Module building intelligent design and simulation system based on digital twinning

Abstract

The invention discloses a modular building intelligent design and simulation system based on digital twinning, particularly relates to the technical field of building intelligent design, and comprises a digital twinning modeling module, a stress analysis and load prediction module, a scene adaptation optimization module, a module connection structure adjustment module and a collaborative design and data sharing module. The system performs dynamic simulation and load prediction and optimizes building design by acquiring a building geometric model, material attributes, environmental data and component stress data in real time, and realizes real-time cooperation and feedback among design teams through a data sharing platform, so that the structural safety and performance of a building under various environmental conditions are ensured. According to the method, through real-time dynamic data calibration and environment simulation, the precision and adaptability of building design are remarkably improved, the problems of load change and structural stress in a complex environment can be effectively solved, the design efficiency is improved, potential safety hazards are reduced, the overall performance and safety of a building are ensured, and the method is suitable for popularization and application. And the full-life-cycle management of the building is optimized.

Description

Technical Field

[0001] This invention relates to the field of intelligent building design technology, and in particular to a modular intelligent building design and simulation system based on digital twins. Background Technology

[0002] The field of intelligent building design technology encompasses systematic approaches to building design, construction, and operation management, utilizing modern computer, automation, and information technologies. This area integrates information technology with the planning, design, construction, use, and management processes of buildings, driving the intelligent development of the construction industry. Core content includes the digitization and intelligentization of each stage of the building lifecycle, particularly in the design phase, where technologies such as computer-aided design (CAD), building information modeling (BIM), and virtual simulation are used to improve design accuracy and efficiency, and to achieve data tracking and control during construction. Furthermore, this field also covers intelligent building operation management, energy optimization, and environmental control, aiming to achieve full lifecycle management and optimization of buildings.

[0003] Among them, the modular building intelligent design and simulation system based on digital twins refers to the virtualization and intelligent management of the design and simulation process of building modules through digital twin technology. This technology covers intelligent design, dynamic simulation, and collaborative optimization of building modules. Specifically, the system creates digital twin models of building modules and uses these models to conduct performance simulation and optimization decisions during the building design process. It supports designers in accurately simulating various aspects of a building in a virtual environment, including structural, functional, and environmental factors. Simultaneously, the system supports integration with multiple design platforms, enabling cross-platform data sharing and collaborative work, ensuring coordination and feasibility between different modules. Through this system, architectural designers can perform design verification, risk assessment, and resource allocation optimization based on virtual models.

[0004] In current technologies, the digitalization and intelligentization of architectural design mainly rely on traditional Building Information Modeling (BIM) and Computer-Aided Design (CAD) technologies. While these technologies have improved design efficiency to some extent, they still have many shortcomings in dynamic environments. Existing systems typically lack real-time responses to environmental changes, and architectural designs often fail to reflect the impact of environmental factors such as wind speed, temperature, and earthquakes on building structures in real time. Therefore, in practical applications, architectural designs often rely on static assumptions for load prediction and stress calculation, potentially leading to structural defects that may be exposed during actual use, making them unable to effectively cope with stress changes in complex environments. For example, in the event of rapid changes in wind speed or temperature, existing systems cannot adjust the design in real time, which may result in uneven structural stress or stress concentration, increasing safety hazards. Furthermore, in current technologies, design teams often operate independently, lacking a unified data sharing platform. Feedback on design parameters and modification suggestions is often delayed and inaccurate, making collaborative work difficult and hindering real-time information updates and synchronized adjustments across departments and platforms. This information silo effect often prevents errors and optimization opportunities in the design from being detected and corrected in a timely manner, thus affecting the final quality and safety of the building. Summary of the Invention

[0005] The main objective of this invention is to provide a modular building intelligent design and simulation system based on digital twins. By introducing dynamic data calibration, real-time environmental simulation, and optimized design of modular connection components, it solves the safety hazards and efficiency problems in existing building designs, such as the inability to respond to environmental changes in real time and the lack of collaborative design and optimization.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A modular building intelligent design and simulation system based on digital twins, the system comprising: Digital twin modeling module: acquires geometric model, material properties, and environmental factor data of building structure, collects displacement and stress data of building components, performs digital twin modeling, calibrates the model based on dynamic data, adjusts geometric shape and stress state, calls environmental data for simulation, simulates load conditions, calculates and generates load prediction values ​​of digital twin model; Stress Analysis and Load Prediction Module: Based on the load prediction values ​​of the digital twin model, it analyzes the stress distribution of the building under different working conditions, identifies the module connection components with large loads, filters the areas with large loads based on the stress change trend, and calculates the stress prediction values ​​of overloaded components. Scene adaptation optimization module: Based on the innovative design of the module connection structure, it obtains wind load and seismic environmental conditions, monitors the stress changes of the connection components, and calculates and generates scene adaptability correction coefficients; Module connection structure adjustment module: Based on the scene adaptability correction coefficient, adjust the material thickness and connection method of the module connection components, calculate the optimized stress response, and generate the optimized stress value of the connection components; Collaborative Design and Data Sharing Module: Based on the data from the aforementioned modules, data sharing and collaboration are achieved through a cloud platform, allowing for real-time data access and design optimization, and generating collaborative design work reports.

