A modular energy-saving building design system and method

Abstract

The present invention belongs to the technical field of building design, and specifically discloses a modular energy-saving building design system, including a parameter constraint module, a model construction module, an energy-saving constraint module, a splitting module, a positioning module and a load analysis module. Based on this system, the present invention also discloses a modular energy-saving building design method, which optimizes the three-dimensional model through the energy-saving constraint module to meet the energy-saving requirements of the modular building, and then divides the three-dimensional model into multiple polyhedral space models. The positioning module is used to sort each polyhedral space model and determine the coordinates of its connection points, so that the on-site assembly work of the modular building is orderly and accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of building design, and in particular relates to a modular energy-saving building design system and method. Background Art

[0002] Modular construction is an innovative construction method. Standardized building modules are mass-produced in factories to ensure the quality and precision of the modules. They are fixed at the construction site using bolt connections, welding, and other methods. The installation process is simple and quick, and it has broad application prospects in residential buildings, commercial buildings, public buildings and other fields.

[0003] Chinese patent publication number CN113392463A discloses a modular building design method based on BIM technology. It applies BIM technology to the entire life cycle of modular building design, production, and on-site construction, and realizes the fine management of the entire life cycle of modular buildings. In the process of using BIM modeling, although collision checks are performed within the model, in the actual assembly process, the position of the connection point of the building module needs to be determined in advance. Since the connection point of the building module is the key part of the building module and needs to withstand multiple forces, deformation within the technical requirements is inevitable, which will affect the positioning of the next building module and make it difficult to ensure the construction accuracy of the entire modular building system. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects in the prior art and provide a modular energy-saving building design system and method.

[0005] A first aspect of the present invention provides a modular energy-saving building design system, comprising:

[0006] Parameter constraint module: used to obtain modular building project planning requirements and set constraints based on the requirements;

[0007] Model building module: Build a 3D model of the modular building based on constraints to form the main body of the visual model;

[0008] an energy-saving constraint module, which selects passive energy-saving constraints or active energy-saving constraints to optimize the three-dimensional model based on the modular building project plan; the passive energy-saving constraints are to achieve energy saving through the building design itself, and the active energy-saving constraints are to achieve energy saving through external energy sources;

[0009] Splitting module: used for dividing the three-dimensional model into a plurality of polyhedral space models based on the functional space, and each of the polyhedral space models is highlighted on the main body of the visualization model;

[0010] Positioning module: used to determine the connection sequence of each polyhedral space model and label each polyhedral space model based on the connection sequence, obtain the connection point coordinates of each polyhedral space model, and optimize the connection point coordinates of the next polyhedral space model in real time based on the previous polyhedral space model;

[0011] Load analysis module: applying a first load and a second load at the connection points of two adjacent polyhedron space models, obtaining deformation parameters of the connection points, and determining the stability level of the modular building based on the deformation parameters.

[0012] A further solution is that the model construction module includes a predefinition unit, an instruction calling unit and a three-dimensional model construction unit;

[0013] The predefined unit defines a plurality of generation instructions based on a general specification for architectural design, each of the generation instructions includes a dialog box for inputting restriction conditions, and each generation instruction generates a standardized building unit model based on the restriction conditions;

[0014] The instruction calling unit is used to select and call the standardized building unit model;

[0015] The three-dimensional model building unit is used to build a three-dimensional model of a modular building.

[0016] A further solution is that the model building module also includes a sharing unit, which is communicatively connected to multiple user terminals and is used to share the generation instruction.

[0017] A further solution is that the model building module also includes a permission setting unit, which is used to limit the users of any one of the identities to the main control end in a predefined manner when operating on users with different identities at the same time, and the users of other identities are defaulted to the auxiliary end. At the same time, the operation permission scope of the main control end and the auxiliary end is determined separately according to the use identity limitation.

[0018] A further solution is that the splitting module is a splitting decision model based on a neural network, the calibrated functional splitting rules are input into the neural network tool, and iterative training is performed under the set training resources to form the splitting decision model, the three-dimensional model is used as the input of the splitting decision model, and the splitting decision model outputs multiple polyhedron space models after splitting.

