Building-oriented digital twin modeling method

Abstract

The invention discloses a building-oriented digital twin modeling method, which relates to the technical field of building modeling, and comprises a measurement module, a central processing module, a data construction module, a model analysis module and a demonstration module, and the digital twin modeling method can construct the state and abnormal change of a building through a model. Therefore, structural problems, potential safety hazards or other abnormal conditions can be found in time, the safety and reliability of the building are improved, and the building performance, structural health and maintenance requirements can be deeply understood by analyzing and modeling the building data. The data-driven decision support helps to optimize building maintenance and operation strategies, improve efficiency and reduce cost.

Description

Technical Field

[0001] The present invention relates to the technical field of building modeling, and in particular to a digital twin modeling method for buildings. Background Art

[0002] The digital twin modeling method is a method of creating and managing virtual models of buildings using digital technology. It combines building information modeling (BIM) and other related technologies such as the Internet of Things (IoT), artificial intelligence (AI) and big data analysis to achieve more accurate, efficient and sustainable building design, construction and operation.

[0003] General architectural modeling is constructed in the form of physical models, which requires the customization of a large number of plastic or plastic-like panels for model construction to simulate the shape of the building after construction, thereby providing customers with samples for reference in building planning.

[0004] However, physically constructed models not only have problems such as high consumption of panels and relative independence of the models, but also fail to objectively show the changes of buildings under different factors and disasters. As a result, customers are unable to effectively obtain the structural problems of the buildings, possible safety hazards and other abnormal situations, resulting in a slight deviation in actual building use. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the existing technology, the present invention provides a digital twin modeling method for buildings. This method constructs the status and abnormal changes of buildings through models, which helps to timely discover structural problems, safety hazards or other abnormal situations, and improve the safety and reliability of buildings.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a digital twin modeling method and system for buildings, a digital twin modeling method for buildings, including a measurement module, a central processing module, a data construction module, a model analysis module and a demonstration module;

[0009] The measurement module is used to measure the height, material and shape of the building, as well as the real-time status and abnormal changes of the building under different geographical and climatic conditions;

[0010] The central processing module is used to perform data analysis on the building height, building materials, and building shape collected by the measurement module, and combine the data with the real-time status and abnormal changes of the building under different geographical and climatic conditions;

[0011] The data construction module is used to construct a model based on the data analyzed by the central processing module, thereby generating a basic model;

[0012] The model analysis module is used to deduce and compare the basic model produced in the data construction module with the data produced in the central processing module, so as to calculate the correlation coefficient of abnormal changes;

[0013] The demonstration module is used to combine the relevant data obtained by the model analysis module with the data construction module, so as to simulate and demonstrate the change of building status.

[0014] Preferably, the measurement module includes a height measurement unit, a material analysis unit, a regional climate measurement unit and a shape analysis unit;

[0015] The height measuring unit is used to detect the building height G d The material analysis unit is used to detect the building material C z The regional climate measurement unit is used to detect the regional climate factor Q h The shape analysis unit is used to detect the building shape factor W X .

[0016] Preferably, the building height G d 、Building material C z , Regional climate factors Q h And the building form factor W X Input into the central processing module and calculate the building change value B through the normalization method h , the specific formula is as follows:

[0017]

[0018] Among them, 0<α<1, 0<β<1, 0<γ<1, 0<δ<1;

[0019] α, β, γ and 6 are constants, and the specific values ​​are adjusted and set by the user.

[0020] Preferably, the central module will input the building height G d 、Building material C z , and building form factor W X Input into the data construction module, so as to construct the basic framework through the normalization method, and input the basic framework value into the data construction module to construct the building model. Through the building model, data deduction can be performed, so as to combine special values ​​to obtain the changes that occur in the building under extreme conditions.

[0021] Preferably, the number of people R in the building preload is input into the central processing module respectively. s 、Architectural function Zy , and the building single-layer preload weight Z z Used to improve model building.

