collaborative work of various functional modules. The tool covers multiple links such as information extraction and storage, intelligent layout and optimization, modeling and simulation, model output and application, NLP (natural language processing) and user interaction modules, embodying the modular design concept and ensuring the scalability and maintainability of the system.

[0108] (2) The method provided in this solution aims to comprehensively improve the modeling efficiency of building structures and MEP systems by introducing advanced intelligent technology. Specifically, the method can reduce manual intervention and intelligently handle complex design tasks, thereby achieving higher modeling accuracy and quality. Through precise modeling and optimization algorithms, the present invention can also effectively reduce the error rate in the construction process and avoid rework problems caused by design errors. In addition, the present invention has significant advantages in saving modeling time and costs, and can provide more efficient and reliable design and construction solutions for construction projects, ultimately improving the execution efficiency and economic benefits of the entire project. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0110] Figure 1 The technical flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0111] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0112] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0113] See also Figure 1 , this embodiment provides an intelligent modeling tool for building structure and MEP system, including:

[0114] Information extraction and storage module, used to achieve efficient and accurate extraction and storage of digital information from architectural drawings;

[0115] Intelligent layout and optimization module, used to generate building layout and MEP system layout plans that meet design specifications and optimization goals;

[0116] Modeling and simulation module, used to convert the optimized layout into a 3D model and perform simulation verification to ensure the feasibility of the design;

[0117] Model output and application module, used to realize the final output of the model and interaction with users;

[0118] The NLP (natural language processing) and user interaction module is used to achieve natural language interaction with users, automatically generate design suggestions, and provide intelligent technical support and feedback through ChatGPT technology.

[0119] Specifically, in this embodiment, the information extraction and storage module includes:

[0120] a. Input interface:

[0121] The input interface supports and is compatible with the input of different types of architectural design files, such as DWG, PDF, IFC, DGN, DXF, etc. It supports emerging file formats through plug-ins or extension mechanisms, and converts the input DWG or other format drawings into PDF or PNG format;

[0122] The input interface integrates a pre-processing unit, which has the functions of input file recognition, drawing conversion, drawing correction, noise filtering, etc., to ensure the clarity and accuracy of the input drawings;

[0123] b. Information extraction engine:

[0124] ODA SDK parsing: By integrating ODA SDK, the system can directly read a wide range of architectural design file formats, such as DWG, DXF, DGN, etc., and can not only accurately parse the graphic data in these files, but also extract the detailed architectural information contained therein, such as geometric structure, hierarchical relationship, annotations, etc.;

[0125] Image recognition module: uses deep learning technology to identify building elements (such as walls, columns, beams, doors and windows, etc.) and specific components of MEP (mechanical, electrical, and piping) systems in drawings;

[0126] Text and symbol parser: can accurately extract notes, identifiers, dimensioning and other information on drawings and convert them into digital data;

[0127] Data verification mechanism: Verify the accuracy of extracted data by comparing it with pre-defined building specifications to ensure the reliability of subsequent processing;

[0128] c. Data storage unit:

[0129] Structured database: stores information about building structures and MEP systems, using a combination of relational databases (MySQL) and NoSQL databases (MongoDB) to optimize data query and retrieval efficiency;

[0130] Metadata management: records metadata during the data extraction process (data source, extraction time, extraction accuracy, etc.) and supports data version control and traceability.

[0131] Specifically, in this embodiment, the intelligent layout and optimization module includes:

[0132] a. Rule base:

[0133] Building regulations module: covers national and regional building regulations, industry standards, environmental protection requirements, etc., and supports dynamic updates to adapt to changes in regulations;

[0134] MEP layout rule library: stores MEP layout rules for different types of buildings, covering pipeline layout, cable routing, ventilation system, etc.;

[0135] Custom rule interface: allows users to add or modify rules according to specific project requirements, supports rule priority setting and conflict detection;

[0136] b.Layout generation engine:

[0137] Preliminary layout generator: Based on the rule base and extracted data, it uses heuristic algorithms to quickly generate preliminary layouts, covering structural arrangement and MEP system configuration;

[0138] Clash Detection Module: After the initial layout is generated, clash detection (collision between building structure and MEP system) is performed immediately and intelligent correction suggestions are provided;

[0139] c. Optimization algorithm module:

[0140] Multi-objective optimizer: Combine genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm, etc. to perform multi-objective optimization, including maximizing space utilization, minimizing material cost, minimizing construction difficulty, etc.;

[0141] Constraint processing unit: handles various constraints encountered during the optimization process, such as space constraints, structural strength requirements, channel priorities of MEP systems, etc.