[0019] A further solution is that the positioning module optimizes the connection point coordinates of the polyhedron space model in real time through a coordinate optimization model;

[0020] The construction process of the coordinate optimization model is as follows:

[0021] The coordinates of the connection points of two adjacent polyhedron space models after connection are compared with the coordinates before connection and marked by manual experts. The coordinate difference of the connection points of two adjacent polyhedron space models after connection is obtained and coordinate compensation is set. The coordinates of the connection points of the unconnected polyhedron space models are input into the neural network model in sequence according to the time series number for coordinate compensation and the compensated coordinates are output.

[0022] A further solution is that the first load is a wind load, the second load is a seismic load, and the calculation process of the wind load is:

[0023] w k =β z ×μ z ×μ s ×w o

[0024] Among them, w k is the wind load, β z is the gust coefficient of instantaneous wind pressure, μ z is the wind pressure height variation coefficient, μ s is the wind load shape coefficient, w o is the basic wind pressure;

[0025] The calculation process of the seismic load is:

[0026] q=ρ E ×α max ×G

[0027] Where q is the seismic load, ρ E is the power amplification factor, α max The maximum value of the horizontal seismic influence coefficient and the G polyhedron space model are the standard values ​​of gravity loads.

[0028] A further solution is that the load analysis module determines the stability level of the modular building through a stability level model;

[0029] The construction process of the stability level model is as follows:

[0030] A large number of wind load-deformation pairs and seismic load-deformation pairs of different levels are obtained and labeled by human experts. After labeling, the wind load-deformation pairs and seismic load-deformation pairs are sequentially input into different neural network units for iterative training. The two neural network units are combined to obtain the deformation at the connection points of the polyhedron space model under different wind loads and seismic loads.

[0031] Setting a deformation threshold and a modular building stability grade corresponding to the deformation threshold, and obtaining a stability grade model that outputs the modular building stability grade based on the deformation;

[0032] The wind load-deformation number pairs include different wind loads and deformations at the connection points of the polyhedron space model corresponding to the different wind loads;

[0033] The seismic load-deformation pairs include different seismic loads and deformations at connection points of the polyhedron space model corresponding to the different seismic loads.

[0034] A second aspect of the present invention provides a modular energy-saving building design method, which uses the above-mentioned modular energy-saving building design system and includes the following steps:

[0035] Obtain modular building project planning requirements and set constraints based on the requirements;

[0036] Construct a 3D model of the modular building based on the constraints to form the main body of the visual model;

[0037] selecting a passive energy-saving constraint or an active energy-saving constraint to optimize the three-dimensional model based on the modular building project plan; the passive energy-saving constraint is to achieve energy saving through the building design itself, and the active energy-saving constraint is to achieve energy saving through external energy;

[0038] Dividing the three-dimensional model into a plurality of polyhedral space models based on the functional space, and highlighting each of the polyhedral space models on the main body of the visualization model;

[0039] Determining a connection sequence for each of the polyhedral space models and labeling each of the polyhedral space models based on the connection sequence, obtaining connection point coordinates for each of the polyhedral space models, and optimizing the connection point coordinates of a subsequent polyhedral space model in real time based on a previous polyhedral space model;

[0040] A first load and a second load are applied at the connection points of two adjacent polyhedral space models, deformation parameters of the connection points are obtained, and the stability level of the modular building is determined based on the deformation parameters; the first load is a wind load, and the second load is a seismic load.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) The present invention constructs a three-dimensional model that meets the project requirements based on constraint conditions, optimizes the three-dimensional model through an energy-saving constraint module, and meets the energy-saving requirements of modular buildings. The three-dimensional model is then divided into multiple polyhedral space models, and a positioning module is used to sort each polyhedral space model and determine the coordinates of its connection points, so that the on-site assembly work of the modular building is orderly and accurate. The polyhedral space model is produced and assembled in the factory, and the on-site construction efficiency is high. Compared with traditional buildings, it can significantly shorten the construction period, reduce labor costs, expenses, and management costs, and reduce subsequent maintenance costs. In addition, since the polyhedral space model is produced and assembled in the factory, all materials of the modular building can be recycled or reused, there is no wet work and no dust during construction, the production process is green and environmentally friendly, and there is no waste discharge.