[0022] Preferably, the building preload number R s With building height G d and building material C z And the architectural role Z y The weight relationship between them is obtained by the following formula;

[0023]

[0024] Where 0<ε<1, 0<∈<1, 0<θ<1, and ε, ∈ and θ are all constants, and their specific values ​​are adjusted and set by the user;

[0025] Preferably, in order to construct the disaster resistance coefficient Kz of the building when encountering a disaster, the building height G is input into the model. d 、Building material C z 、Building preload number R s And the building form factor W X , the specific formula is as follows:

[0026]

[0027] Among them, 0<θ<1, 0<μ<1, 0<π<1, 0<ρ<1, and θ, μ, π, ρ are all constants, and their specific values ​​are adjusted and set by the user, and π does not represent pi in this solution.

[0028] Preferably, to construct a single-layer building, the preload weight Z z The coefficient of the building height G is input into the model. d 、Building material C z , Building change value B h And the disaster resistance coefficient Kz, the specific coefficient value is obtained by the following formula:

[0029]

[0030] Wherein 0<ρ<1, 0<σ<1, and ρ and σ are both constants, and their specific values ​​are adjusted and set by the user.

[0031] Preferably, the building change value B is input into the model h And the disaster resistance coefficient Kz, the safety threshold Y is constructed through the model z , the specific formula is as follows:

[0032]

[0033] Where 0 <T<1、 And τ and They are all constants, and their specific values ​​are adjusted and set by the user.

[0034] Preferably, it is characterized in that: by respectively setting the safety threshold Y z , Building change value B h , and the disaster resistance coefficient Kz are input into the central processing module, and the building index Z is determined by calculating the comparison value. b Is it within the safety threshold Y? z The specific formula is as follows:

[0035] Z b = {Y z -B h}+{Y z -Kz}

[0036] When 0≤Z b When it is less than 0.2, it is a low-level risk;

[0037] When 0.2≤Z b When <0.4, it is medium risk;

[0038] When 0.4≤Z b When it is less than 0.6, it is a high-level risk;

[0039] When 0.6≤Z b When <1, it is a high risk.

[0040] (III) Beneficial effects

[0041] The present invention provides a digital twin modeling method and system for buildings, which has the following beneficial effects:

[0042] (1) This digital twin modeling method for buildings can construct the status and abnormal changes of buildings through models, which helps to timely discover structural problems, safety hazards or other abnormal situations, improve the safety and reliability of buildings, and obtain a deep understanding of building performance, structural health and maintenance needs through analysis and modeling of building data. This data-driven decision support helps optimize building maintenance and operation strategies, improve efficiency and reduce costs.

[0043] (2) This digital twin modeling method for buildings can simulate and predict the state changes of buildings under different conditions based on the analysis and deduction of digital twin models. This helps to evaluate the performance of buildings in different geographical and climatic environments and provide a basis for improving design and planning. The demonstration module can combine the model analysis results with the data construction module to display the changes in the building state in a visual way and demonstrate the changes of the building under different states to customers, so that customers can better plan the use of the building and the construction of building safety facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the module structure in the present invention.

[0045] Figure 2 It is a schematic diagram of the structure of the modules and auxiliary units in the present invention.

[0046] Figure 3 Flow chart of the method in the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] The digital twin modeling method is a method of creating and managing virtual models of buildings using digital technology. It combines building information modeling (BIM) and other related technologies such as the Internet of Things (IoT), artificial intelligence (AI) and big data analysis to achieve more accurate, efficient and sustainable building design, construction and operation.

[0050] General architectural modeling is constructed in the form of physical models, which requires the customization of a large number of plastic or plastic-like panels for model construction to simulate the shape of the building after construction, thereby providing customers with samples for reference in building planning.

[0051] However, the physically constructed model not only has problems such as large consumption of panels and relative independence of the model, but also cannot objectively show the changes of the building under different factors and disasters, which leads to customers' relatively general planning of the building and some deviations in the actual use of the building.