[0142] Feedback loop mechanism: Combine simulation results and user feedback to continuously iterate and optimize layout solutions.

[0143] Specifically, in this embodiment, the modeling and simulation module includes:

[0144] a. 3D modeling engine:

[0145] Model generation core: Based on BIM (Building Information Modeling) technology, generate 3D models that meet industry standards (supporting the formats of BIM software such as Revit and Navisworks);

[0146] Component library interface: call building components and MEP equipment from the predefined component library and make parameterized adjustments according to specific project requirements;

[0147] LOD (level of detail) management: supports model generation with different levels of detail, and flexibly adjusts model accuracy from concept design to construction drawings;

[0148] b.Simulation Engine:

[0149] Structural stability simulation: Based on finite element analysis, stress and strain analysis of the building structure is performed to ensure its stability and safety;

[0150] MEP system simulation: Simulate pipeline fluid mechanics, electrical load, ventilation efficiency, etc. to ensure the rationality of system design;

[0151] Collision detection and repair module: performs collision detection between building structure and MEP system, and provides automatic repair suggestions to avoid problems during construction;

[0152] c.Simulation analysis report generator:

[0153] Automatic report generator: Generates detailed simulation analysis reports, including various charts, data statistics, key result summaries, etc., and supports export in multiple formats (PDF, Excel, HTML, etc.);

[0154] Decision support tools: Provide users with decision support through data visualization tools to help identify potential problems and develop optimization plans.

[0155] Specifically, in this embodiment, the model output and application module includes:

[0156] a.BIM file generator

[0157] Multi-format support: Generate files in multiple BIM formats, such as IFC, RVT, NWC, etc., to ensure compatibility with commonly used BIM tools in the industry;

[0158] File optimization tools: provide model lightweight options, optimize BIM file size, and improve transmission and loading efficiency, especially for mobile devices or cloud applications;

[0159] b. Model application:

[0160] Visualization tools: Provide a variety of 3D model viewing tools, support rotation, zooming, cutting, annotation and other functions, so that users can view the design results in detail;

[0161] User feedback system: Users can submit feedback in real time on the interface, and the system will automatically adjust the model or prompt the user to intervene manually based on the feedback;

[0162] Collaboration platform integration: Supports integration with mainstream project management and collaboration platforms (such as Autodesk BIM 360, TrimbleConnect) to achieve multi-person collaborative design and real-time data sharing.

[0163] Specifically, in this embodiment, the NLP (natural language processing) and user interaction module includes:

[0164] a. AI interaction enhances user interaction:

[0165] ChatGDP Intelligent Design Assistant: Provides intelligent assistant functions that can answer users' design-related questions, explain building specifications, material selection, construction techniques, etc., and can adjust the model according to user needs;

[0166] Instant technical support: When users encounter system operation problems or technical difficulties, ChatGPT can provide instant help, including system operation guides, FAQs and solutions to specific technical problems;

[0167] Training and guidance: Provide user training and guidance functions, guide new users to become familiar with system functions through dialogue, and provide operation examples and skills.

[0168] b. Intelligent design suggestion generation:

[0169] Intelligent design proposals: Based on existing building codes and extracted data, ChatGPT can generate preliminary design suggestions, including layout adjustments, material replacements, etc. Users can directly adjust or accept these suggestions through natural language;

[0170] Parametric adjustment support: When a user makes a modification request for a design element, ChatGPT can automatically adjust the model parameters based on the user's input and generate a new design solution;

[0171] c. Decision support and feedback loop:

[0172] Interpretation of simulation results: In the simulation analysis report generator section, ChatGPT can help users interpret complex simulation results, provide explanations and suggestions of key data in natural language, and help users better understand the analysis report;

[0173] Optimization suggestion feedback: Combined with the feedback loop mechanism, ChatGPT can provide optimization suggestions based on user feedback and make automated adjustments to better meet user needs.

[0174] A working method of an intelligent modeling tool for building structures and MEP systems comprises the following steps:

[0175] (1) ODA plug-in drawing analysis:

[0176] At the initial stage of the process, the system accepts architectural drawings provided by the user, which are usually uploaded in DWG or other common formats;

[0177] Using a plug-in developed based on the Open Design Alliance (ODA), the system can directly extract key structural information of the building from these files, such as the size, layout and other annotation data of walls, doors, windows, columns, etc.

