Green building performance analysis method and system based on BIM and AI large models

Abstract

The invention discloses a BIM and AI large model-based green building performance analysis method and system, and the method comprises the steps: constructing a three-dimensional information model of a building based on a BIM technology, carrying out the performance simulation of the three-dimensional information model, obtaining the performance simulation data of the building, carrying out the deep learning and processing of the performance simulation data through employing the AI large model, and obtaining the performance simulation data of the building. And obtaining a building performance analysis result, and generating and displaying a building performance optimization suggestion according to the building performance analysis result. According to the method, the three-dimensional information model is constructed by using the BIM technology, and the full-life-cycle information of the building is integrated, so that the performance analysis is more comprehensive and accurate. Meanwhile, the AI large model technology is combined, hidden information and rules in the data can be deeply mined, and the analysis accuracy is further improved. And through an automatic data processing flow, the manual operation is greatly reduced, and the analysis efficiency is improved. The introduction of the AI large model enables the data processing to be intelligent and adaptive, and the processing strategy can be automatically adjusted according to the data characteristics.

Description

Technical Field

[0001] The present invention belongs to the technical field of building performance analysis and optimization, and in particular relates to a green building performance analysis method and system based on BIM and AI large model. Background Art

[0002] As the world pays more and more attention to sustainable development and environmental protection, green buildings, as an efficient and environmentally friendly building form, have received widespread attention. However, there are still many challenges in the design, construction and management of green buildings, such as how to accurately evaluate building performance and how to optimize building design to reduce energy consumption and improve comfort. In the existing technology, there are some building performance analysis methods based on BIM and AI technology. BIM technology provides a rich data foundation for performance analysis by constructing a three-dimensional information model of the building. AI technology can perform deep learning and processing on these data, discover hidden laws and trends in the data, and thus put forward optimization suggestions. Although the existing public technology has made some progress in the field of green building performance analysis, it still has some obvious shortcomings. First, the public technology may have limitations in data processing and analysis. Since building performance analysis involves a large amount of data and information, including the structure, materials, environmental parameters and other aspects of the building, traditional data processing and analysis methods may not be able to effectively cope with such a complex data set. This may affect the precision and accuracy of the analysis results and fail to provide strong support for designers and decision makers. Secondly, the existing technology may lack sufficient intelligence and adaptability. Green building performance analysis needs to constantly adapt to changing environments and conditions, but existing technologies can only provide fixed analysis models and algorithms, and cannot be flexibly adjusted and optimized according to actual conditions. This limits the scope of application and effect of the technology, and the analysis results may deviate greatly from the actual situation. For example, a technical solution has been disclosed that uses a building performance analysis method based on BIM and machine learning. This method uses a BIM model to extract building information and predicts and optimizes building performance through a machine learning algorithm. However, this method mainly relies on traditional machine learning algorithms, which have limited processing and analysis capabilities for big data, and it is difficult to discover deep laws and trends in the data. There are often problems such as incomplete data integration, inaccurate performance analysis, and incomplete optimization suggestions. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a green building performance analysis method and system based on BIM and AI big model to solve the shortcomings of the existing technology in data processing, analysis, intelligence and adaptability, and the problem that it is unable to provide a more comprehensive, efficient and accurate green building performance analysis solution.

[0004] To achieve the above objectives, the present invention provides a green building performance analysis method based on BIM and AI big model, comprising: Construct a three-dimensional information model of the building based on BIM technology; Perform performance simulation on the three-dimensional information model to obtain performance simulation data of the building; Use AI big models to conduct deep learning and processing of performance simulation data to obtain building performance analysis results; Generate and display building performance optimization suggestions based on building performance analysis results.

[0005] According to a specific embodiment of the present invention, constructing a three-dimensional information model of a building based on BIM technology specifically includes: Obtain information data on the entire life cycle of a building; Construct a three-dimensional BIM model of the building based on full life cycle information data.

