Clean room design system based on parameterized design and intelligent cooperation

Abstract

The invention relates to a clean room design system based on parameterization design and intelligent collaboration, and relates to the technical field of air purification and environment control, the system adopts a cloud native architecture, and mainly comprises a parameterization design module, a collaborative design platform and clean room performance simulation and optimization software. The parameterization design module decomposes the clean room design into reusable design modules, flexible calling and combination are supported, and the design efficiency is improved; the collaborative design platform utilizes an intelligent collaborative design algorithm to detect data conflicts in real time and provide intelligent design suggestions, and efficient collaboration of multidisciplinary experts is promoted; the performance simulation and optimization software comprehensively analyzes the cleanliness, the temperature, the humidity and the air distribution, and optimizes the design scheme according to the simulation result; according to the system, the design quality and the operation efficiency of the clean room are remarkably improved, the construction, operation and maintenance cost is reduced, and the requirement for high-standard clean room design is met.

Description

Technical Field

[0001] The present application relates to the technical field of air purification and environmental control, and in particular to a clean room design system based on parametric design and intelligent collaboration. Background Art

[0002] Currently, the design and operation of clean rooms have been widely used in industries such as semiconductor manufacturing, biopharmaceuticals, and precision instrument assembly. With the continuous improvement of requirements for product quality and production environment, traditional clean room design has gradually exposed many problems, such as uneven air flow, difficulty in accurately controlling temperature and humidity, and high energy consumption. These problems not only affect product quality and output, but also increase the operating costs of enterprises. Therefore, how to improve the performance of clean rooms through scientific and reasonable design has become a research hotspot in the industry.

[0003] At present, common cleanroom design schemes mainly include two airflow organization forms: vertical unidirectional flow and non-unidirectional flow. Among them, vertical unidirectional flow is usually used in occasions requiring extremely high cleanliness. Its characteristic is that the airflow flows from the top to the bottom, forming a laminar flow effect, which can effectively reduce the deposition of particulate matter. However, this airflow organization form has high requirements for equipment, and the construction and operation and maintenance costs are relatively high. On the other hand, non-unidirectional flow is more flexible and can achieve different airflow distributions by adjusting the position of the supply and return air outlets, but its cleaning effect is relatively poor, especially under high cleanliness requirements.

[0004] In addition, existing temperature and humidity control systems mostly rely on traditional sensors and controllers, which are unable to accurately adjust indoor temperature and humidity in real time, resulting in low comfort and energy efficiency. For pressure control, although studies have shown that an appropriate positive pressure gradient can effectively prevent external pollutants from entering the clean room, in actual applications, due to the lack of effective monitoring and control methods, pressure fluctuations often occur, affecting the overall performance of the clean room. Summary of the Invention

[0005] The purpose of this application is to provide a clean room design system based on parametric design and intelligent collaboration to solve the problems existing in the existing clean room design and improve the design efficiency and performance, thereby realizing efficient and flexible clean room design.

[0006] The present application provides a cleanroom design system based on parametric design and intelligent collaboration, which is built using a cloud-native architecture to achieve elastic expansion and on-demand allocation of resources. The system includes a parametric design module, a collaborative design platform, and cleanroom performance simulation and optimization software. The parametric design module decomposes the cleanroom design into multiple reusable design modules, each of which has clear input parameters and output parameters. The input parameters include, but are not limited to, dimensions, material type, equipment type, and air flow rate, and the output parameters include, but are not limited to, design drawings, bills of materials, equipment configuration tables, and energy consumption assessment reports. Parametric design tools (such as Revit and Grasshopper) are used to associate various cleanroom design elements with their related parameters, supporting rapid modification and adjustment. The collaborative design platform uses intelligent collaborative design algorithms to automatically detect and resolve data conflicts and version inconsistencies, and provides intelligent design suggestions and optimization solutions based on the designer's behavior patterns and learning experience. It also provides a real-time feedback mechanism, allowing designers to interact and modify design solutions. The clean room performance simulation and optimization software can simulate and analyze the cleanliness, temperature and humidity, airflow organization and other performance of the clean room, and optimize the design scheme based on the simulation results.