[0178] This step ensures that the basic information of the building is accurately digitized, laying a solid foundation for subsequent steps;

[0179] (2) Drawing preprocessing:

[0180] To further optimize the processing of drawings, the system will convert DWG or other formats of drawings into PDF or PNG format. The converted drawings will undergo a series of pre-processing operations, including angle correction, noise removal, line enhancement, etc., to ensure that the drawings remain of high quality after conversion and are ready for the image recognition process;

[0181] The clarity and accuracy of the drawings are guaranteed to the greatest extent at this stage;

[0182] (3) Image recognition:

[0183] Using advanced deep learning algorithms, the system can identify architectural elements from pre-processed drawings; the recognition covers basic building components such as walls, columns, doors and windows, as well as complex mechanical, electrical and plumbing systems (MEP);

[0184] In addition, key information such as text annotations, identifiers and dimensioning in the drawings will also be extracted by the system and converted into usable digital data;

[0185] This step makes the architectural drawing information more systematic and structured, which is convenient for subsequent processing and analysis;

[0186] (4) Data integration and verification:

[0187] The system integrates all data extracted from different formats and steps into a complete digital data set. To ensure the accuracy of these data, the system automatically compares them with predefined building specifications;

[0188] If it detects that certain design elements (such as wall thickness) do not meet the specification requirements, the system will automatically mark them and prompt the user to make corrections;

[0189] This process effectively reduces human errors and ensures design consistency and compliance;

[0190] (5) Preliminary layout generation:

[0191] After data integration and verification, the system will automatically generate a preliminary building layout design based on the building design specifications and the extracted structured data; this layout not only covers the overall structure of the building, but also includes the preliminary layout of the mechanical, electrical, and plumbing systems (MEP); the system will automatically arrange the positions of major elements such as walls, doors, windows, and stairs according to the building's use function and space planning;

[0192] During the layout generation process, users can make specific design requests to the system through natural language. For example, users may want to adjust the room layout of a certain area, or change the use of certain building materials. ChatGPT will automatically adjust the model parameters based on the user's input and generate corresponding design suggestions. The system will update the layout design in real time to ensure that the user's requirements are met.

[0193] (6) Layout optimization

[0194] After the initial layout is generated, the system will immediately optimize the layout. The system uses optimization algorithms to analyze the configuration of each building element in the layout to ensure that the layout maximizes space utilization and optimizes building functionality while complying with building regulations.

[0195] Genetic algorithms can be used to find the optimal spatial layout configuration. For example, among multiple possible room layouts, genetic algorithms can select the best layout solution in terms of functionality, space utilization, and aesthetics through continuous iteration. It is particularly suitable for solving optimization problems with multiple objectives and complex constraints.

[0196] The application of simulated annealing algorithm in layout optimization is mainly reflected in solving conflict detection and local adjustment problems. For example, after detecting the collision between the pipeline and the wall in the layout, the simulated annealing algorithm can find a solution that can both resolve the conflict and minimize the impact through random perturbation and step-by-step optimization. This algorithm is suitable for dealing with complex layout problems with multiple local optimal solutions.

[0197] Particle swarm optimization algorithm is often used in layout optimization for multi-objective optimization problems. For example, when considering multiple factors such as space utilization, construction cost, and energy consumption at the same time, the particle swarm optimization algorithm can quickly find an optimal layout solution that balances various objectives through a group search strategy. It is particularly suitable for handling multi-dimensional and multi-objective optimization tasks.

[0198] (7) Conflict Detection

[0199] After the layout optimization is completed, the system will automatically start the conflict detection module to identify various conflicts that may occur in the design. These conflicts may include the collision of pipes and walls, the intersection of cables and ventilation systems, and insufficient space for equipment installation. The system will locate each conflict point in detail through precise geometric analysis, functional evaluation and space verification. After detecting the conflict, the system will not only generate detailed correction suggestions, but also automatically adjust the layout design to eliminate the conflict. The adjusted layout will be tested again to ensure that all conflicts are completely resolved while maintaining the functionality, feasibility and compliance with building code requirements of the design;

[0200] (8) 3D modeling and simulation:

[0201] The optimized layout is converted into a detailed 3D model. Based on BIM (Building Information Modeling) technology, these models can show every architectural detail. The system then performs simulation tests on the model to ensure the stability of the structure and the rationality of the MEP system. Through methods such as finite element analysis and fluid dynamics simulation, the system can discover and automatically correct potential problems in the design to ensure that the final building design can be smoothly implemented in actual construction.