[0052] See also Figure 1-3 ,The present invention provides a digital twin modeling method for buildings, including a measurement module, a central processing module, a data construction module, a model analysis module and a demonstration module;

[0053] The measurement module is used to measure the building's height, building materials, building shape, as well as the real-time status and abnormal changes of the building under different geographical and climatic conditions;

[0054] The central processing module is used to conduct digital analysis on the building height, building materials, and building shapes collected by the measurement module, and to combine the data with the real-time status and abnormal changes of the building under different geographical and climatic conditions;

[0055] The data construction module is used to construct a model based on the data analyzed by the central processing module, thereby generating a basic model;

[0056] The model analysis module is used to deduce and compare the basic model produced in the data construction module with the data produced in the central processing module, so as to calculate the correlation coefficient of abnormal changes;

[0057] The demonstration module is used to combine the relevant data obtained by the model analysis module with the data construction module to simulate and demonstrate the changes in building status.

[0058] In this embodiment:

[0059] This solution involves the following modules:

[0060] Measurement module: This module uses various sensors and measuring equipment to measure the height, material and shape of the building. It includes technologies such as laser scanning, photogrammetry, and architectural drawing analysis. In addition, the measurement module can also monitor the status of the building in real time under different geographical and climatic conditions and detect any abnormal changes, such as structural displacement, temperature changes, etc.

[0061] Central processing module: This module can transmit the collected building data to the central processing unit for analysis. The central processing module will conduct digital analysis of the building's height, material and shape, and can use computer vision, image processing, data processing and other technologies. At the same time, it will also be combined with the real-time status and abnormal changes of the building under different geographical and climatic conditions to obtain more comprehensive information.

[0062] Data Construction Module: This module is used to build a digital twin model of the building using the data analyzed by the central processing module. The data construction module can use building information modeling software, virtual reality technology, etc. to create an accurate digital representation. By combining data from the measurement module and the central processing module, this module is able to generate a basic model that reflects the characteristics and status of the actual building.

[0063] Model Analysis Module: This module uses the basic model generated by the data construction module to deduce and compare with the data generated by the central processing module. The model analysis module can perform simulation experiments, data comparison and correlation analysis to calculate the correlation coefficient of abnormal changes. Through this module, the stability, safety and health of the building can be evaluated to find potential problems or abnormal situations.

[0064] Demonstration module: This module is used to combine the relevant data obtained from the model analysis module with the data construction module to simulate the changes in the state of the building. Through visualization and interactive demonstration, the state changes of the building under different conditions can be presented, including shape, material properties, structural changes, etc. This helps designers, engineers and decision makers better understand the behavior and performance of the building, and make optimizations and decisions.

[0065] With this method, you can use digital twin technology to simulate and analyze buildings, monitor changes in building status, and calculate the correlation coefficient of abnormal changes. This method can provide valuable data support in areas such as building design, operation and maintenance.

[0066] Example 2

[0067] See also Figure 1-3 ,The measurement module includes a height measurement unit, a material analysis unit, a regional climate measurement unit, and a modeling analysis unit;

[0068] Height measurement unit is used to detect building height G d , the material analysis unit is used to detect the building material C z , the regional climate measurement unit is used to detect regional climate factors Q h , the shape analysis unit is used to detect the building shape factor W X .

[0069] In this embodiment, the measurement module includes a height measurement unit, a material analysis unit, a regional climate measurement unit and a shape analysis unit. The following is a description of the function of each unit.

[0070] Height measurement unit: This unit is used to measure the building height G d , which can be obtained by a laser rangefinder, altimeter or other measuring equipment to obtain the vertical dimensions of the building.

[0071] Material analysis unit: This unit is used to detect building material C z , which can be determined by spectral analysis, chemical analysis or other techniques to determine the type and properties of materials used in buildings, such as concrete, steel, glass, etc.

[0072] Regional climate measurement unit: This unit is used to measure regional climate factors Q h , which can be composed of temperature sensors, humidity sensors, wind speed sensors, etc., to collect and monitor regional meteorological data such as temperature, humidity, wind speed, etc.