[0007] Preferably, the implementation of the intelligent collaborative design algorithm mainly includes the following technical frameworks and tools: Machine Learning Framework: Use machine learning frameworks such as TensorFlow and PyTorch to build behavioral pattern recognition and prediction models; Data analysis tools: Use data analysis libraries such as Pandas and NumPy to process and analyze collected data and extract useful information; Rule Engine: Use rule engines such as Drools to automate conflict detection and resolution strategies; Optimization algorithm: Apply optimization algorithms such as genetic algorithm and simulated annealing to find the best configuration in the design scheme; Cloud computing platform: elastically scales algorithms through cloud services (such as AWS and Azure), supporting large-scale data processing and real-time collaboration.

[0008] Preferably, the design module supports rapid calling and combining of standardized design component libraries according to actual needs, including but not limited to: Wall module: defines the thickness, material, insulation performance, etc. of the wall; Door and window module: configure the type, size, opening method, etc. of doors and windows; Clean bench module: specifies the configuration, materials and functions of the workbench; Filter module: Select appropriate filter type, size, and performance parameters.

[0009] Preferably, the collaborative design platform is used for information sharing between experts in different disciplines, and the collaborative design platform includes: allowing design team members to edit and feedback on design schemes in real time to ensure the comprehensiveness and feasibility of the design scheme; providing version control function to track the historical changes of design documents, ensuring that the team uses the latest design information.

[0010] Preferably, the design module includes: Structure module: Define the basic structure of the clean room, including the geometric parameters of the walls, ceiling, floor, etc. Equipment module: Contains the equipment parameters required in the clean room, such as air filters, temperature control equipment, and their arrangement; Material module: Specifies the material properties used in the clean room, including corrosion resistance, thermal insulation, easy cleaning, etc.

[0011] Preferably, the collaborative design platform supports online collaboration of multiple people, realizes real-time sharing and updating of design information, and ensures that all participants can access the latest information; ensures the security and integrity of design data, uses encryption technology and permission management mechanism to prevent unauthorized access.

[0012] Preferably, the system has data sharing and integration functions, which can realize seamless connection and conversion of CAD drawings, BIM models and performance analysis data, ensure the compatibility of data between different design tools, and realize data format standardization, support multiple file formats import and export, and facilitate integration with other systems.

[0013] Preferably, the system applies BIM technology to realize the digitization, modeling and integration of design information, specifically including: supporting the creation and visualization of three-dimensional models to improve the intuitiveness of design; providing dynamic updates of design parameters to ensure information consistency during the design process to improve the accuracy and reliability of design.

[0014] Preferably, the clean room performance simulation and optimization software can perform multi-dimensional performance evaluation on different design schemes, including but not limited to simulation analysis of cleanliness, temperature and humidity, and air flow organization; generate detailed performance evaluation reports and provide specific suggestions for design improvement.

[0015] Preferably, the system supports adaptive learning mechanism based on historical data and user behavior, and the specific functions include: analyzing user design behavior and preferences to optimize the relevance of design suggestions; regularly update machine learning models to improve the accuracy and efficiency of intelligent collaborative design algorithms.

[0016] Preferably, the system provides a user-friendly interface, specifically including: an intuitive operation interface design, facilitating the interaction between the designer and the system; a function supporting rapid iteration and optimization, allowing users to easily compare and select design schemes.

[0017] In summary, the present application includes at least one of the following beneficial technical effects: 1. The introduction of the parametric design module enables the design of the clean room to be divided into multiple reusable modules, and the designer can quickly adjust and combine design elements according to project requirements, effectively responding to specific requirements of different customers and industries, and improving design efficiency; 2. Through intelligent collaborative design algorithms, the system can monitor and analyze the airflow organization form in real time: such as vertical unidirectional flow and non-unidirectional flow, and optimize the design according to the actual situation, which helps to improve the uniformity of air flow and ensure effective control under different cleanliness requirements, thereby improving product quality and yield; 3. The clean room performance simulation and optimization software of the system can perform real-time simulation analysis on temperature and humidity, and provide accurate control recommendations. By integrating advanced sensors and control technology, the problem of inaccurate temperature and humidity control in traditional systems is solved, improving comfort and energy efficiency, and reducing operating costs; 4. Using cloud-native architecture and data sharing and integration functions, the system can achieve efficient use and elastic expansion of resources, reducing the construction and operation and maintenance costs of infrastructure. In addition, the standardized component library of parametric design reduces repetitive labor in design and construction, and improves the delivery speed of projects; 5. The implementation of the collaborative design platform enables experts from different disciplines to share information and design schemes in real time, achieving efficient collaboration and ensuring the comprehensiveness and feasibility of the design scheme, thereby reducing design errors caused by information asymmetry; 6. Through real-time monitoring and control means, the system can ensure the positive pressure gradient in the clean room, prevent the invasion of external pollutants, and effectively alleviate the problem of pressure fluctuations in traditional design, improving the overall performance of the clean room. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is the implementation process flowchart of the present application. DETAILED DESCRIPTION