[0007] Preferably, the digital twin modeling module includes: Geometric Model Acquisition Submodule: Acquires geometric model data of building structure, collects material properties and environmental factor data of building components, collects displacement and stress data through sensors, generates basic model, acquires original geometric feature data, and generates initial geometric feature values; Data dynamic calibration submodule: Based on the initial geometric feature values, it calls the dynamic data collected in real time to adjust the geometric shape and stress state of building components, calibrate the model, obtain error data, analyze the differences between the model before and after calibration, and generate geometric shape calibration values. Load simulation analysis submodule: Based on the geometric calibration value, it calls environmental data to perform simulation, simulates the building's performance under different loads, calculates the load response under different working conditions, and generates and calculates the load prediction value of the digital twin model.

[0008] Preferably, the original geometric feature data includes the geometric dimensions, shape, connection node information, component location, structural distribution, and material property description of each part of the building.

[0009] Preferably, the stress analysis and load prediction module includes: Load distribution analysis submodule: Based on the load prediction value of the digital twin model, it obtains the stress distribution data of the building under different working conditions, monitors the stress change trend of the module connection components, analyzes the stress value of each connection component, screens out areas that may have a large load, and generates stress distribution change values ​​based on stress changes and environmental influences. Overload identification submodule: Based on the stress distribution change value, it determines whether the load of the module connection component exceeds the set threshold, identifies the connection component with excessive load, and further filters out the overload area by analyzing the load trend and comparing the stress value of each component, and generates overload component identification value. Stress prediction calculation submodule: Based on the identified values ​​of overloaded components, calculate the stress response changes in the overloaded area, analyze the stress state of components under different load conditions, and perform prediction calculations in combination with preset stress thresholds to finally generate the predicted stress values ​​of overloaded components.

[0010] Preferably, the scene adaptation and optimization module includes: Environmental Condition Acquisition Submodule: Acquires real-time meteorological data and seismic fluctuation data, generates environmental condition datasets, records environmental change data in real time through monitoring equipment, and obtains environmental condition datasets; Stress Change Monitoring Submodule: Based on the environmental condition dataset, monitor the stress changes of the building module connection components under different environmental conditions, collect stress response data of each component, analyze stress change trends, screen out areas with large stress changes, and generate stress change trend data. Adaptive Correction Calculation Submodule: Based on stress change trend data, calculates the adaptive correction coefficient of building connection components under different environmental conditions, performs correction calculations based on set thresholds, optimizes and adjusts connection components with high stress, and generates scene adaptive correction system.

[0011] Preferably, the methods for optimizing and adjusting the connecting components with high stress include, but are not limited to, increasing connection points or modifying the connection method, or adjusting material parameters.

[0012] Preferably, the module connection structure adjustment module includes: Material parameter adjustment submodule: Based on the scene adaptability correction coefficient, obtain the material information of the connecting parts, adjust the material thickness of the connecting parts in the high stress area, analyze the impact of different material strengths on load distribution, increase the material strength or use stronger materials, calculate the adjusted material properties, and generate optimized material parameters; Connection method optimization submodule: Based on the optimized material parameters, adjust the connection method of the module connection components, add or change the position and number of connection points, modify the connection method, analyze the impact of the adjusted connection method on stress distribution, and generate optimized connection method data; Stress response calculation submodule: Based on the optimized connection method data, calculate the stress response of the adjusted module connection components, monitor the stress distribution changes, analyze the load distribution change trend, calculate the optimized stress response data, and generate the optimized connection component stress value.

[0013] Preferably, the method for modifying the connection method in the connection method optimization submodule is to add welding points or select different types of connectors.