[0006] According to a specific embodiment of the present invention, the full life cycle information data includes engineering drawings, material lists and equipment information data of the building.

[0007] According to a specific embodiment of the present invention, the performance simulation of the three-dimensional information model is performed to obtain the performance simulation data of the building, including: The thermal environment, light environment and acoustic environment of the three-dimensional information model are simulated and calculated according to the preset environmental parameters and building parameters, and the thermal environment simulation data, light environment simulation data and acoustic environment simulation data of the building under different environmental parameters and building parameters are obtained.

[0008] According to a specific embodiment of the present invention, the AI ​​big model is used to perform deep learning and processing on the performance simulation data, and the building performance analysis results obtained include: Performing data cleaning and preprocessing on the performance simulation data to obtain processed data of the performance simulation data; Use AI big model to extract features from processed data to obtain building performance feature data; Train the AI ​​big model based on building performance characteristic data and output building performance analysis results; The AI ​​large model is adaptively adjusted and optimized according to the actual environment and input conditions, and the actual building performance analysis results are output.

[0009] According to a specific embodiment of the present invention, the building performance analysis results include building energy consumption analysis results, thermal performance analysis results, ventilation performance analysis results, renewable energy utilization analysis results and equipment system energy efficiency analysis results.

[0010] According to a specific embodiment of the present invention, generating and displaying building performance optimization suggestions based on building performance analysis results includes: Generate corresponding performance optimization suggestions based on the building energy consumption analysis results, thermal performance analysis results, ventilation performance analysis results, renewable energy utilization analysis results and equipment system energy efficiency analysis results; The building energy consumption analysis results, thermal performance analysis results, ventilation performance analysis results, renewable energy utilization analysis results, equipment system energy efficiency analysis results and corresponding performance optimization suggestions are visualized in the form of analysis reports and charts.

[0011] According to a specific embodiment of the present invention, the performance optimization suggestion includes: a plan for improving the building design, a plan for optimizing the material selection, and a plan for improving the energy efficiency.

[0012] A green building performance analysis system based on BIM and AI big model, including: Data acquisition and modeling module, used to build a three-dimensional information model of the building based on BIM technology; Building performance simulation module, used to simulate the performance of the three-dimensional information model and obtain the performance simulation data of the building; AI big model processing module, used to use AI big model to conduct deep learning and processing of performance simulation data to obtain building performance analysis results; The optimization suggestion output module is used to generate and display building performance optimization suggestions based on the building performance analysis results.

[0013] According to a specific embodiment of the present invention, data acquisition and modeling module, building performance simulation module, AI large model processing module and optimization suggestion output module are used for data transmission and collaboration through data interface and computer program.

[0014] Compared with the prior art, the present invention provides a green building performance analysis method and system based on BIM and AI big model. The system integrates BIM building information model and AI big model technology to achieve comprehensive, efficient and accurate analysis of green building performance. The present application can process larger and more complex data sets by introducing AI big model technology, and discover deep laws and trends in the data through deep learning and data mining technology, greatly improving the efficiency of data analysis, and can quickly identify and optimize key issues of building performance. The AI ​​big model can automatically adjust and optimize the analysis model according to environmental changes and new data inputs to ensure that the analysis results are always consistent with the actual situation, so it has higher intelligence and adaptability. Through deep learning and data mining technology, the present application can discover deep laws and trends in the data and put forward more accurate and targeted optimization suggestions. The present application also combines the advantages of BIM technology to achieve comprehensive integration and efficient management of building information, providing a more accurate data basis for performance analysis. The present application can easily use the system of the present application for building performance analysis and view the analysis results in real time through the visual interface design. In summary, this application has important technical value and practical significance in the field of green building performance analysis, and can provide more comprehensive, efficient and accurate technical support for the design, construction and management of green buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention provides a flowchart of a green building performance analysis method based on BIM and AI big model according to an embodiment of the present invention.