[0019] The following will be explained in detail in combination with the accompanying Figure 1 Further detailed description of the present application.

[0020] The present application provides a cleanroom design system based on parametric design and intelligent collaboration, which is built using a cloud-native architecture to achieve elastic expansion and on-demand allocation of resources. The system includes a parametric design module, a collaborative design platform, and cleanroom performance simulation and optimization software. The parametric design module decomposes the cleanroom design into multiple reusable design modules. Each design module has clear input parameters and output parameters. The input parameters include but are not limited to: size, material type, equipment type, and air flow rate. The output parameters include but are not limited to: design drawings, bill of materials, equipment configuration table, and energy consumption assessment report. Parametric design tools (such as Revit, Grasshopper, etc.) are used to associate various cleanroom design elements with their related parameters, supporting rapid modification and adjustment. The collaborative design platform uses intelligent collaborative design algorithms to automatically detect and resolve data conflicts and version inconsistencies. It also provides intelligent design suggestions and optimization solutions based on the designer's behavior patterns and learning experience, and provides a real-time feedback mechanism, allowing designers to interact and modify design solutions. Cleanroom performance simulation and optimization software can simulate and analyze the cleanroom's cleanliness, temperature and humidity, airflow organization and other performance, and optimize the design scheme based on the simulation results.

[0021] The implementation of intelligent collaborative design algorithms mainly includes the following technical frameworks and tools: Machine Learning Framework: Use machine learning frameworks such as TensorFlow and PyTorch to build behavioral pattern recognition and prediction models; Data analysis tools: Use data analysis libraries such as Pandas and NumPy to process and analyze collected data and extract useful information; Rule Engine: Use rule engines such as Drools to automate conflict detection and resolution strategies; Optimization algorithm: Apply optimization algorithms such as genetic algorithm and simulated annealing to find the best configuration in the design scheme; Cloud computing platform: elastically scales algorithms through cloud services (such as AWS and Azure), supporting large-scale data processing and real-time collaboration; The intelligent collaborative design algorithm works as follows: Data collection and preprocessing: Collect design documents, version history, user feedback, design parameters, and other data from the design team; clean and preprocess the collected data to ensure data accuracy and consistency; Behavioral pattern recognition: By analyzing historical data, we can identify common behavioral patterns of designers, including design habits, modification frequency, and feedback responses. We can also build user behavior models to predict the possible actions of designers in specific situations. Conflict detection and resolution: During the design process, the dependencies between different design modules are monitored in real time, automatically detecting data conflicts (such as when the same parameter is modified by different team members). Conflicts are automatically resolved based on priority and version control strategies, and notifications are sent to relevant designers to ensure information transparency. Intelligent suggestions and optimization: Based on historical data and current design status, intelligent design suggestions are provided to designers, such as parameter adjustment and design solution optimization. Optimization algorithms (such as genetic algorithms and simulated annealing) are used to conduct multi-dimensional evaluations of design solutions and provide improvement suggestions. Continuous learning and iteration: The system continuously updates and optimizes the behavioral model through user feedback and new design data, improving the accuracy and efficiency of the algorithm. The adaptive learning mechanism enables the algorithm to be adjusted according to the characteristics of different projects and teams.

[0022] The design module supports the rapid calling and combination of standardized design component libraries according to actual needs, including but not limited to: Wall module: defines the thickness, material, insulation performance, etc. of the wall; Door and window module: configure the type, size, opening method, etc. of doors and windows; Clean bench module: specifies the configuration, materials and functions of the workbench; Filter module: Select the appropriate filter type, specifications and performance parameters.

[0023] Among them, the collaborative design platform is used for information sharing among experts from different disciplines. The collaborative design platform includes: allowing design team members to edit and provide feedback on design plans in real time to ensure the comprehensiveness and feasibility of the design plans; providing version control functions to track historical changes in design documents to ensure that the team uses the latest design information.