[0014] Preferably, the collaborative design and data sharing module includes: Data sharing submodule: Based on the data from the aforementioned modules, real-time data from various design teams is shared through the cloud platform. Based on real-time environmental and structural data, synchronous updates are performed to generate a shared dataset. Design optimization submodule: Based on the shared dataset, each design team optimizes the design according to real-time load data and stress response data, modifies the design parameters of the connecting components, and makes real-time adjustments and feedback through the data sharing platform to generate optimized design suggestions; The work report generation submodule: Based on the optimized design suggestions, it organizes all design modifications, optimization data, stress distribution information and correction suggestions to generate a collaborative design work report. The report includes the work results of each design team, optimization suggestions and data update information, and is delivered to the construction unit as the final design document.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention significantly improves the reliability and safety of building structures during design and construction by introducing dynamic data calibration, real-time environmental data retrieval, and precise adjustment of modular connection components into architectural design. In traditional architectural design, structural safety assessment typically relies on static data, which cannot fully simulate the dynamic response of buildings under complex environments. However, by acquiring real-time environmental change data, such as wind speed, temperature, and seismic fluctuations, architectural design can perform real-time simulations based on this dynamic data, simulating stress changes in buildings under different load conditions, thereby providing more accurate load predictions and stress responses. This process does not simply rely on initial modeling but continuously corrects and updates the design model during actual operation, enabling the various components of the building to be optimally designed under changing environmental conditions, improving the building's adaptability to various working conditions. By identifying and optimizing overloaded areas, not only can structural overload and potential safety hazards be reduced, but the design parameters of building components, such as material thickness and connection methods, can also be adjusted according to actual stress changes, thereby improving the accuracy and efficiency of architectural design. Furthermore, the data sharing platform enables real-time collaboration and data synchronization among design teams, allowing each design stage to work collaboratively on a unified platform, providing timely feedback and design corrections, avoiding information silos and the accumulation of design errors, and providing a reliable guarantee for the overall performance optimization of the building. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the workflow of the present invention; Detailed Implementation

[0017] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the linguistic context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0018] It should be understood that the terms "system," "device," "unit," and / or "module" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other words can achieve the same purpose, they may be replaced by other expressions.

[0019] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0020] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0021] The following describes in detail, with reference to the accompanying drawings, the modular building intelligent design and simulation system based on digital twins provided in the embodiments of this specification.

[0022] like Figure 1 As shown, in some embodiments, the modular building intelligent design and simulation system based on digital twins can be executed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), and any combination thereof. In some embodiments, Figure 1 One or more operations in the flowchart of the digital twin-based modular building intelligent design and simulation system shown can be implemented by processing devices and / or terminal devices. For example, the digital twin-based modular building intelligent design and simulation system can be stored in a storage device in the form of computer programs and / or instructions, and invoked and / or executed by processing devices and / or terminal devices.

[0023] The present invention discloses a modular building intelligent design and simulation system based on digital twins, comprising: Digital twin modeling module: acquires geometric model, material properties, and environmental factor data of building structure, collects displacement and stress data of building components, performs digital twin modeling, calibrates the model based on dynamic data, adjusts geometric shape and stress state, calls environmental data for simulation, simulates load conditions, calculates and generates load prediction values ​​of digital twin model; Stress Analysis and Load Prediction Module: Based on the load prediction values ​​of the digital twin model, it analyzes the stress distribution of the building under different working conditions, identifies the module connection components with large loads, filters the areas with large loads based on the stress change trend, and calculates the stress prediction values ​​of overloaded components. Scene adaptation optimization module: Based on the innovative design of the module connection structure, it obtains wind load and seismic environmental conditions, monitors the stress changes of the connection components, and calculates and generates scene adaptability correction coefficients; Module connection structure adjustment module: Based on the scene adaptability correction coefficient, adjust the material thickness and connection method of the module connection components, calculate the optimized stress response, and generate the optimized stress value of the connection components; Collaborative Design and Data Sharing Module: Based on the data from the aforementioned modules, data sharing and collaboration are achieved through a cloud platform, allowing for real-time data access and design optimization, and generating collaborative design work reports.

[0024] Furthermore, in another embodiment of the present invention, the digital twin modeling module includes: Geometric Model Acquisition Submodule: Acquires geometric model data of building structure, collects material properties and environmental factor data of building components, collects displacement and stress data through sensors, generates basic model, acquires original geometric feature data, and generates initial geometric feature values; The geometric model acquisition submodule begins by acquiring geometric model data of the building structure. This data includes the geometric dimensions, shape, connection node information, component locations, structural distribution, and material property descriptions of each part of the building. For example, a floor of the building might have a rectangular structure 20 meters long and 10 meters wide, with reinforced concrete walls. Through real-time sensor acquisition, displacement and stress data of building components are obtained. For instance, a displacement sensor might record a displacement of 0.05 meters at a wall location, or sense a stress value of 300 Pa on a column. This data is used to generate the basic model. After the basic model is generated, the initial geometric feature values ​​include geometric information such as the building's length, width, and height, as well as the material and node information of the building components. Next, the data dynamic calibration submodule adjusts the building based on the initial geometric feature values ​​and the real-time acquired dynamic data. By using sensor data to reflect the stress and displacement changes of building components, the geometric shape and stress state of the building are adjusted. This process corrects the geometric model by calling real-time collected stress and displacement data. For example, if the collected stress value at a certain location is 300 Pa, the adjusted stress value should be 350 Pa according to the calibration algorithm. The error data reflects the difference before and after correction, generating a geometric calibration value. Based on this, the load simulation analysis submodule begins operation. It uses the geometric calibration value and calls environmental data (such as wind speed, air pressure, temperature, and other meteorological data) to simulate the building's performance under different loads. For example, if the environmental data indicates a wind speed of 50 m / s and a temperature change of 30 degrees Celsius, this data will be input into the simulation model to calculate the load response under different operating conditions, thereby generating a load prediction value for the digital twin model. During the calculation, by combining environmental conditions (such as wind load), the possible stress changes of the building under specific conditions are analyzed, ultimately obtaining the load prediction value. For instance, under a wind speed of 50 m / s, the model shows that a certain component experiences a pressure of 1000 Pa.