[0016] Figure 2 It is a flow chart of a method for constructing a three-dimensional information model of a building according to an embodiment of the present invention.

[0017] Figure 3 It is a flow chart of a method for deep learning and processing performance simulation data using an AI big model provided according to an embodiment of the present invention.

[0018] Figure 4 It is a flow chart of a method for generating building performance optimization suggestions based on building performance analysis results provided according to an embodiment of the present invention.

[0019] Figure 5 It is a structural diagram of a green building performance analysis system based on BIM and AI big model provided according to one embodiment of the present invention.

[0020] Reference numerals: 1-Data collection and modeling module; 2-Building performance simulation module; 3-AI large model processing module; 4-Optimization suggestion output module. DETAILED DESCRIPTION

[0021] In order to make those skilled in the art understand the concept and thought of the present invention more clearly, the present invention is described in detail below in conjunction with specific embodiment.It should be understood that the embodiment provided herein is only a part of all embodiments that the present invention may have.Those skilled in the art, after reading the specification of the application, have the ability to make improvements, transformations, or replacements to part or the whole of the following embodiments, and these improvements, transformations, or replacements are also included in the scope of the present invention.

[0022] In this article, the terms "first", "second" and other similar words are not intended to imply any order, quantity and importance, but are only used to distinguish different elements. In this article, the terms "one", "an" and other similar words are not intended to indicate that there is only one thing, but to indicate that the relevant description is only for one of the things, and the thing may have one or more. In this article, the terms "comprise", "include" and other similar words are intended to indicate logical mutual relations, and cannot be regarded as indicating spatial structural relations. For example, "A includes B" is intended to indicate that B belongs to A logically, but does not mean that B is located inside A in space. In addition, the meanings of the terms "comprise", "include" and other similar words should be regarded as open, rather than closed. For example, "A includes B" is intended to indicate that B belongs to A, but B does not necessarily constitute the whole of A, and A may also include other elements such as C, D, and E.

[0023] In this document, the terms "embodiment", "this embodiment", "one embodiment", and "an embodiment" do not mean that the relevant description is only applicable to a specific embodiment, but rather that the description may also be applicable to one or more other embodiments. Those skilled in the art should understand that in this document, any description of a certain embodiment can be replaced, combined, or combined in other ways with the relevant descriptions in one or more other embodiments, and the new embodiments generated by the replacement, combination, or combination in other ways are easily conceivable by those skilled in the art and fall within the scope of protection of the present invention.

[0024] Example 1 Additional aspects and advantages of embodiments of the present invention will be described in part in the following description, and will become apparent from the following description, or will be learned through practice of embodiments of the present invention. Figure 1-Figure 4 The embodiment of the present invention provides a green building performance analysis method based on BIM and AI big model, including: S1: Construct a three-dimensional information model of the building based on BIM technology.

[0025] S2: Perform performance simulation on the three-dimensional information model to obtain performance simulation data of the building.

[0026] S3: Use AI big models to conduct deep learning and processing of performance simulation data to obtain building performance analysis results.

[0027] S4: Generate and display building performance optimization suggestions based on the building performance analysis results.

[0028] Specifically, step S1 constructs a three-dimensional information model of a building based on BIM technology, including: S11: Obtain the full life cycle information data of the building, which includes the building's engineering drawings, material lists and equipment information data.

[0029] S12: Construct a three-dimensional BIM model of the building based on the full life cycle information data.

[0030] This application uses BIM technology to construct a three-dimensional information model of a building. The model integrates the entire life cycle information of the building, including not only the geometric information of the building, but also the detailed information of the building's materials, equipment, structure, etc. These data not only provide a static description of the building's performance, but also include the dynamic response information of the building under different environmental conditions. At the same time, through the data management function of BIM, various data related to building performance can be easily acquired and integrated. Traditional building performance analysis is often limited by the difficulty and incompleteness of data acquisition. Compared with traditional building performance analysis, this application can achieve a full-scale analysis of green building performance through the comprehensiveness of the BIM model, and realize the comprehensive integration and efficient management of building information through BIM technology, providing a comprehensive and accurate data foundation for performance analysis.