[0073] Modeling analysis unit: This unit is used to detect the building shape factors W X, which can use image processing technology, 3D modeling technology or other methods to analyze the shape, outline and geometric features of the building to obtain information about the building's appearance

[0074] Through these measurement units, the measurement module can obtain important data such as the building's height, material, regional climate and shape. These data will be passed to the central processing module for data analysis and further processing to support the construction of the digital twin model and the simulation analysis of the building status.

[0075] Example 3

[0076] The building height G d 、Building material C z , Regional climate factors Q h And the building form factor W X The data is input into the central processing module, which will separate each data through the data splitting unit and input it into the calculation unit. The calculation unit then calculates the building change value B by combining the input value through the normalization method. h , the specific formula is as follows:

[0077]

[0078] Among them, 0<α<1, 0<β<1, 0<γ<1, 0<δ<1;

[0079] α, β, γ and 6 are constants, and the specific values ​​are adjusted and set by the user.

[0080] In this embodiment: when the building height G d 、Building material C z , Regional climate factors Q h and building form factor W X When input to the central processing module, the central processing module will perform detailed processing according to the pre-loaded formula

[0081] Data Splitting Unit: The data splitting unit in the central processing module will ensure that each input data (building height, building material, regional climate factors and building shape factors) is processed separately for subsequent calculations

[0082] In the calculation unit, the central processing module will use the normalization method to combine the input values ​​to calculate the building change value B h The specific formula is:

[0083]

[0084] Among them, 0<α<1, 0<β<1, 0<γ<1, 0<δ<1;

[0085] α, β, γ and 6 are constants, and their specific values ​​are adjusted and set by the user so as to be adjusted according to actual conditions and needs. Their selection is determined based on weight distribution and importance.

[0086] The building change value B calculated by the calculation unit h It can be used to evaluate the degree of change of buildings under the influence of different factors. h A larger value indicates that the building may be affected by more factors and change, while a smaller building change value B h A smaller value indicates a smaller change or less impact

[0087] Through these steps, the central processing module can normalize and comprehensively calculate the input building-related data to obtain the building change value. This value can be used to analyze the changes in building status, evaluate abnormal situations, and provide a reference for decision-making.

[0088] Example 4

[0089] See also Figure 1-3 The central module will input the building height G d 、Building material C z , and building form factor W X Input into the data construction module, so as to construct the basic framework through the normalization method, and input the basic framework value into the data construction module to construct the building model. Through the building model, data deduction can be performed, so as to combine special values ​​to obtain the changes that occur in the building under extreme conditions.

[0090] In this embodiment:

[0091] The building height after the central module is processed is G d 、Building material C z and building form factor W X The data construction module uses these parameters to construct the building model. The data construction module can use numerical calculation methods, building specifications or other modeling techniques to generate the basic framework of the building model. The complete building model is constructed through the basic framework generated by the data construction module and the normalized values. The building model may include information such as the building's geometric shape, structural properties, and material properties to provide simulation and analysis capabilities for building behavior.

[0092] Through the established building model, data deduction can be performed, which means that simulation analysis, performance evaluation, risk analysis and other operations can be performed based on the model to understand the behavior and performance of the building under different circumstances. This method emphasizes the changes in buildings under extreme conditions. By combining special values, such as changes in height, material or shape factors under extreme conditions, the response and behavior of the building can be predicted. This helps to evaluate aspects such as the resilience, disaster resistance or safety performance of the building.

[0093] This building-oriented digital twin modeling method passes input parameters to the central module, and through steps such as data construction, normalization, and modeling, generates a building model and performs data deduction to understand possible changes in the building under different circumstances.

[0094] By inputting the above parameters into the model analysis module, necessary parameters are provided to the height analysis comparison unit, material analysis comparison unit, climate analysis comparison unit and shape analysis comparison unit in the model analysis module, thereby improving the construction of the model.

[0095] Example 5

[0096] A digital twin modeling method for buildings, which inputs the building preload number R s 、Architectural function Z y , and the building single-layer preload weight Z z Used to improve model building.

[0097] The building preloads the number of people R s With building height G d and building material C z And the architectural role Z y The weight relationship between them is obtained by the following formula;

[0098]

[0099] Wherein 0<ε<1, 0<∈<1, 0<θ<1, and ε, ∈ and θ are all constants, and their specific values ​​are adjusted and set by the user.