[0025] Data dynamic calibration submodule: Based on the initial geometric feature values, it calls the dynamic data collected in real time to adjust the geometric shape and stress state of building components, calibrate the model, obtain error data, analyze the differences between the model before and after calibration, and generate geometric shape calibration values. The execution process of the data dynamic calibration submodule begins with acquiring initial geometric feature values. Assuming a building component has initial geometric features of 5 meters long and 2 meters wide, made of steel, and sensors record stress data of 1000 Pa and displacement of 0.01 meters on this component, the module then uses real-time acquired dynamic data. The real-time monitored stress data is 1200 Pa, and the displacement is 0.02 meters. This dynamic data is used to adjust the geometry and stress state of the building component, thus correcting the model. By comparing the data with the initial data, the error is calculated as a stress difference of (1200 Pa - 1000 Pa) = 200 Pa, and the displacement error is 0.02 meters - 0.01 meters = 0.01 meters. The calibration calculation formula is then used to update the stress and geometry of the building component. The calibrated stress and displacement data are then used in the adjusted model output. Error analysis yields geometric calibration values. For example, assuming a building operates at a wind speed of 50 m / s and an ambient temperature of 35 degrees Celsius, the calibration results show a geometric change of 0.02 meters, and the corrected stress distribution differs from the original model by 200 Pa. Ultimately, based on these calibration values, the digital twin model obtains updated load predictions to more accurately simulate the building's behavior under specific environmental conditions.

[0026] Load simulation analysis submodule: Based on the geometric calibration value, it calls environmental data to perform simulation, simulates the building's performance under different loads, calculates the load response under different working conditions, and generates and calculates the load prediction value of the digital twin model.

[0027] Taking a building as an example, assuming a wind speed of 50 m / s, a seismic intensity of magnitude 5, and a temperature change of 30 degrees Celsius, this environmental data will be input into the load simulation model. In the model, the material properties and geometric data of various building components (such as walls, columns, and roof) have been calibrated. The load simulation will simulate the building's performance under different conditions based on this data. For example, under a simulated wind speed of 50 m / s, the load response value of the building's exterior walls might be 2000 Pa, and the load response of the roof might be 1500 Pa. Based on these simulation results, the load response data will generate load prediction values ​​for the digital twin model. For example, assuming a predicted load value of 1800 Pa for a certain component, it means that the maximum load on that component under the current environmental conditions should be 1800 Pa. Exceeding this predicted value may lead to structural deformation or damage to the building. Through this simulation analysis, important data can be provided for the safety assessment of buildings, ensuring that buildings can operate safely under different environmental loads.

[0028] In another embodiment of the present invention, the stress analysis and load prediction module includes: Load distribution analysis submodule: Based on the load prediction value of the digital twin model, it obtains the stress distribution data of the building under different working conditions, monitors the stress change trend of the module connection components, analyzes the stress value of each connection component, screens out areas that may have a large load, and generates stress distribution change values ​​based on stress changes and environmental influences. The load prediction value is a numerical value derived from the aforementioned geometrically calibrated building data, reflecting the building's response under specific environmental conditions. For example, assuming an environment with a wind speed of 50 m / s and an air temperature of 30 degrees Celsius, the model calculates a load prediction value of 1200 Pa for a certain component. Based on this, the system acquires stress distribution data of the building under different operating conditions. This stress distribution data is collected in real time by sensors on various components of the building; for example, the stress in a column under this load is 1000 Pa, while the stress on the roof is 1500 Pa. Next, the system monitors the stress change trends of the components connected to the monitoring module, analyzing the stress values ​​of each connected component by comparing the rate and magnitude of stress changes in different components. For example, if the monitored stress in a column changes from 1000 Pa to 1200 Pa (a change of 200 Pa), while the roof increases from 1500 Pa to 1800 Pa (a change of 300 Pa), the system will select the roof as the area with potentially higher load. Based on stress changes and environmental influences, the system uses this data to identify areas with significant stress variations. This process is based on the trend of stress changes; for example, when the wind speed increases to 60 m / s, the rate of stress change on the roof is more significant, thus identifying this area as a region with a high load. Finally, stress distribution variation values ​​are generated, which can be used for subsequent load identification and optimization adjustments.