[0202] (9) Simulation results and feedback loop:

[0203] After the simulation is completed, the system will generate a detailed analysis report, including key information such as simulation results and data statistics. For complex simulation results, ChatGPT can provide natural language explanations and suggestions to help users better understand the report content. Combined with the feedback loop mechanism, users can propose new requirements or adjustments based on the simulation results. The system will automatically optimize the design and regenerate the layout and model, thereby achieving continuous improvement and optimization of the design.

[0204] (10) Model output and application:

[0205] After multiple optimizations and verifications, the 3D model will be exported to various BIM file formats, such as IFC or RVT, which are convenient for use in different architectural design software. Through the 3D model viewing tool, the design team can check every detail of the model in detail. If any areas for improvement are found at this stage, the system can automatically adjust the model based on the feedback to ensure the best design.

[0206] (11) Enhanced user interaction:

[0207] The system provides an intelligent assistant function that can answer users' questions about design in real time, explain building specifications, material selection, construction techniques, etc., and adjust the model according to user needs;

[0208] When users encounter operational problems or technical difficulties when using the system, ChatGPT can provide instant help, including operation guides, FAQs and solutions to specific technical problems. At the same time, the system can also guide new users to familiarize themselves with its functions through dialogue, and provide operation examples and practical tips.

[0209] (12) Collaboration and project implementation:

[0210] To ensure the smooth implementation of the project, the final 3D model will be uploaded to a collaborative platform, such as Autodesk BIM360, which allows team members to view, discuss and jointly modify the design in real time, ensuring that everyone is working on the same version and avoiding communication and coordination problems caused by version differences. Ultimately, this design, which has been optimized many times, will be used for actual construction to ensure that the construction of the building complies with all specifications and requirements and successfully achieves the expected goals of the project.

[0211] The intelligent modeling method of the present invention can be further extended to the modeling of other building systems, such as curtain wall systems and fire protection systems. In addition, the automation and intelligence level of the system can be further improved by integrating more intelligent algorithms.

[0212] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, material or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, material or apparatus.

[0213] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent modeling tool for building structures and MEP systems, characterized in that: include: Information extraction and storage module, used to achieve efficient and accurate extraction and storage of digital information from architectural drawings; Intelligent layout and optimization module, used to generate building layout and MEP system layout plans that meet design specifications and optimization goals; Modeling and simulation module, used to convert the optimized layout into a 3D model and perform simulation verification to ensure the feasibility of the design; Model output and application module, used to realize the final output of the model and interaction with users; The NLP and user interaction module is used to achieve natural language interaction with users, automatically generate design suggestions, and provide intelligent technical support and feedback through ChatGPT technology.

2. The intelligent modeling tool for building structures and MEP systems according to claim 1, characterized in that: The information extraction and storage module includes: a. Input interface: The input interface supports and is compatible with the input of different types of architectural design files, supports emerging file formats through plug-ins or extension mechanisms, and converts input DWG or other format drawings into PDF or PNG formats; The input interface integrates a preprocessing unit, which has the functions of input file recognition, drawing conversion, drawing correction, and noise filtering to ensure the clarity and accuracy of the input drawings; b. Information extraction engine: ODA SDK parsing: By integrating the ODA SDK, the system can directly read a wide range of architectural design file formats, not only accurately parsing the graphic data in these files, but also extracting the detailed architectural information contained therein; Image recognition module: uses deep learning technology to identify architectural elements in drawings and specific components of the MEP system; Text and symbol parser: can accurately extract information including annotations, identifiers, and dimensioning on drawings and convert them into digital data; Data verification mechanism: Verify the accuracy of extracted data by comparing it with pre-defined building specifications to ensure the reliability of subsequent processing; c. Data storage unit: Structured database: stores information about building structures and MEP systems, using a combination of relational and NoSQL databases to optimize data query and retrieval efficiency; Metadata management: records metadata during data extraction and supports data version control and traceability.

3. The intelligent modeling tool for building structures and MEP systems according to claim 1, characterized in that: The intelligent layout and optimization module includes: a. Rule base: Building regulations module: covers national and regional building regulations, industry standards, and environmental protection requirements, and supports dynamic updates to adapt to changes in regulations; MEP layout rule library: stores MEP layout rules for different types of buildings, covering pipeline layout, cable routing, and ventilation systems; Custom rule interface: allows users to add or modify rules according to specific project requirements, supports rule priority setting and conflict detection; b.Layout generation engine: Preliminary layout generator: Based on the rule base and extracted data, it uses heuristic algorithms to quickly generate preliminary layouts, covering structural arrangement and MEP system configuration; Conflict detection module: After the initial layout is generated, conflict detection is performed immediately and intelligent correction suggestions are provided; c. Optimization algorithm module: Multi-objective optimizer: Combines genetic algorithm, simulated annealing algorithm, and particle swarm optimization algorithm to perform multi-objective optimization, including maximizing space utilization, minimizing material costs, and minimizing construction difficulty; Constraint processing unit: handles various constraints encountered during the optimization process, including space constraints, structural strength requirements, and channel priorities of the MEP system; Feedback loop mechanism: Combine simulation results and user feedback to continuously iterate and optimize layout solutions.