[0100] In this embodiment: define input parameters to the central processing module:

[0101] Building height G d : The vertical dimension of a building, usually measured in meters.

[0102] Building Material C z : The type of material used in the building, such as concrete, steel structure, etc.

[0103] Building Function Z y :The actual use of the building, such as supermarkets, apartments, residential buildings.

[0104] ε, ∈, and θ: customer-defined weight parameters ranging from 0 to 1, used to adjust the importance of each factor

[0105] Use the following formula to calculate the building preload number R s :

[0106]

[0107] Its value is affected by the building height, building material and building function. By adjusting the values ​​of ε, ∈ and θ, customers can determine the effect of each factor on the building preload number R according to actual needs. s For example, if the customer thinks that the building height has a more important impact on the number of people preloaded, the value of ε can be increased; if the building material has a greater impact on the number of people preloaded, R s The influence of ∈ is more important, and the value of ∈ can be increased; if the building effect has an impact on the number of people R s The influence of θ is more important, and the value of θ can be increased

[0108] This modeling approach can customize the weight relationship based on the specific attributes and functions of the building, as well as the importance that the client places on different factors, thereby more accurately simulating the building's preload capacity R. s changes.

[0109] This digital twin modeling approach for buildings can be used to build accurate building models, analyze and optimize them based on the specific attributes and functions of the building, combined with the importance that customers place on different factors. This helps predict building behavior, evaluate structural performance, and develop effective design and improvement strategies.

[0110] Example 7

[0111] In order to construct the disaster resistance coefficient Kz of the building when encountering a disaster, the building height G is input into the model. d 、Building material C z 、Building preload number R s And the building form factor W X , the specific formula is as follows:

[0112]

[0113] Among them, 0<θ<1, 0<μ<1, 0<π<1, 0<ρ<1, and θ, μ, π, ρ are all constants, and their specific values ​​are adjusted and set by the user, and π does not represent pi in this solution.

[0114] In this embodiment: the method calculates the disaster resistance coefficient by inputting building height, building material, building preloaded number of people and building shape factors into the model. The specific formula is as follows:

[0115]

[0116] Among them, \(K_z\) represents the disaster resistance coefficient, \(G\) d represents the building height, \(C\) z represents the building material, \(R\) s represents the preloaded number of people in the building, \(W\) X represents the building shape factor. \(\theta\), \(\mu\), \(\pi\), and \(p\) are constants between 0 and 1, and their specific values are adjusted and set by the user, and they represent the weights of different factors on the disaster resistance coefficient

[0117] In the formula, each factor is multiplied by the corresponding weight parameter, and then they are added together. In this way, according to the requirements and preferences of the customer, the contribution degree of each factor to the disaster resistance coefficient can be determined. Then, it is divided by the sum of the weight parameters and multiplied by a constant \(B_h\) for normalizing the value of the disaster resistance coefficient

[0118] Through this formula, the disaster resistance coefficient of the building in the event of a disaster can be calculated. This coefficient comprehensively considers the impacts of the building height, material, preloaded number of people, and shape factor on the disaster resistance ability. Customers can adjust the values of the weight parameters according to actual needs and importance to obtain a disaster resistance coefficient that meets their requirements.

[0119] Example 8

[0120] To construct the coefficient of the single - layer pre - load \(Z\) of the building z of the building, the building height \(G\) d , the building material \(C\) z , the building change value \(B\) h and the disaster resistance coefficient \(K_z\) are input into the model. The specific coefficient value is obtained by the following formula:

[0121]

[0122] where \(0 < p < 1\), \(0 < \sigma < 1\), and both \(\rho\) and \(\sigma\) are constants, and their specific values are adjusted and set by the user.