[0029] Overload identification submodule: Based on the stress distribution change value, it determines whether the load of the module connection component exceeds the set threshold, identifies the connection component with excessive load, and further filters out the overload area by analyzing the load trend and comparing the stress value of each component, and generates overload component identification value. The overload identification submodule begins by acquiring stress distribution change values. Assuming the analysis above shows a stress change of 300 Pa in the roof area and 200 Pa in the columns, the system needs to determine if these stress changes exceed a set threshold. Assuming the threshold is set at 250 Pa, if the stress change in the roof significantly exceeds this threshold, the system will identify the roof as an overloaded connection component. Further analysis comparing the stress values ​​of each component with the load trend reveals a larger increase in roof stress from 1500 Pa to 1800 Pa, compared to a larger increase in column stress from 1000 Pa to 1200 Pa. Therefore, the roof is identified as an overloaded component. Finally, an overload identification value is generated. For example, the overload identification value for the roof component is 1800 Pa, exceeding the system's preset threshold, confirming the component as an overloaded area.

[0030] Stress prediction calculation submodule: Based on the identified values ​​of overloaded components, calculate the stress response changes in the overloaded area, analyze the stress state of components under different load conditions, and perform prediction calculations in combination with preset stress thresholds to finally generate the predicted stress values ​​of overloaded components.

[0031] The stress prediction calculation submodule begins its execution by identifying the overloaded component. Here, we assume the identified overloaded component is the roof, with a stress value of 1800 Pa. Next, the system analyzes the stress response changes under different load conditions based on the identified overloaded component value. Assuming the current environmental conditions are a wind speed of 60 m / s and a temperature of 35 degrees Celsius, with an increased wind load, the roof's load response will be affected by these new conditions. For example, assuming the roof's stress value increases from 1800 Pa to 2200 Pa under a wind speed of 60 m / s, this indicates a significant change in the stress state of the roof component under the new load conditions. The system then performs prediction calculations based on a preset stress threshold. Assuming the stress threshold is 2000 Pa, when the roof's stress value reaches 2200 Pa, the system will predict the potential structural damage or instability risk of the component. Therefore, through stress prediction calculations, the system ultimately generates a predicted stress value for the overloaded component. For instance, a predicted stress value of 2200 Pa for the roof indicates that the roof component has exceeded the preset safe stress threshold under a wind speed of 60 m / s.

[0032] In some embodiments of the present invention, the scene adaptation optimization module includes: Environmental Condition Acquisition Submodule: Acquires real-time meteorological data and seismic fluctuation data, generates environmental condition datasets, records environmental change data in real time through monitoring equipment, and obtains environmental condition datasets; The execution process of the scene adaptation and optimization module begins with the environmental condition acquisition submodule. This submodule collects meteorological and seismic fluctuation data through real-time monitoring equipment. The data includes wind speed, temperature, humidity, and seismic vibration, generating an environmental condition dataset. For example, assuming the environmental data collection results show a wind speed of 45 m / s, a temperature of 30 degrees Celsius, and a seismic fluctuation frequency of 5 Hz, this data is input into the subsequent analysis module. The system continuously records environmental change data through monitoring equipment. For instance, if the wind speed increases from 40 m / s to 45 m / s within two hours, the temperature rises from 28 degrees Celsius to 30 degrees Celsius, and the intensity of seismic fluctuations varies in different areas, this change data is updated in real-time to the environmental condition dataset, ensuring that the building's response can be tracked and adjusted. Through this dataset, the system can reflect the dynamic changes in the environmental conditions of the building's location, thus providing accurate input data for the subsequent stress change monitoring submodule.

[0033] Stress Change Monitoring Submodule: Based on the environmental condition dataset, monitor the stress changes of the building module connection components under different environmental conditions, collect stress response data of each component, analyze stress change trends, screen out areas with large stress changes, and generate stress change trend data. The stress change monitoring submodule begins by acquiring environmental condition datasets. The system uses this data to monitor stress changes in building module connection components under different environmental conditions. For example, as temperatures rise, the building structure may expand, causing changes in the stress values ​​of connection components. The system collects stress response data for each component in real time. If the stress value of a wall increases from 1200 Pa to 1500 Pa, and the stress value of another component increases from 1000 Pa to 1300 Pa, the system analyzes the stress change trend based on this data. During the analysis, the system compares the stress responses of different components, identifying areas with significant stress changes. For instance, if the stress change in a column is greater than 100 Pa, while the change in another component is smaller, the system considers the area where the column is located to have a larger stress change and selects this area as the target area for subsequent optimization adjustments. Finally, the system generates stress change trend data, reflecting the stress change amplitude of building components under different environmental conditions, for further adaptive correction calculations.

[0034] The adaptive correction calculation submodule calculates the adaptive correction coefficients for building connection components under different environmental conditions based on stress change trend data. It performs correction calculations based on set thresholds, optimizes and adjusts connection components with high stress, and generates scene adaptive correction coefficients. Methods for optimizing and adjusting connection components with high stress include, but are not limited to, adding connection points, modifying connection methods, and adjusting material parameters.