4. The intelligent modeling tool for building structures and MEP systems according to claim 1, characterized in that: The modeling and simulation module includes: a. 3D modeling engine: Model generation core: Based on BIM technology, generate 3D models that meet industry standards; Component library interface: call building components and MEP equipment from the predefined component library and make parameterized adjustments according to specific project requirements; LOD management: supports model generation at different levels of detail, and flexibly adjusts model accuracy from concept design to construction drawings; b.Simulation Engine: Structural stability simulation: Based on finite element analysis, stress and strain analysis of the building structure is performed to ensure its stability and safety; MEP system simulation: simulate pipeline fluid mechanics, electrical load, and ventilation efficiency to ensure the rationality of system design; Collision detection and repair module: performs collision detection between building structure and MEP system, and provides automatic repair suggestions to avoid problems during construction; c.Simulation analysis report generator: Automatic report generator: Generate detailed simulation analysis reports, including various charts, data statistics, key result summaries, and support export in multiple formats; Decision support tools: Provide users with decision support through data visualization tools to help identify potential problems and develop optimization plans.

5. The intelligent modeling tool for building structures and MEP systems according to claim 1, characterized in that: The model output and application module includes: a.BIM file generator Multi-format support: Generate files in multiple BIM formats to ensure compatibility with commonly used BIM tools in the industry; File optimization tools: provide model lightweight options, optimize BIM file size, and improve transmission and loading efficiency, especially for mobile devices or cloud applications; b. Model application: Visualization tools: Provide a variety of 3D model viewing tools, support rotation, scaling, sectioning, and annotation, so that users can view the design results in detail; User feedback system: Users can submit feedback in real time on the interface, and the system will automatically adjust the model or prompt the user to intervene manually based on the feedback; Collaboration platform integration: supports integration with mainstream project management and collaboration platforms to achieve multi-person collaborative design and real-time data sharing.

6. The intelligent modeling tool for building structures and MEP systems according to claim 1, characterized in that: The NLP and user interaction module includes: a. AI interaction enhances user interaction: ChatGDP Intelligent Design Assistant: Provides intelligent assistant functions that can answer users' design-related questions, explain building specifications, material selection, construction techniques, and adjust the model according to user needs; Instant technical support: When users encounter system operation problems or technical difficulties, ChatGPT can provide instant help, including system operation guides, FAQs and solutions to specific technical problems; Training and guidance: Provide user training and guidance functions, guide new users to become familiar with system functions through dialogue, and provide operation examples and skills; b. Intelligent design suggestion generation: Intelligent design proposals: Based on existing building codes and extracted data, ChatGPT can generate preliminary design suggestions, including layout adjustments and material replacements, which users can directly adjust or accept through natural language; Parametric adjustment support: When a user makes a modification request for a design element, ChatGPT can automatically adjust the model parameters based on the user's input and generate a new design solution; c. Decision support and feedback loop: Interpretation of simulation results: In the simulation analysis report generator section, ChatGPT can help users interpret complex simulation results, provide explanations and suggestions of key data in natural language, and help users better understand the analysis report; Optimization suggestion feedback: Combined with the feedback loop mechanism, ChatGPT can provide optimization suggestions based on user feedback and make automated adjustments to better meet user needs.