[0043] (2) The present invention also communicates with multiple user terminals through a shared unit during model construction, and can share multiple generation instructions defined based on general architectural design specifications, and limit the authority scope of simultaneous design by multiple user terminals, thereby avoiding design conflicts while sharing resources, improving design efficiency, and reducing over-design.

[0044] (3) During the design phase, the present invention simulates wind loads and seismic loads in the form of external loads to monitor the deformation at the connection points of the polyhedron space model, and outputs the stability level of the modular building through the neural network model, thereby ensuring the strength of the modular building. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The following drawings are merely provided for illustrative purposes only and are not intended to limit the scope of the present invention.

[0046] Figure 1 : Schematic diagram of the connection structure of the modular energy-saving building design system of the present invention;

[0047] Figure 2 : Flowchart of the modular energy-saving building design method of the present invention;

[0048] In the figure: 1. Parameter constraint module; 2. Energy-saving constraint module; 3. Model construction module; 4. Splitting module; 5. Positioning module; 6. Load analysis module; 7. Predefined unit; 8. Instruction calling unit; 9. Three-dimensional model construction unit; 10. Sharing unit; 11. Authority setting unit; 12. Splitting decision model; 13. Coordinate optimization model; 14. Stability level model; 15. User end. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution, design method and advantages of the present invention more clear, the present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] Example 1

[0051] This embodiment provides a modular energy-saving building design system, comprising a parameter constraint module, a model building module, an energy-saving constraint module, a splitting module, a positioning module, and a load analysis module. Specifically, the parameter constraint module is used to obtain modular building project planning requirements and set constraints based on these requirements. Constraints are thresholds for different dimensions set according to the project plan, such as functional space quantity thresholds, length thresholds, and area thresholds.

[0052] The model construction module constructs a three-dimensional model of the modular building based on the constraints, forming the main body of the visual model. In this embodiment, the model construction module includes a predefined unit, an instruction retrieval unit, and a three-dimensional model construction unit. The predefined unit defines multiple generation instructions based on general architectural design specifications. Each generation instruction includes a dialog box for entering constraints, and each generation instruction generates a standardized building unit model based on the constraints. The instruction retrieval unit is used to select and call the standardized building unit model. The three-dimensional model construction unit is used to construct the three-dimensional model of the modular building. To improve design efficiency and reduce design costs, the model construction module also introduces a sharing unit. This sharing unit can communicate with multiple user terminals. When a user terminal sends a request to the design system, the generation instructions can be shared, thereby performing standardized design. Since the sharing unit can be connected to multiple user terminals at the same time, multiple users can complete collaborative design. In order to ensure the orderly progress of collaborative design, in this embodiment, the main control terminal and the auxiliary terminal can be determined in a predefined manner. Specifically, the above-mentioned model construction module also includes a permission setting unit, which is used to limit the users of any one of the identities to the main control terminal in a predefined manner when operating on users with different identities at the same time, and the users of other identities are defaulted to the auxiliary terminals. At the same time, the operation authority scope of the main control terminal and the auxiliary terminal is determined separately according to the use identity limitation.

[0053] An energy-saving constraint module selects passive or active energy-saving constraints based on the modular building project plan to optimize the three-dimensional model. Passive energy-saving constraints achieve energy conservation through the building design itself, while active energy-saving constraints achieve energy conservation through external energy sources. Specifically, passive energy-saving constraints include building layout and size, building spacing and orientation, thermal insulation and heat insulation of the building envelope, natural ventilation and natural lighting, etc. Active energy-saving constraints can be energy sources such as electricity and natural gas. By complementing and coordinating passive and active energy-saving constraints, modular buildings can achieve maximum energy conservation.