[0035] The adaptive correction calculation submodule first calculates the adaptive correction coefficients for building connection components under different environmental conditions based on stress change trend data. Assuming the system identifies significant stress changes in the column area based on the stress change trend data, requiring optimization, and assuming the stress change in this area exceeds 50 Pa (the set threshold is 50 Pa), the system will begin correction calculations. The optimization goal is to reduce the stress change in this area. The correction calculations consider adding connection points, modifying connection methods, or adjusting material parameters. For example, increasing the number of column connection points or changing the original single bolt connection to a stronger welded connection can increase the load-bearing capacity of the component and reduce stress changes. Assuming the stress change in the column decreases from 1500 Pa to 1200 Pa after correction, the system will generate new scene adaptive correction coefficients. Assuming the final correction coefficient is 1.2, it indicates that the optimized connection components can better adapt to stress changes under different environmental conditions, improving the safety and stability of the building.

[0036] In another embodiment of the present invention, the module connection structure adjustment module includes: Material parameter adjustment submodule: Based on the scene adaptability correction coefficient, obtain the material information of the connecting parts, adjust the material thickness of the connecting parts in the high stress area, analyze the impact of different material strengths on load distribution, increase the material strength or use stronger materials, calculate the adjusted material properties, and generate optimized material parameters; The execution process of the scene adaptation and optimization module begins with the environmental condition acquisition submodule, which is responsible for collecting real-time meteorological and seismic fluctuation data. Assuming a construction project records a wind speed of 45 m / s, a temperature of 28 degrees Celsius, and seismic fluctuation data for an area with an earthquake magnitude of 6.0, the collected environmental condition data will be recorded in real time and an environmental condition dataset will be generated. This dataset includes not only meteorological data (such as wind speed, temperature, and humidity) but also information on the intensity and frequency of seismic activity. By monitoring equipment recording environmental changes in real time, such as a temperature change at a location from 28 degrees to 30 degrees Celsius, or recording peak data of localized seismic activity, the generated environmental condition dataset will provide complete information about changes in the environment surrounding the building. This dataset can include multiple parameters such as wind speed change curves, temperature fluctuation trends, and seismic frequency, providing data support for subsequent stress change monitoring.

[0037] Connection method optimization submodule: Based on the optimized material parameters, adjust the connection method of the module connection components, add or change the position and number of connection points, modify the connection method by adding welding points or selecting different types of connectors, analyze the impact of the adjusted connection method on stress distribution, and generate optimized connection method data; The stress change monitoring submodule begins by acquiring an environmental condition dataset, which it uses to monitor stress changes in building module connection components under different environmental conditions. For example, when the wind speed increases from 45 m / s to 60 m / s, the stress response of the building's roof components may increase. In this case, the monitoring system collects stress data for the roof components under the new environmental conditions. Assuming the roof stress was 500 Pa under the original wind speed conditions and 700 Pa under the new wind speed conditions, by comparing the stress data under different environmental conditions and analyzing the stress change trend, the system can determine that the stress increase in the roof components is 200 Pa. Further analysis reveals that this trend is clearly correlated with wind speed changes. Areas with significant stress changes are further identified, such as the roof components where stress increases are particularly pronounced. Therefore, the system marks the roof components as areas with significant stress changes and generates stress change trend data. This data reflects the changes in the stress response of the building modules when environmental conditions change, providing a basis for subsequent adaptive correction calculations.

[0038] Stress response calculation submodule: Based on the optimized connection method data, calculate the stress response of the adjusted module connection components, monitor the stress distribution changes, analyze the load distribution change trend, calculate the optimized stress response data, and generate the optimized connection component stress value.

[0039] In the aforementioned stress change trend data, the stress of the roof component increased from 500 Pa to 700 Pa, a change of 200 Pa. Based on this data, the system calculates the adaptability correction coefficient for the building's connection components according to a preset threshold. Assuming the preset stress threshold is 600 Pa, the stress change of the roof component significantly exceeds this threshold, thus requiring correction calculation. The correction calculation process includes optimizing the connection method of the roof component, such as adding connection points or adjusting the connection method (e.g., adding welding points or replacing with stronger connector types). Simultaneously, based on areas with high stress, material parameters are adjusted, such as increasing the material thickness of the roof connection components or using stronger materials (e.g., switching to high-strength steel). Through these adjustments, the system generates the scenario adaptability correction coefficient for the roof component. For example, the correction coefficient might be set to 1.2, indicating that the roof component needs a 20% increase in structural strength to ensure it can adapt to stress changes under different environmental conditions. Finally, by calculating the optimized stress response, an adaptively corrected stress value is generated. This correction process, by comparing actual environmental conditions and the building's stress response, provides the structural optimization scheme required for practical applications.