[0031] Specifically, step S2 performs performance simulation on the three-dimensional information model to obtain performance simulation data of the building including: The thermal environment, light environment and acoustic environment of the three-dimensional information model are simulated and calculated according to the preset environmental parameters and building parameters, and the thermal environment simulation data, light environment simulation data and acoustic environment simulation data of the building under different environmental parameters and building parameters are obtained.

[0032] This application simulates and analyzes the performance of green buildings based on the BIM model, including thermal environment simulation, light environment simulation and acoustic environment simulation. By simulating the building performance under different environmental conditions, key indicators such as the building's energy consumption and comfort level can be evaluated, providing designers and decision makers with a more accurate basis for decision-making. This application uses professional simulation software to simulate and calculate the building's thermal environment, light environment, and acoustic environment according to different environmental conditions and building parameters. During the simulation process, various environmental parameters and building parameters will be considered, such as climate conditions, building materials, equipment performance, etc., to obtain accurate simulation results. By comparing and analyzing the simulation results with the actual data, key indicators such as the building's energy consumption and comfort level can be evaluated, providing a basis for performance optimization.

[0033] Specifically, step S3 uses the AI ​​big model to perform deep learning and processing on the performance simulation data, and obtains the building performance analysis results including: S31: Cleaning and preprocessing the performance simulation data to obtain processed data of the performance simulation data.

[0034] This application uses an AI big model to clean and preprocess the performance simulation data in order to extract the key features required for performance analysis.

[0035] S32: Use the AI ​​big model to extract features from the processed data to obtain building performance characteristic data.

[0036] This application introduces AI big model technology. Unlike existing technologies that mainly rely on traditional machine learning algorithms, AI big model technology can handle larger and more complex data sets, and discover deep laws and trends in the data through deep learning and data mining technology. This application uses AI big models to conduct deep learning and processing of large amounts of data obtained through simulation and analysis, and can automatically identify and extract key features related to building performance, and discover hidden laws and trends in the data through machine learning and data mining. This not only improves the efficiency and accuracy of data analysis, but also enables the system to make more accurate and targeted green building performance optimization suggestions.

[0037] S33: Train the AI ​​big model based on the building performance characteristic data and output the building performance analysis results. The building performance analysis results include building energy consumption analysis results, thermal performance analysis results, ventilation performance analysis results, renewable energy utilization analysis results and equipment system energy efficiency analysis results.

[0038] This application uses AI big models to analyze green buildings in multiple dimensions, such as energy consumption, thermal performance, and ventilation performance, and generates performance analysis reports, which can evaluate the performance of buildings under different climate conditions, usage conditions, and time periods. This application can be used in the design phase of green buildings to perform performance analysis on design solutions and predict the future operating effects of buildings, which helps designers discover and solve potential problems at an early stage, optimize design solutions, and improve the energy efficiency and comfort of buildings.

[0039] S34: Adaptively adjust and optimize the AI ​​large model according to the actual environment and input conditions, and output the actual building performance analysis results.

[0040] Existing technologies can only provide fixed analysis models and algorithms, and cannot be flexibly adjusted according to actual conditions. However, this application can automatically adjust and optimize the analysis model of the AI ​​big model according to the actual environment and conditions, ensuring that the analysis results are always consistent with the actual situation, making the analysis results more in line with the actual situation.

[0041] Specifically, step S4 generates building performance optimization suggestions based on the building performance analysis results and displays the following: S41: Generate corresponding performance optimization suggestions based on the building energy consumption analysis results, thermal performance analysis results, ventilation performance analysis results, renewable energy utilization analysis results and equipment system energy efficiency analysis results. The performance optimization suggestions include: building design improvement plan, material selection optimization plan and energy efficiency improvement plan.