[0123] In this example:

[0124] This method calculates the coefficient of the single - layer pre - load of the building by inputting the building height, building material, building change value, and disaster resistance coefficient into the model. The specific formula is as follows:

[0125]

[0126] where, \(Z\) z represents the coefficient of the single - layer pre - load of the building, \(G\) d represents the building height, \(C\) z represents the building material, \(B\) hrepresents the building change value, and Kz represents the disaster resistance coefficient. ρ and σ are constants between 0 and 1, and their specific values ​​are adjusted by the user.

[0127] In the formula, the building height and building material are multiplied by the two weight parameters p and σ respectively, and then they are added together. Then, this result is divided by (B h +Kz) and (p+σ) are the products of these two factors.

[0128] The purpose of this formula is to calculate the coefficient of the preload of a single floor of a building based on the height, material, building change value and the impact of the disaster resistance coefficient. By adjusting the values ​​of ρ and σ, customers can determine the importance of building height and building material to the preload of a single floor according to actual needs, and combine the impact of the disaster resistance coefficient.

[0129] This coefficient can be used to more accurately calculate the preload of a single floor of a building to meet design requirements and safety standards. Customers can adjust the values ​​of ρ and σ to obtain a suitable coefficient for the preload of a single floor of a building according to their needs and the characteristics of a specific building.

[0130] Example 9

[0131] Enter the building change value B into the model h And the disaster resistance coefficient Kz, the safety threshold Y is constructed through the model z , the specific formula is as follows:

[0132]

[0133] Where 0 <T<1、 And τ and They are all constants, and their specific values ​​are adjusted and set by the user.

[0134] In this embodiment: the method constructs the safety threshold Yz by inputting the building change value Bh and the disaster resistance coefficient Kz. The specific formula is as follows:

[0135]

[0136] Among them, Y_z represents the safety threshold, B_h represents the building change value, Kz represents the disaster resistance coefficient, τ and They are constants between 0 and 1, and their specific values ​​are adjusted and set by the user;

[0137] In the formula, the building change value B_h and the disaster resistance coefficient Kz are multiplied by τ and These two weight parameters are then added together. Finally, this result is divided by The sum of this factor

[0138] The purpose of this formula is to calculate the safety threshold Y_z based on the influence of building change value and disaster resistance coefficient. The customer can determine the importance of building change value and disaster resistance coefficient to the safety threshold according to actual needs.

[0139] Through this safety threshold, the safety of the building can be evaluated and judged. If the calculated Y_z value exceeds or approaches the safety threshold, appropriate measures may need to be taken to ensure the safety of the building. Customers can adjust τ and to obtain a suitable safety threshold.

[0140] Example 10

[0141] By setting the safety threshold Y z , Building change value B h , and the disaster resistance coefficient Kz are input into the central processing module, and the data are compared by the calculation unit and the safety threshold setting unit to determine the building index Z b Is it within the safety threshold Y? z The specific formula is as follows:

[0142] Z b =(Y z -B h )+(Y z -Kz)

[0143] When 0≤Z b When it is less than 0.2, it is a low-level risk;

[0144] When 0.2≤Z b When it is less than 0.4, it is medium risk;

[0145] When 0.4≤Z b When it is less than 0.6, it is a high-level risk;

[0146] When 0.6≤Z b When <1, it is a high risk.

[0147] In this embodiment: the method is to set the safety threshold Y z , Building change value B h The disaster resistance coefficient Kz is input into the central processing module, and then calculated and compared to determine the building index Z b Is it within the safety threshold Y? z The specific formula is as follows

[0148] By calculating the building index Z bBy comparing it with a specific threshold range, the risk level of the building can be assessed. This helps determine whether the building is in a safe state and take appropriate measures based on different levels of risk. b In the input value demonstration module, you can use the building indicator Z b To provide necessary parameter support for the high-rise disaster resistance demonstration unit, stair disaster simulation unit, climate change demonstration unit and modeling demonstration unit.