[0024] The design module includes: Structure module: defines the basic structure of the clean room, including geometric parameters of walls, ceilings, floors, etc. Equipment module: contains the equipment parameters required in the clean room, such as air filters, temperature control equipment and their layout; Materials Module: Specifies the properties of materials used in cleanrooms, including corrosion resistance, thermal insulation, ease of cleaning, etc.

[0025] Among them, the collaborative design platform supports multi-person online collaboration, realizes real-time sharing and updating of design information, and ensures that all participants have access to the latest information; it guarantees the security and integrity of design data, and adopts encryption technology and permission management mechanisms to prevent unauthorized access.

[0026] Among them, the system has data sharing and integration functions, which can realize the seamless connection and conversion of CAD drawings, BIM models and performance analysis data, ensure the compatibility of data between different design tools, and realize data format standardization, support the import and export of multiple file formats, and facilitate integration with other systems.

[0027] Among them, the system applies BIM technology to realize the digitization, modeling and integration of design information, including: supporting the creation and visualization of three-dimensional models to improve the intuitiveness of design; providing dynamic updates of design parameters to ensure information consistency during the design process, so as to improve the accuracy and reliability of design.

[0028] Among them, the clean room performance simulation and optimization software can conduct multi-dimensional performance evaluation for different design schemes, including but not limited to simulation analysis of cleanliness, temperature and humidity, and airflow organization; generate detailed performance evaluation reports, and provide specific suggestions for design improvements.

[0029] Among them, the system supports an adaptive learning mechanism based on historical data and user behavior. Specific functions include: analyzing user design behavior and preferences to optimize the relevance of design suggestions; regularly updating machine learning models to improve the accuracy and efficiency of intelligent collaborative design algorithms.

[0030] The system provides a user-friendly interface, including an intuitive operating interface design that facilitates interaction between designers and the system; and supports rapid iteration and optimization functions, allowing users to easily compare and select design options.

[0031] The implementation process of this application is as follows: 1. Demand analysis and planning Goal setting: Identify the cleanroom's intended use, such as semiconductor manufacturing or biopharmaceuticals, and set specific goals for cleanliness level, temperature and humidity range, airflow rate, etc., based on industry standards and customer needs; Data collection: Collect design specifications, standards, and best practices from relevant industries, analyze existing cleanroom design cases on the market, and identify deficiencies in current designs; Teamwork: Organize the design team to conduct brainstorming and invite experts from different fields, such as environmental engineers, mechanical engineers, architects, etc., to participate in the discussion to ensure the comprehensiveness of the design plan; 2. Build a BIM model Select BIM tools: Choose appropriate BIM software, such as Revit, Archicad, etc., and configure the software environment according to project requirements; Build a 3D model: Structural elements: Model the structural parts of the cleanroom, including walls, floors, and ceilings, and define material properties such as thermal insulation and corrosion resistance.

[0032] Equipment layout: Rationally arrange HVAC systems, filters, clean benches and other equipment in the model to ensure they meet design requirements; Parametric settings: Define input parameters (such as size, material type, etc.) and output parameters (such as performance indicators, cost estimates, etc.) for each design element to achieve dynamic adjustment; 3. Performance analysis and optimization Simulation software application: Use computational fluid dynamics (CFD) software, such as ANSYS Fluent and COMSOL Multiphysics, to simulate airflow distribution and analyze the airflow organization in the clean room; Temperature and humidity simulation: Use thermodynamic simulation tools to simulate temperature and humidity changes in the clean room and evaluate the impact of different design options on environmental control; Optimization algorithm: Apply intelligent algorithms such as genetic algorithm and particle swarm optimization to iteratively optimize design parameters based on simulation results to find the best design solution; 4. Collaborative design and real-time updates Build a collaborative platform: Choose appropriate collaborative design tools, such as Autodesk BIM 360 and Microsoft Teams, to ensure that team members can access and edit design files; Real-time feedback mechanism: Design review: hold regular design review meetings, invite team members to evaluate the current design plan, collect feedback and record it; Version control: Implement strict version control to ensure that all design changes are recorded and traceable to the specific designer and time; 5. Data Management and Integration Data standardization: defining data standards and formats to ensure consistency across CAD drawings, BIM models, and performance analysis data; Integration process: Data import and export: realize seamless data exchange between different software, ensuring smooth transmission of design information at all stages; Data storage and security: Choose a secure cloud storage solution to protect the security of design data and ensure that only authorized personnel can access sensitive information; 6. Construction simulation and management Construction plan development: After the design is completed, a detailed construction plan is developed, including timetable, resource allocation and risk assessment; Construction progress monitoring: Real-time monitoring of construction progress is achieved through a collaborative platform, and comparison with the design plan is performed to ensure consistency during implementation; 7. Operation and Maintenance Handover and training: After the project is completed, the BIM model and related documents will be handed over to the operations team, and they will be trained to ensure that they can effectively manage and maintain the cleanroom; Continuous monitoring: Install a real-time monitoring system to regularly evaluate the cleanroom’s performance and make necessary adjustments and optimizations based on the feedback data.