[0042] S42: Visually display the building energy consumption analysis results, thermal performance analysis results, ventilation performance analysis results, renewable energy utilization analysis results, equipment system energy efficiency analysis results and corresponding performance optimization suggestions in the form of analysis reports and charts.

[0043] This application outputs optimization suggestions for green building performance based on the analysis results of the AI ​​large model. The optimization suggestions include specific measures such as improving building design, optimizing material selection, and improving energy efficiency, aiming to enhance the green performance and sustainability of buildings, reduce energy consumption, and improve the indoor environment. It provides strong support for the design and management of green buildings. The optimization suggestions are presented in a visual form, including charts, reports, etc., which are easy for users to understand and implement. Through friendly interface design and convenient operation, users can easily use the system of this application to analyze building performance, view the analysis results in real time, and adjust the building design or operation strategy according to the suggestions. At the same time, the system also provides a wealth of visualization tools to help users better understand the analysis results and optimization suggestions.

[0044] The advantages of the green building performance analysis method based on BIM and AI large model proposed in this invention are mainly reflected in the following aspects: 1. Comprehensive and accurate performance analysis: This invention uses BIM technology to build a three-dimensional information model, integrating the entire life cycle information of the building, making the performance analysis more comprehensive and accurate. At the same time, combined with AI large model technology, it can deeply mine the hidden information and rules in the data, further improving the accuracy of the analysis.

[0045] 2. Efficient and automated data processing: Traditional performance analysis often involves manual processing of large amounts of data, which is inefficient and error-prone. This invention greatly reduces manual operations and improves analysis efficiency by automating the data processing process. The introduction of AI big models makes data processing intelligent and adaptive, and can automatically adjust processing strategies based on data characteristics.

[0046] 3. Intelligent optimization suggestion output: Based on the results of deep learning and data mining, the present invention can output accurate and targeted optimization suggestions. These suggestions not only take into account the improvement of building performance, but also take into account economy and feasibility, providing strong support for designers and decision makers.

[0047] 4. User-friendly interactive experience: The present invention focuses on user experience, provides a simple and intuitive interface design and a friendly operation method. Users can easily get started and view analysis results and optimization suggestions in real time. At the same time, the system also provides a wealth of visualization tools to help users better understand the analysis results.

[0048] 5. Broad application prospects: The present invention is not only applicable to green building performance analysis, but can also be applied to performance evaluation and optimization of other building types. With the popularization of green buildings and sustainable development concepts, the application prospects of the present invention will be even broader.

[0049] Example 2 Combination Figure 5 The embodiment of the present invention provides a green building performance analysis system based on BIM and AI big model, including: The data acquisition and modeling module 1 is used to construct a three-dimensional information model of a building based on BIM technology.

[0050] This module is responsible for collecting, integrating and managing various data related to building performance, and uses BIM technology to build a three-dimensional information model of the building to achieve comprehensive integration of building information. The model contains information on geometry, materials, equipment, etc., providing an accurate data basis for performance analysis. In addition, this module also has data cleaning and preprocessing functions to ensure data quality and consistency.

[0051] The building performance simulation module 2 is used to perform performance simulation on the three-dimensional information model to obtain performance simulation data of the building.

[0052] This module simulates building performance based on the BIM model, and uses professional simulation software to simulate and calculate the building's thermal environment, light environment, acoustic environment, etc. according to different environmental conditions and building parameters. By comparing and analyzing the simulation results with actual data, key indicators such as the building's energy consumption and comfort level can be evaluated to provide a basis for performance optimization.

[0053] The AI ​​big model processing module 3 is used to use the AI ​​big model to perform deep learning and processing on the performance simulation data to obtain the building performance analysis results.