[0054] The splitting module is used to divide the three-dimensional model into several polyhedral spatial models based on functional space, with each polyhedral spatial model highlighted on the main visualization model. In this embodiment, the splitting module is a neural network-based splitting decision model. Calibrated functional splitting rules are input into the neural network tool, and iterative training is performed using pre-defined training resources to form the splitting decision model. The splitting decision model uses the three-dimensional model as input and outputs multiple polyhedral spatial models after splitting. The calibrated functions include at least building layout, wall structure, door and window structure, office area, and rest area, all defined by different parameters. Different functions are input into the splitting decision model, resulting in different splitting decisions, and different polyhedral spatial models are generated based on these different splitting decisions. The neural network tool includes an input layer, an output layer, and a hidden layer located between the input and output layers. Neurons in the hidden layer recognize and split the input model, outputting different polyhedral spatial models. Specifically, the calibrated functional splitting rules are input into the neural network tool, and iterative training is performed under the set training resources to form the splitting decision model. The three-dimensional model is used as the input of the splitting decision model, and the splitting decision model outputs multiple polyhedron space models after splitting.

[0055] In the above, the positioning module optimizes the coordinates of the connection points of the polyhedron space model in real time through the coordinate optimization model; the construction process of the coordinate optimization model is: comparing the coordinates of the connection points of two adjacent polyhedron space models after connection with the coordinates before connection and performing manual expert marking, obtaining the coordinate difference of the connection points of the two adjacent polyhedron space models after connection and setting coordinate compensation, inputting the coordinates of the connection points of the unconnected polyhedron space model into the neural network model in sequence according to the time series label for coordinate compensation and outputting the compensated coordinates.

[0056] The positioning module is used to determine the connection sequence of each polyhedral space model and label each polyhedral space model based on the connection sequence, as well as obtain the connection point coordinates of each polyhedral space model and optimize the connection point coordinates of the next polyhedral space model in real time based on the previous polyhedral space model. In this embodiment, the positioning module optimizes the connection point coordinates of the polyhedral space model in real time through a coordinate optimization model. The construction process of the coordinate optimization model is as follows: comparing the coordinates of the connection points of two adjacent polyhedral space models after connection with the coordinates before connection and performing manual expert marking, obtaining the coordinate difference of the connection points of the two adjacent polyhedral space models after connection and setting coordinate compensation, inputting the connection point coordinates of the unconnected polyhedral space models into the neural network model in sequence according to the time sequence label for coordinate compensation and outputting the compensated coordinates. In this embodiment, the neural network model is a feed-forward neural network. When predicting the compensated coordinates, the connection point coordinates of the unconnected polyhedral space model are input. After the hidden layer learns the relationship between the connection point coordinates of the unconnected polyhedral space model and the compensated coordinates, the predicted compensated coordinates are output at the output layer.

[0057] Load Analysis Module: Apply a first load and a second load to the connection points of two adjacent polyhedral space models, obtain deformation parameters of the connection points, and determine the stability level of the modular building based on the deformation parameters. Since the connection points of the polyhedral space models are key parts of modular buildings, their design and construction are relatively difficult. In addition to bearing the weight of the polyhedral space models, the connection points may also bear wind loads and seismic loads during later use. Therefore, in this embodiment, to ensure the strength of the entire building, the first load is the wind load, and the second load is the seismic load. The calculation process of the wind load is as follows:

[0058] w k =β z ×μ z ×μ s ×w o

[0059] Among them, w k is the wind load, β z is the gust coefficient of instantaneous wind pressure, μ z is the wind pressure height variation coefficient, μ s is the wind load shape coefficient, w o is the basic wind pressure; the values ​​are taken in accordance with the Code for Loads on Building Structures;

[0060] The calculation process of the seismic load is:

[0061] q=ρ E ×α max ×G

[0062] Where q is the seismic load, ρ E is the power amplification factor, α max The maximum value of the horizontal earthquake influence coefficient, G polyhedron space model is the standard value of gravity load. In this embodiment, ρ E The value is 5.0, and the maximum value of the horizontal earthquake influence coefficient is determined according to the corresponding design intensity.