7. The working method of the intelligent modeling tool for building structure and MEP system according to claim 1, characterized in that: The specific steps include: (1) ODA plug-in drawing analysis: At the initial stage of the process, the system accepts architectural drawings provided by the user, which are usually uploaded in DWG or other common formats; Using a plug-in developed based on the Open Design Alliance (ODA), the system can directly extract key structural information of the building from these files, including the size, layout and other annotation data of walls, doors, windows and columns; (2) Drawing preprocessing: To further optimize the processing of drawings, the system will convert drawings in DWG or other formats into PDF or PNG format. The converted drawings will undergo a series of pre-processing operations, including angle correction, noise removal, and line enhancement to ensure that the drawings remain of high quality after conversion and are ready for the image recognition process; (3) Image recognition: Using advanced deep learning algorithms, the system can identify architectural elements from pre-processed drawings, including basic building components such as walls, columns, doors and windows, as well as complex mechanical, electrical and plumbing systems (MEP); In addition, key information in the drawings, including text annotations, identifiers and dimensioning, will also be extracted by the system and converted into usable digital data; (4) Data integration and verification: The system integrates all data extracted from different formats and steps into a complete digital data set. To ensure the accuracy of these data, the system automatically compares them with predefined building specifications; If it is detected that some design elements do not meet the specification requirements, the system will automatically mark them and prompt the user to make corrections; (5) Preliminary layout generation: After data integration and verification, the system will automatically generate a preliminary building layout design based on the building design specifications and the extracted structured data; the layout not only covers the overall structure of the building, but also includes the preliminary layout of the mechanical, electrical and piping systems; the system will automatically arrange the positions of major elements including walls, doors, windows and stairs according to the building's functional use and space planning; During the layout generation process, users can make specific design requests to the system through natural language; ChatGPT will automatically adjust the model parameters based on the user's input and generate corresponding design suggestions. The system will update the layout design in real time to ensure that the user's requirements are met; (6) Layout optimization After the initial layout is generated, the system will immediately optimize the layout. The system uses optimization algorithms to analyze the configuration of each building element in the layout to ensure that the layout maximizes space utilization and optimizes building functionality while complying with building regulations. Genetic algorithms can be used to find the optimal spatial layout configuration. The algorithm is suitable for solving optimization problems with multiple objectives and complex constraints. The application of simulated annealing algorithm in layout optimization is mainly reflected in solving conflict detection and local adjustment problems. The algorithm is suitable for dealing with complex layout problems with multiple local optimal solutions. Particle swarm optimization algorithm is often used in layout optimization for multi-objective optimization problems. This algorithm is suitable for handling multi-dimensional and multi-objective optimization tasks; (7) Conflict Detection After the layout optimization is completed, the system will automatically start the conflict detection module to identify various conflicts that may occur in the design, including the collision between pipes and walls, the intersection of cables and ventilation systems, and insufficient space for equipment installation. The system will locate each conflict point in detail through precise geometric analysis, functional evaluation and space verification. After detecting the conflict, the system will not only generate detailed correction suggestions, but also automatically adjust the layout design to eliminate the conflict. The adjusted layout will be tested again to ensure that all conflicts are completely resolved while maintaining the functionality, feasibility and compliance with building code requirements of the design; (8) 3D modeling and simulation: The optimized layout is converted into a detailed 3D model. Based on BIM technology, these models can show every architectural detail. The system then performs simulation tests on the model to ensure the stability of the structure and the rationality of the MEP system. Through finite element analysis and fluid mechanics simulation methods, the system can find and automatically correct potential problems in the design to ensure that the final architectural design can be smoothly implemented in actual construction. (9) Simulation results and feedback loop: After the simulation is completed, the system will generate a detailed analysis report containing key information such as simulation results and data statistics. For complex simulation results, ChatGPT can provide natural language explanations and suggestions to help users better understand the report content; Combined with the feedback loop mechanism, users can propose new requirements or adjustments based on simulation results, and the system will automatically optimize the design and regenerate the layout and model, thereby achieving continuous improvement and optimization of the design; (10) Model output and application: After multiple optimizations and verifications, the 3D model will be exported into a variety of BIM file formats, which are convenient for use in different architectural design software. Through the 3D model viewing tool, the design team can check every detail of the model in detail; If areas for improvement are found at this stage, the system can automatically adjust the model based on the feedback to ensure the best design. (11) Enhanced user interaction: The system provides an intelligent assistant function that can answer users' questions about design in real time and adjust the model according to user needs; When users encounter operational problems or technical difficulties when using the system, ChatGPT can provide instant help, including operation guides, FAQs and solutions to specific technical problems. At the same time, the system can also guide new users to familiarize themselves with its functions through dialogue, and provide operation examples and practical tips. (12) Collaboration and project implementation: To ensure the smooth implementation of the project, the final 3D model will be uploaded to the collaborative platform, allowing team members to view, discuss and jointly modify the design in real time, ensuring that everyone is working on the same version and avoiding communication and coordination problems caused by version differences. Ultimately, this design, which has been optimized many times, will be used for actual construction to ensure that the construction of the building complies with all specifications and requirements and successfully achieves the expected goals of the project.

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