[0054] This module introduces AI big model technology to conduct deep learning and processing of large amounts of data obtained through simulation and analysis. The AI ​​big model can discover deep information and trends hidden in the data by automatically extracting data features and learning data patterns. By analyzing and mining this information, the module can put forward more accurate and targeted green building performance optimization suggestions.

[0055] The optimization suggestion output module 4 is used to generate and display building performance optimization suggestions based on the building performance analysis results.

[0056] This module outputs optimization suggestions for green building performance based on the building performance analysis results output by the AI ​​large model processing module. These suggestions may include specific measures such as improving building design, optimizing material selection, and improving energy efficiency. The suggestions are presented in a visual form, including charts, reports, etc., to facilitate user understanding and implementation. At the same time, the module also provides user interaction functions, and users can adjust and improve the suggestions according to actual conditions.

[0057] Specifically, data collection and modeling module 1, building performance simulation module 2, AI large model processing module 3 and optimization suggestion output module 4 are used for data transmission and collaboration through data interface and computer program.

[0058] The system architecture of this application is mainly composed of a data acquisition and modeling module 1, a building performance simulation module 2, an AI large model processing module 3, and an optimization suggestion output module 4. The modules realize information transmission and collaborative work through data interfaces and algorithm logic. The entire system is based on the data source, and realizes comprehensive evaluation and optimization of green building performance through steps such as BIM modeling, data processing, performance analysis, and optimization suggestions. The AI ​​large model plays a key role in the system, and improves the accuracy and efficiency of performance analysis through machine learning and data analysis technology. Finally, through the display of the user interface and the assistance of the decision support system, it helps designers and decision makers make scientific and reasonable decisions. The model is trained and optimized using deep learning algorithms and large-scale data sets, giving it powerful data processing and analysis capabilities. The shape and structure of the AI ​​large model can be customized and adjusted according to specific application scenarios and requirements to ensure its adaptability and accuracy. The AI ​​large model and other modules are connected and interacted through data interfaces and algorithm logic to realize information transmission and collaborative work. The technical solution of this application realizes a comprehensive, efficient and accurate analysis of green building performance through the collaborative work of various parts. The various parts are closely connected and have clear functions, together forming a complete and efficient green building performance analysis system.

[0059] The hardware for building the system mainly includes servers, storage devices, input and output devices, etc. The server is responsible for running the various modules and algorithms of the system, the storage device is used to store building information and analysis results, and the input and output devices are used for user interaction with the system. The hardware is connected through the network, and the information transmission and collaborative work are realized through the data interface. The server reads the building information in the storage device, runs the AI ​​large model processing module and the performance simulation and analysis module, and then displays the analysis results to the user through the output device. The user can operate and control the system through the input device.

[0060] Example 3 An embodiment of the present invention provides a working principle of a green building performance analysis system based on BIM and AI big model.

[0061] First, a three-dimensional information model of the building is constructed through the data acquisition and modeling module 1. The model integrates the entire life cycle information of the building, including geometry, materials, equipment and other data. These data not only provide a static description of the building performance, but also include the dynamic response information of the building under different environmental conditions.

[0062] Secondly, the building performance simulation module 2 is used to simulate the building performance, including thermal environment simulation, light environment simulation, and acoustic environment simulation, to evaluate key indicators such as building energy consumption and comfort. During the simulation process, the module will consider various environmental parameters and building parameters, such as climate conditions, building materials, equipment performance, etc., to obtain accurate simulation results.

[0063] Thirdly, the AI ​​big model processing module 3 performs deep learning and processing on the large amount of data obtained through simulation and analysis. The AI ​​big model processing module 3 can discover the deep information and trends hidden in the data by automatically extracting data features and learning data rules. By analyzing and mining this information, the AI ​​big model can put forward more accurate and targeted green building performance optimization suggestions.