[0040] In some embodiments of the present invention, the collaborative design and data sharing module includes: Data sharing submodule: Based on the data from the aforementioned modules, real-time data from various design teams is shared through the cloud platform. Based on real-time environmental and structural data, synchronous updates are performed to generate a shared dataset. The execution process of the data sharing submodule within the collaborative design and data sharing module first relies on real-time data provided by the design teams in the aforementioned modules. This data may include stress response and load data of the building structure, as well as meteorological data such as wind speed and temperature collected from the environment. During implementation, the cloud platform integrates the data from each design team, storing and updating it in real time. For example, design team A provides stress data for the columns, design team B provides load data for the roof, and design team C provides data on environmental wind speed changes. The cloud platform integrates and synchronously updates this data, generating a shared dataset based on real-time data access for all teams. For instance, design team A's column stress under a specific condition is 1000 Pa, team B's roof load under the same condition is 1500 Pa, and team C's wind speed data is 60 meters per hour. The shared dataset contains all this data, ensuring that teams can collaborate and update their design parameters.

[0041] Design optimization submodule: Based on the shared dataset, each design team optimizes the design according to real-time load data and stress response data, modifies the design parameters of the connecting components, and makes real-time adjustments and feedback through the data sharing platform to generate optimized design suggestions; Assuming the shared dataset has been updated, design teams A, B, and C optimize their designs based on real-time load and stress response data. For example, design team A optimizes based on column stress data (1000 Pa) and finds that the current column design may not meet higher load requirements. Therefore, they optimize the design by adjusting column design parameters (such as material strength or cross-sectional dimensions). Design team B optimizes based on roof load data (1500 Pa) and finds potential safety hazards in the roof design. They optimize the connection methods, such as increasing the number of connection points or strengthening the material strength of connecting components. Meanwhile, design team C makes environmental adaptability adjustments based on wind speed data. Assuming a wind speed variation of 60 m / s, they adjust the stability design of the roof structure to address the issue of wind pressure on the roof. All these design optimizations are adjusted and fed back in real time through the data sharing platform, with optimized design recommendations adjusted based on changes in real-time data. The optimized design recommendations are then returned to each design team for further confirmation and implementation.

[0042] The work report generation submodule: Based on the optimized design suggestions, it organizes all design modifications, optimization data, stress distribution information and correction suggestions to generate a collaborative design work report. The report includes the work results of each design team, optimization suggestions and data update information, and is delivered to the construction unit as the final design document.

[0043] The execution process of the work report generation submodule begins with optimizing design suggestions, integrating all design modifications, optimization data, stress distribution information, and correction recommendations. For example, design team A modifies the material strength and dimensions of the columns, design team B adjusts the roof connection method, and design team C optimizes the roof structure to address wind pressure issues. All these modifications are summarized and a final design report is generated. The report includes not only the modified and optimized design data but also the work results and optimization suggestions from each team, ensuring the construction unit clearly understands all modification details. For instance, assuming the column material strength increases from C30 to C40, the number of roof connection points increases from 4 to 6, and the roof material strength increases by 15% under certain wind conditions, all these adjustments and recommendations are detailed in the work report, ensuring the construction unit can follow the report during actual construction. The report also includes real-time updated data, such as predicted roof load values ​​and column stress response values, providing detailed technical support to the construction unit. Finally, the report is delivered to the construction unit as a design document.

[0044] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0045] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0046] Furthermore, those skilled in the art will understand that various aspects of this specification can be described and illustrated in several patentable ways or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, various aspects of this specification can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, various aspects of this specification may be represented as a computer product located on one or more computer-readable media, including computer-readable program code.

[0047] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0048] Finally, it should be understood that the embodiments in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments in this specification are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments in this specification are not limited to those explicitly described and illustrated herein.

Claims

1. A modular building intelligent design and simulation system based on digital twins, characterized in that, The system includes: Digital twin modeling module: acquires geometric model, material properties, and environmental factor data of building structure, collects displacement and stress data of building components, performs digital twin modeling, calibrates the model based on dynamic data, adjusts geometric shape and stress state, calls environmental data for simulation, simulates load conditions, calculates and generates load prediction values ​​of digital twin model; Stress Analysis and Load Prediction Module: Based on the load prediction values ​​of the digital twin model, it analyzes the stress distribution of the building under different working conditions, identifies the module connection components with large loads, filters the areas with large loads based on the stress change trend, and calculates the stress prediction values ​​of overloaded components. Scene adaptation optimization module: Based on the innovative design of the module connection structure, it obtains wind load and seismic environmental conditions, monitors the stress changes of the connection components, and calculates and generates scene adaptability correction coefficients; Module connection structure adjustment module: Based on the scene adaptability correction coefficient, adjust the material thickness and connection method of the module connection components, calculate the optimized stress response, and generate the optimized stress value of the connection components; Collaborative Design and Data Sharing Module: Based on the data from the aforementioned modules, data sharing and collaboration are achieved through a cloud platform, allowing for real-time data access and design optimization, and generating collaborative design work reports.