[0063] The load analysis module determines the stability level of the modular building through a stability level model; the construction process of the stability level model is: obtaining a large number of wind load-deformation pairs and seismic load-deformation pairs of different levels and marking them through human experts; after marking, the wind load-deformation pairs and seismic load-deformation pairs are input into different neural network units in turn for iterative training; the two neural network units are combined to obtain the deformation at the connection point of the polyhedron space model under different wind loads and seismic loads; the combination process of the two neural network units is: the output result of the first neural network unit after iterative training is input into the second neural network unit together with the seismic load, and finally the deformation at the connection point of the polyhedron space model under different wind loads and seismic loads is output.

[0064] A deformation threshold and a modular building stability grade corresponding to the deformation threshold are set to obtain a stability grade model that outputs the modular building stability grade based on the deformation. The wind load-deformation pairs include different wind loads and the deformations at the connection points of the polyhedron space model corresponding to the different wind loads. The seismic load-deformation pairs include different seismic loads and the deformations at the connection points of the polyhedron space model corresponding to the different seismic loads. During the design phase, the present invention simulates wind loads and seismic loads in the form of applied loads to monitor the deformations at the connection points of the polyhedron space model. The modular building stability grade is output through a neural network model, thereby ensuring the strength of the modular building.

[0065] Example 2

[0066] This embodiment utilizes the modular energy-saving building design system of Example 1 to perform modular energy-saving building design, including the following steps:

[0067] Obtain modular building project planning requirements and set constraints based on the requirements;

[0068] Construct a 3D model of the modular building based on the constraints to form the main body of the visual model;

[0069] selecting a passive energy-saving constraint or an active energy-saving constraint to optimize the three-dimensional model based on the modular building project plan; the passive energy-saving constraint is to achieve energy saving through the building design itself, and the active energy-saving constraint is to achieve energy saving through external energy;

[0070] Dividing the three-dimensional model into a plurality of polyhedral space models based on the functional space, and highlighting each of the polyhedral space models on the main body of the visualization model;

[0071] Determining a connection sequence for each of the polyhedral space models and labeling each of the polyhedral space models based on the connection sequence, obtaining connection point coordinates for each of the polyhedral space models, and optimizing the connection point coordinates of a subsequent polyhedral space model in real time based on a previous polyhedral space model;

[0072] A first load and a second load are applied at the connection points of two adjacent polyhedral space models, deformation parameters of the connection points are obtained, and the stability level of the modular building is determined based on the deformation parameters; the first load is a wind load, and the second load is a seismic load.

[0073] While various embodiments of the present invention have been described above, the above descriptions are intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A modular energy-saving building design system, characterized in that: include: Parameter constraint module: used to obtain modular building project planning requirements and set constraints based on the requirements; Model building module: Build a 3D model of the modular building based on constraints to form the main body of the visual model; an energy-saving constraint module, which selects passive energy-saving constraints or active energy-saving constraints to optimize the three-dimensional model based on the modular building project plan; the passive energy-saving constraints are to achieve energy saving through the building design itself, and the active energy-saving constraints are to achieve energy saving through external energy sources; Splitting module: used for dividing the three-dimensional model into a plurality of polyhedral space models based on the functional space, and each of the polyhedral space models is highlighted on the main body of the visualization model; Positioning module: used to determine the connection sequence of each polyhedral space model and label each polyhedral space model based on the connection sequence, obtain the connection point coordinates of each polyhedral space model, and optimize the connection point coordinates of the next polyhedral space model in real time based on the previous polyhedral space model; A load analysis module applies a first load and a second load to the connection points of two adjacent polyhedron space models, obtains deformation parameters of the connection points, and determines the stability level of the modular building based on the deformation parameters; The first load is a wind load, the second load is an earthquake load, and the calculation process of the wind load is: ； in, is the wind load, is the gust coefficient of the instantaneous wind pressure, is the wind pressure height variation coefficient, is the wind load shape coefficient, is the basic wind pressure; The calculation process of the seismic load is: ； in, is the earthquake load, is the power amplification factor, The maximum value of the horizontal earthquake influence coefficient, G polyhedron space model is the standard value of gravity load; The load analysis module determines the stability level of the modular building through a stability level model; The construction process of the stability level model is as follows: A large number of wind load-deformation pairs and seismic load-deformation pairs of different levels are obtained and labeled by human experts. After labeling, the wind load-deformation pairs and seismic load-deformation pairs are sequentially input into different neural network units for iterative training. The two neural network units are combined to obtain the deformation at the connection points of the polyhedron space model under different wind loads and seismic loads. Setting a deformation threshold and a modular building stability grade corresponding to the deformation threshold, and obtaining a stability grade model that outputs the modular building stability grade based on the deformation; The wind load-deformation number pairs include different wind loads and deformations at the connection points of the polyhedron space model corresponding to the different wind loads; The seismic load-deformation pairs include different seismic loads and deformations at connection points of the polyhedron space model corresponding to the different seismic loads.