[0064] Finally, the optimization suggestion output module 4 outputs the analysis results of the AI ​​large model processing module in a visual form. These suggestions may include specific measures such as improving building design, optimizing material selection, and improving energy efficiency. Through the visual presentation method, users can more intuitively understand the effect and implementation method of the optimization suggestions.

[0065] In order to prove the technical effect of the present invention, a series of experiments were conducted and relevant experimental data were collected. These experimental data not only verified the accuracy and efficiency of the present invention, but also demonstrated its wide application in practical applications.

[0066] 1. Verification of performance analysis accuracy: By comparing the analysis results of the present invention with the actual building operation data, it is found that the consistency between the two is as high as 95% or more. In multiple test cases, the present invention can accurately identify key indicators of building performance, such as energy consumption, temperature distribution, etc., and provide a detailed performance analysis report.

[0067] 2. Data processing efficiency test: The time required for performance analysis using the present invention was recorded and compared with the traditional method. The results showed that the present invention improved the efficiency of data processing and analysis by about 30%. This is mainly due to the automation and intelligent processing capabilities of the AI ​​big model, which greatly reduced the time for manual operation and data collation.

[0068] 3. Evaluation of the effectiveness of optimization suggestions: The optimization suggestions proposed by the present invention were applied to actual construction projects, and the effects after implementation were tracked. Experimental data showed that the optimized buildings had significant improvements in energy consumption, comfort, etc. For example, in an optimization project of an office building, energy consumption was reduced by 15% and employee satisfaction was increased by 10%.

[0069] The technical effects of the embodiments of the present invention are mainly reflected in the accuracy and comprehensiveness of performance analysis, the improvement of data processing and analysis efficiency, the practicality and innovation of optimization suggestions, and the improvement of user experience and interactivity. The realization of these technical effects makes the present invention have significant advantages and application value in the field of green building performance analysis. It mainly has the following uses: 1. Green building design and evaluation: This invention can be used in the design stage of green buildings to analyze the performance of design solutions and predict the future operation of buildings. This helps designers to find and solve potential problems at an early stage, optimize design solutions, and improve the energy efficiency and comfort of buildings.

[0070] 2. Renovation and upgrading of existing buildings: For existing buildings, the present invention can be used to evaluate their current performance and make targeted renovation and upgrading suggestions. This helps to extend the service life of the building, improve its energy efficiency and environmental performance, and reduce operating costs.

[0071] 3. Policy formulation and standard setting: Governments and enterprises can use the performance analysis results of the present invention to formulate more scientific and reasonable green building policies and standards. This will help promote the healthy development of the green building industry and improve the sustainable development level of the entire society.

[0072] In summary, the present invention provides a green building performance analysis method and system based on BIM and AI big model. The system integrates BIM building information model and AI big model technology to achieve comprehensive, efficient and accurate analysis of green building performance. This application can process larger and more complex data sets by introducing AI big model technology, and discover deep laws and trends in the data through deep learning and data mining technology, which greatly improves the efficiency of data analysis and can quickly identify and optimize key issues of building performance. The AI ​​big model can automatically adjust and optimize the analysis model according to environmental changes and new data inputs to ensure that the analysis results are always consistent with the actual situation, so it has higher intelligence and adaptability. This application can discover deep laws and trends in the data through deep learning and data mining technology, and put forward more accurate and targeted optimization suggestions. This application also combines the advantages of BIM technology to achieve comprehensive integration and efficient management of building information, providing a more accurate data basis for performance analysis. This application can easily use the system of this application for building performance analysis and view the analysis results in real time through a visual interface design. In summary, this application has important technical value and practical significance in the field of green building performance analysis, and can provide more comprehensive, efficient and accurate technical support for the design, construction and management of green buildings.

[0073] The above describes in detail the concepts, principles and ideas of the present invention in conjunction with specific implementation methods (including embodiments and examples). Those skilled in the art should understand that the implementation methods of the present invention are not limited to the forms given above. After reading this application document, those skilled in the art can make any possible improvements, substitutions and equivalent forms to the steps, methods, systems and components in the above implementation methods. These improvements, substitutions and equivalent forms should be deemed to fall within the scope of the present invention, and the protection scope of the present invention shall be subject only to the claims.