[0033] Example 1: Semiconductor Manufacturing Clean Room Design 1. Demand analysis and planning Goal setting: Design a clean room for a semiconductor manufacturing company to achieve ISO Class 5 cleanliness, requiring temperature to be controlled at 20±2°C and humidity to be controlled at 40%±5%.

[0034] Data collection: Collect relevant industry standards, analyze existing semiconductor cleanroom design cases, and identify problems such as uneven air flow and inaccurate temperature and humidity control.

[0035] 2. Build a BIM model Select BIM tools: Use Revit software for model construction.

[0036] Build a 3D model: Structural elements: Model walls, floors, and ceilings, select appropriate antistatic materials, and ensure the cleanroom structure complies with ISO standards.

[0037] Equipment layout: Rationally arrange the HVAC system, HEPA filters, clean workbenches, and other equipment to ensure that airflow flows from top to bottom, forming a laminar flow effect.

[0038] Parameterized settings: Define input parameters (wall thickness, filter specifications) and output parameters (energy consumption estimation, cleanliness assessment) for each module.

[0039] 3. Performance analysis and optimization Simulation software application: Use ANSYS Fluent to simulate airflow distribution and analyze the airflow organization in the clean room.

[0040] Temperature and humidity simulation: COMSOL Multiphysics was used to simulate the temperature and humidity changes of different design schemes and evaluate their impact on cleanliness.

[0041] Optimization algorithm: Genetic algorithm is applied to optimize design parameters to ensure uniform airflow distribution and meet design requirements.

[0042] 4. Collaborative design and real-time updates Build a collaborative platform: Use Autodesk BIM 360 to ensure that team members share design information in real time.

[0043] Real-time feedback mechanism: Design review: Hold design review meetings regularly and invite relevant experts to provide feedback.

[0044] Version Control: Implement strict version control to record all design changes.

[0045] 5. Data Management and Integration Data standardization: Define data standards for CAD drawings and BIM models to ensure consistency.

[0046] Integration process: Realize seamless data exchange between CAD drawings and BIM models to ensure smooth information transmission.

[0047] Choose a secure cloud storage solution to protect the security of your design data.

[0048] 6. Construction simulation and management Construction plan development: Develop a detailed construction schedule and resource allocation plan.

[0049] Construction progress monitoring: Real-time monitoring of construction progress to ensure consistency with the design plan.

[0050] 7. Operation and Maintenance Handover and training: Hand over the BIM model and operation manual to the operation team and conduct training.

[0051] Continuous Monitoring: Install a real-time monitoring system to regularly evaluate cleanroom performance and make necessary adjustments.

[0052] Example 2: Biopharmaceutical Cleanroom Design 1. Demand analysis and planning Goal setting: Design a cleanroom for a biopharmaceutical factory, requiring ISO Class 7 cleanliness, temperature control at 22±2°C, and humidity control at 50%±5%.

[0053] Data Collection: Analyze existing biopharmaceutical cleanroom design cases to identify issues with inaccurate temperature and humidity control and high energy consumption in current designs.

[0054] 2. Build a BIM model Choose BIM tools: Use Archicad for model building.

[0055] Build a 3D model: Structural elements: Model walls and floors, select corrosion-resistant materials, and ensure the cleanroom structure meets biopharmaceutical industry standards.

[0056] Equipment Layout: Arrange HVAC systems, humidity control equipment, and clean benches to ensure optimal airflow distribution through reasonable supply and return air vent locations.