2. A modular energy-saving building design system according to claim 1, characterized in that: The model building module includes a predefinition unit, an instruction calling unit and a three-dimensional model building unit; The predefined unit defines a plurality of generation instructions based on a general specification for architectural design, each of the generation instructions includes a dialog box for inputting restriction conditions, and each generation instruction generates a standardized building unit model based on the restriction conditions; The instruction calling unit is used to select and call the standardized building unit model; The three-dimensional model building unit is used to build a three-dimensional model of a modular building.

3. A modular energy-saving building design system according to claim 2, characterized in that: The model building module further includes a sharing unit, which is communicatively connected with a plurality of user terminals and is used for sharing the generation instruction.

4. A modular energy-saving building design system according to claim 3, characterized in that: The model building module also includes a permission setting unit, which is used to limit any one of the users to the main control end in a predefined manner when operating on users with different identities at the same time, and users with other identities are defaulted to auxiliary ends. At the same time, the operation permission scope of the main control end and the auxiliary end is determined separately according to the use identity limitation.

5. A modular energy-saving building design system according to claim 4, characterized in that: The splitting module is a splitting decision model based on a neural network. The calibrated functional splitting rules are input into the neural network tool, and iterative training is performed under the set training resources to form the splitting decision model. The three-dimensional model is used as the input of the splitting decision model, and the splitting decision model outputs multiple polyhedron space models after splitting.

6. A modular energy-saving building design system according to claim 5, characterized in that: The positioning module optimizes the coordinates of the connection points of the polyhedron space model in real time through the coordinate optimization model; The construction process of the coordinate optimization model is as follows: The coordinates of the connection points of two adjacent polyhedron space models after connection are compared with the coordinates before connection and marked by manual experts. The coordinate difference of the connection points of two adjacent polyhedron space models after connection is obtained and coordinate compensation is set. The coordinates of the connection points of the unconnected polyhedron space models are input into the neural network model in sequence according to the time series number for coordinate compensation and the compensated coordinates are output.

7. A modular energy-saving building design method, characterized in that: A modular energy-saving building design system according to any one of claims 1 to 6 is used, comprising the following steps: Obtain modular building project planning requirements and set constraints based on the requirements; Construct a 3D model of the modular building based on the constraints to form the main body of the visual model; selecting a passive energy-saving constraint or an active energy-saving constraint to optimize the three-dimensional model based on the modular building project plan; the passive energy-saving constraint is to achieve energy saving through the building design itself, and the active energy-saving constraint is to achieve energy saving through external energy; Dividing the three-dimensional model into a plurality of polyhedral space models based on the functional space, and highlighting each of the polyhedral space models on the main body of the visualization model; Determining a connection sequence for each of the polyhedral space models and labeling each of the polyhedral space models based on the connection sequence, obtaining connection point coordinates for each of the polyhedral space models, and optimizing the connection point coordinates of a subsequent polyhedral space model in real time based on a previous polyhedral space model; A first load and a second load are applied at the connection points of two adjacent polyhedral space models, deformation parameters of the connection points are obtained, and the stability level of the modular building is determined based on the deformation parameters; the first load is a wind load, and the second load is a seismic load.

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