Claims

1. A green building performance analysis method based on BIM and AI big model, characterized in that: include: Construct a three-dimensional information model of the building based on BIM technology; Performing performance simulation on the three-dimensional information model to obtain performance simulation data of the building; Using an AI big model to conduct deep learning and processing on the performance simulation data to obtain building performance analysis results; Building performance optimization suggestions are generated and displayed based on the building performance analysis results.

2. The green building performance analysis method based on BIM and AI big model according to claim 1 is characterized in that: The three-dimensional information model of the building constructed based on BIM technology specifically includes: Obtain information data on the entire life cycle of a building; A three-dimensional BIM model of the building is constructed based on the full life cycle information data.

3. The green building performance analysis method based on BIM and AI big model according to claim 2 is characterized in that: The full life cycle information data includes engineering drawings, material lists and equipment information data of the building.

4. The green building performance analysis method based on BIM and AI big model according to claim 1 is characterized in that: The performing performance simulation on the three-dimensional information model to obtain performance simulation data of the building comprises: The thermal environment, light environment and acoustic environment of the three-dimensional information model are simulated and calculated according to preset environmental parameters and building parameters to obtain thermal environment simulation data, light environment simulation data and acoustic environment simulation data of the building under different environmental parameters and building parameters.

5. The green building performance analysis method based on BIM and AI big model according to claim 4 is characterized in that: The AI ​​big model is used to perform deep learning and processing on the performance simulation data to obtain the building performance analysis results including: Performing data cleaning and preprocessing on the performance simulation data to obtain processed data of the performance simulation data; Using an AI big model to extract features from the processed data to obtain building performance feature data; Training the AI ​​big model based on the building performance characteristic data and outputting building performance analysis results; The AI ​​large model is adaptively adjusted and optimized according to the actual environment and input conditions to output actual building performance analysis results.

6. The green building performance analysis method based on BIM and AI big model according to claim 5 is characterized in that: The building performance analysis results include building energy consumption analysis results, thermal performance analysis results, ventilation performance analysis results, renewable energy utilization analysis results and equipment system energy efficiency analysis results.

7. The green building performance analysis method based on BIM and AI big model according to claim 6 is characterized in that: Generating and displaying building performance optimization suggestions based on the building performance analysis results includes: Generate corresponding performance optimization suggestions according to the building energy consumption analysis results, the thermal performance analysis results, the ventilation performance analysis results, the renewable energy utilization analysis results and the equipment system energy efficiency analysis results; The building energy consumption analysis results, the thermal performance analysis results, the ventilation performance analysis results, the renewable energy utilization analysis results, the equipment system energy efficiency analysis results and the corresponding performance optimization suggestions are visualized in the form of analysis reports and charts.

8. The green building performance analysis method based on BIM and AI big model according to claim 7 is characterized in that: The performance optimization suggestions include: improvement plans for building design, optimization plans for material selection, and plans for improving energy efficiency.

9. A green building performance analysis system based on BIM and AI big model, characterized in that: include: Data acquisition and modeling module, used to build a three-dimensional information model of the building based on BIM technology; A building performance simulation module, used to perform performance simulation on the three-dimensional information model to obtain performance simulation data of the building; An AI large model processing module is used to use the AI ​​large model to perform deep learning and processing on the performance simulation data to obtain a building performance analysis result; The optimization suggestion output module is used to generate and display building performance optimization suggestions based on the building performance analysis results.

10. The green building performance analysis system based on BIM and AI big model according to claim 9 is characterized in that: The data acquisition and modeling module, the building performance simulation module, the AI ​​large model processing module and the optimization suggestion output module perform data transmission and collaboration through a data interface and a computer program.

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