[0149] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin modeling method for buildings, characterized by: It includes measurement module, central processing module, data construction module, model analysis module and demonstration module; The measurement module is used to measure the height, material and shape of the building, as well as the real-time status and abnormal changes of the building under different geographical and climatic conditions; The central processing module is used to perform data analysis on the building height, building materials, and building shape collected by the measurement module, and combine the data with the real-time status and abnormal changes of the building under different geographical and climatic conditions; The data construction module is used to construct a model based on the data analyzed by the central processing module, thereby generating a basic model; The model analysis module is used to deduce and compare the basic model produced in the data construction module with the data produced in the central processing module, so as to calculate the correlation coefficient of abnormal changes; The demonstration module is used to combine the relevant data obtained by the model analysis module with the data construction module, so as to simulate and demonstrate the change of building status.

2. A digital twin modeling method for buildings according to claim 1, characterized in that: The measurement module includes a height measurement unit, a material analysis unit, a regional climate measurement unit, and a shape analysis unit; The height measuring unit is used to detect the building height G d The material analysis unit is used to detect the building material C z The regional climate measurement unit is used to detect the regional climate factor Q h The shape analysis unit is used to detect the building shape factor W X .

3. A digital twin modeling method for buildings according to claim 2, characterized in that: The building height G d 、Building material C z , Regional climate factors Q h And the building form factor W X Input into the central processing module and calculate the building change value B through the normalization method h , the specific formula is as follows: Among them, 0<α<1, 0<β<1, 0<γ<1, 0<δ<1; And α, β, γ and δ are constants, and the specific values ​​are adjusted and set by the user.

4. The digital twin modeling method for buildings according to claim 3 is characterized in that: The central module will input the building height G d 、Building material C z , and building form factor W X Input into the data construction module, so as to construct the basic framework through the normalization method, and input the basic framework value into the data construction module to construct the building model. Through the building model, data deduction can be performed, so as to combine special values ​​to obtain the changes that occur in the building under extreme conditions.

5. The building-oriented digital twin modeling method according to claim 4, characterized in that: Input the building preload number R into the central processing module respectively s 、Architectural function Z y , and the building single-layer preload weight Z z Used to improve model building.

6. The digital twin modeling method for buildings according to claim 5, characterized in that: The building preloads the number of people R s With building height G d and building material C Z And the architectural role Z y The weight relationship between them is obtained by the following formula; Wherein 0<ε<1, 0<∈<1, 0<θ<1, and ε, ∈ and θ are all constants, and their specific values ​​are adjusted and set by the user.

7. The digital twin modeling method for buildings according to claim 6, characterized in that: In order to construct the disaster resistance coefficient Kz of the building when encountering a disaster, the building height G is input into the model. d 、Building material C z 、Building preload number R s And the building form factor W X , the specific formula is as follows: in 0<μ<1, 0<π<1, 0<ρ<1, and μ, π, ρ, are all constants, and their specific values ​​are adjusted and set by the user. In this solution, π does not represent the ratio of a circle to a circumference of ...

8. The building-oriented digital twin modeling method according to claim 7, characterized in that: To construct a single-story building with preloaded weight Z z The coefficient of the building height G is input into the model. d 、Building material C z , Building change value B h And the disaster resistance coefficient Kz, the specific coefficient value is obtained by the following formula: Where 0<ρ<1, 0<σ<1, and ρ and σ are both constants, whose specific values ​​are adjusted and set by the user.

9. The building-oriented digital twin modeling method according to claim 8, characterized in that: Enter the building change value B into the model h And the disaster resistance coefficient Kz, the safety threshold Y is constructed through the model z , the specific formula is as follows: Where 0<τ<1, And τ and They are all constants, and their specific values ​​are adjusted and set by the user.

10. The building-oriented digital twin modeling method according to claim 9, characterized in that: By setting the safety threshold Y z , Building change value B h , and the disaster resistance coefficient Kz are input into the central processing module, and the building index Z is determined by calculating the comparison value. b Is it within the safety threshold Y? Z The specific formula is as follows: Z b ={And z -B h }+{And z -Kz} When 0≤Z b When <0.2, it is a low-level risk; When 0.2≤Z b When <0.4, it is medium risk; When 0.4≤Z b When <0.6, it is a high-level risk; When 0.6≤Z b When <1, it is a high risk.

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