[0057] Parameterization: Define input parameters (device power, material properties) and output parameters (energy consumption, maintenance intervals) for each component.

[0058] 3. Performance analysis and optimization Simulation software application: Use CFD software to simulate airflow distribution and temperature and humidity changes.

[0059] Optimization algorithm: Use particle swarm optimization algorithm to evaluate the performance of different design schemes and optimize airflow and temperature and humidity control.

[0060] 4. Collaborative design and real-time updates Build a collaborative platform: Use Microsoft Teams to ensure that design team members can communicate and share files in real time.

[0061] Real-time feedback mechanism: Design review: Organize multidisciplinary experts to conduct design reviews for collective discussion and feedback.

[0062] Version control: Implement effective version control to ensure that design documents are updated and recorded.

[0063] 5. Data Management and Integration Data standardization: Develop data standards to ensure consistency across CAD drawings, BIM models, and performance analysis data.

[0064] Integration process: Realize data exchange between different design tools to ensure seamless transfer of information.

[0065] Back up data regularly to ensure the security of design information.

[0066] 6. Construction simulation and management Construction plan formulation: Develop a detailed construction plan and clarify the time nodes and responsibilities of each stage.

[0067] Construction progress monitoring: Track construction progress in real time through the collaborative platform to ensure consistency with the design plan.

[0068] 7. Operation and Maintenance Handover and training: After the project is completed, the BIM model and operation manual will be handed over to the operation team, and they will receive systematic training.

[0069] Continuous monitoring: Install a monitoring system to regularly evaluate the cleanroom’s performance indicators and make adjustments and optimizations based on the feedback.

[0070] The examples of this specific embodiment are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, any equivalent changes made based on the structure, shape, and principle of this application should be included in the scope of protection of this application.

Claims

1. A clean room design system based on parametric design and intelligent collaboration, characterized by: Built using a cloud-native architecture to achieve elastic expansion and on-demand allocation of resources, the system includes a parametric design module, a collaborative design platform, and cleanroom performance simulation and optimization software. The parametric design module decomposes the cleanroom design into multiple reusable design modules. Each design module has clear input parameters and output parameters. The input parameters include dimensions and material types, and the output parameters include design drawings and bills of materials. The parametric design tool is used to associate various cleanroom design elements with their relevant parameters. The collaborative design platform uses intelligent collaborative design algorithms to automatically detect and resolve data conflicts and version inconsistencies, and provides intelligent design suggestions and optimization solutions based on designers' behavior patterns and learning experience; The clean room performance simulation and optimization software can simulate and analyze the cleanliness, temperature and humidity, airflow organization and other performance of the clean room, and optimize the design scheme based on the simulation results.

2. A clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The design module supports the rapid calling and combination of standardized design component libraries according to actual needs, including but not limited to parametric models of walls, doors and windows, clean workbenches and filters.

3. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The collaborative design platform is used for information sharing among experts from different disciplines. The collaborative design platform includes: allowing design team members to edit and provide feedback on design plans in real time to ensure the comprehensiveness and feasibility of the design plans, and providing version control functions to track historical changes to design documents to ensure that the team uses the latest design information.

4. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The design module includes: Structure module: defines the basic structure of the clean room; Equipment module: contains the equipment parameters required in the clean room; Materials Module: Specifies the properties of materials used in cleanrooms.

5. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The collaborative design platform supports multi-person online collaboration, realizes real-time sharing and updating of design information, and ensures the security and integrity of design data.

6. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The system has data sharing and integration functions, and can achieve seamless connection and conversion of CAD drawings, BIM models and performance analysis data.

7. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The system applies BIM technology to realize the digitization, modeling and integration of design information to improve the accuracy and reliability of design.

8. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The cleanroom performance simulation and optimization software can perform multi-dimensional performance evaluation for different design schemes, including but not limited to simulation analysis of cleanliness, temperature and humidity, and airflow organization.

9. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The system supports an adaptive learning mechanism based on historical data and user behavior to improve the accuracy and efficiency of the intelligent collaborative design algorithm.

10. The clean room design system based on parametric design and intelligent collaboration according to claim 1, characterized in that: The system provides a user-friendly interface to facilitate interaction between designers and the system, thereby achieving rapid iteration and optimization of design solutions.

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