2. The modular building intelligent design and simulation system based on digital twins according to claim 1, characterized in that, The digital twin modeling module includes: Geometric Model Acquisition Submodule: Acquires geometric model data of building structure, collects material properties and environmental factor data of building components, collects displacement and stress data through sensors, generates basic model, acquires original geometric feature data, and generates initial geometric feature values; Data dynamic calibration submodule: Based on the initial geometric feature values, it calls the dynamic data collected in real time to adjust the geometric shape and stress state of building components, calibrate the model, obtain error data, analyze the differences between the model before and after calibration, and generate geometric shape calibration values. Load simulation analysis submodule: Based on the geometric calibration value, it calls environmental data to perform simulation, simulates the building's performance under different loads, calculates the load response under different working conditions, and generates and calculates the load prediction value of the digital twin model.

3. The modular building intelligent design and simulation system based on digital twins according to claim 2, characterized in that, The original geometric feature data includes the geometric dimensions, shape, connection node information, component location, structural distribution, and material property descriptions of each part of the building.

4. The modular building intelligent design and simulation system based on digital twins according to claim 1, characterized in that, The stress analysis and load prediction module includes: Load distribution analysis submodule: Based on the load prediction value of the digital twin model, it obtains the stress distribution data of the building under different working conditions, monitors the stress change trend of the module connection components, analyzes the stress value of each connection component, screens out areas that may have a large load, and generates stress distribution change values ​​based on stress changes and environmental influences. Overload identification submodule: Based on the stress distribution change value, it determines whether the load of the module connection component exceeds the set threshold, identifies the connection component with excessive load, and further filters out the overload area by analyzing the load trend and comparing the stress value of each component, and generates overload component identification value. Stress prediction calculation submodule: Based on the identified values ​​of overloaded components, calculate the stress response changes in the overloaded area, analyze the stress state of components under different load conditions, and perform prediction calculations in combination with preset stress thresholds to finally generate the predicted stress values ​​of overloaded components.

5. The modular building intelligent design and simulation system based on digital twins according to claim 1, characterized in that, The scenario adaptation and optimization module includes: Environmental Condition Acquisition Submodule: Acquires real-time meteorological data and seismic fluctuation data, generates environmental condition datasets, records environmental change data in real time through monitoring equipment, and obtains environmental condition datasets; Stress Change Monitoring Submodule: Based on the environmental condition dataset, monitor the stress changes of the building module connection components under different environmental conditions, collect stress response data of each component, analyze stress change trends, screen out areas with large stress changes, and generate stress change trend data. Adaptive Correction Calculation Submodule: Based on stress change trend data, calculates the adaptive correction coefficient of building connection components under different environmental conditions, performs correction calculations based on set thresholds, optimizes and adjusts connection components with high stress, and generates scene adaptive correction coefficients.

6. The modular building intelligent design and simulation system based on digital twins according to claim 5, characterized in that, The methods for optimizing and adjusting connecting components with high stress include, but are not limited to, adding connection points or modifying the connection method, and adjusting material parameters.

7. The modular building intelligent design and simulation system based on digital twins according to claim 1, characterized in that, The module connection structure adjustment module includes: Material parameter adjustment submodule: Based on the scene adaptability correction coefficient, obtain the material information of the connecting parts, adjust the material thickness of the connecting parts in the high stress area, analyze the impact of different material strengths on load distribution, increase the material strength or use stronger materials, calculate the adjusted material properties, and generate optimized material parameters; Connection method optimization submodule: Based on the optimized material parameters, adjust the connection method of the module connection components, add or change the position and number of connection points, modify the connection method, analyze the impact of the adjusted connection method on stress distribution, and generate optimized connection method data; Stress response calculation submodule: Based on the optimized connection method data, calculate the stress response of the adjusted module connection components, monitor the stress distribution changes, analyze the load distribution change trend, calculate the optimized stress response data, and generate the optimized connection component stress value.

8. The modular building intelligent design and simulation system based on digital twins according to claim 7, characterized in that, The connection method optimization submodule modifies the connection method by adding welding points or selecting different types of connectors.

9. The modular building intelligent design and simulation system based on digital twins according to claim 1, characterized in that, The collaborative design and data sharing module includes: Data sharing submodule: Based on the data from the aforementioned modules, real-time data from various design teams is shared through the cloud platform. Based on real-time environmental and structural data, synchronous updates are performed to generate a shared dataset. Design optimization submodule: Based on the shared dataset, each design team optimizes the design according to real-time load data and stress response data, modifies the design parameters of the connecting components, and makes real-time adjustments and feedback through the data sharing platform to generate optimized design suggestions; The work report generation submodule: Based on the optimized design suggestions, it organizes all design modifications, optimization data, stress distribution information and correction suggestions to generate a collaborative design work report. The report includes the work results of each design team, optimization suggestions and data update information, and is delivered to the construction unit as the final design document.

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