Design model intelligent creation method and system based on Revit platform

Abstract

The invention relates to the technical field of model creation, and discloses an intelligent design model creation method and system based on a Revit platform, and the system comprises a Revit data platform, a data processing module, a target model generation module, a model processing module and a model application processing module.Based on the rapid modeling technology of Revit, through newly-built or completed model data based on Revit, the design model of the Revit platform is created, and the design model of the Revit platform is created. According to the rapid modeling method, a target platform is imported, rapid modeling, business scene generation and application data export are completed, on the basis of an automatic model turning technology of Revit, through analysis of Revit geometric data and non-geometric data, key feature information is extracted according to classification, a geometric model of the target platform is constructed, model optimization and collision detection are carried out on the automatically-created model, and the target platform is rapidly built. And performing batch model processing, performing online display, and outputting a data conversion and visualization scheme of the target drawing data.

Description

Technical Field

[0001] The present invention relates to the technical field of model creation, and in particular to a method and system for intelligently creating a design model based on a Revit platform. Background Art

[0002] In recent years, scholars and researchers from universities and research institutes at home and abroad have continued to delve deeper into BIM technology and have achieved fruitful research results. In terms of practical application, BIM technology has gradually become a mainstream tool in the field of architectural design and construction. With the promotion and application of BIM technology around the world, Revit software has also developed rapidly. Many domestic institutions have developed BIM simulation systems. For example, the "BIM Simulation System" independently developed by the East China Institute has solved many key technical problems such as the aggregation of multi-source heterogeneous massive data and the rendering of complex special-shaped models, laying a solid foundation for the development of autonomous and controllable technology for the entire process of BIM software. However, the current integration of BIM technology and design process in most engineering projects is low. In the Revit reverse design mode, only local details of the design are corrected. There is insufficient information sharing and collaboration among the design parties. It is impossible to realize changes in all professional design information during the design stage, and the data information generated by the three-dimensional model cannot run through the entire life cycle. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligently creating a design model based on a Revit platform, which solves the problems raised in the above-mentioned background technology.

[0004] To solve the above technical problems, according to one aspect of the present invention, more specifically, a design model intelligent creation system based on the Revit platform includes a Revit data platform, a data processing module, a target model generation module, a model processing module and a model application processing module, which is characterized in that: the Revit data platform: obtains geometric model data through Revit interface data, and establishes model preset relationships and texture color preset relationships through the data;

[0005] The Revit data platform creates a command table based on the data and generates a target instruction list;

[0006] Data processing module: performs data processing through data and instructions;

[0007] Generation processing module: quickly generate the target model based on data and instructions;

[0008] Model processing module: The target model is automatically processed through the model processing module, including collision detection, defect processing and model optimization;

[0009] Model application processing module: The target model is designed and applied through the model application processing module, including materials, rendering, simulation and drawing.

[0010] Furthermore, the following steps are included:

[0011] S1. Revit model architecture construction: Based on the Revit platform technology, the project-level model architecture is constructed to obtain the Revit model, wherein the Revit geometric model data, structural data and project information are obtained through the Revit interface and file format;

[0012] S2. Generate preprocessing commands through preliminary analysis of the above information;

[0013] S3, Revit model preprocessing

[0014] 3.1. Realize the analysis of Revit geometric and non-geometric data and generate data formats supported by the target platform;

[0015] 3.2. Realize the processing and mapping relationship of Revit family library data and material data;

[0016] 3.3. Realize the collection and preparation of preprocessing instructions;

[0017] 3.4. Preprocess the above data and instructions to generate data supported by the target platform;

[0018] 3.5. Optimize the Revit model through Laplace smoothing, where the calculation formula of Laplace smoothing is:

[0019] In the formula, Adj i (P) = N(i);

[0020] N(i)=j / (i,j)∈E, indicating vertex v i The set of direct neighboring points of ;

[0021]

[0022] U (P) To calculate the displacement of vertex (i), it takes the position (v i ) minus the position (v) of its neighboring point (j) j ), and then divided by the degree (d i ), which means that vertex (i) will move in the direction of its neighbors;

[0023] d i Represents a fixed point v i The number of direct domain points of ;

[0024] represents the average position of the neighboring points of vertex (i), which converts the positions (v j ) and then divided by the degree (d) of vertex (i) i );

[0025] The smoothing algorithm is transformed. First, when calculating the average position, different weighting strategies can be used. Generally speaking, the neighboring points that are far away from the center point P can have a smaller impact on the position of P after smoothing. In this way, a Laplace smoothing with the inverse of the distance as the weight can be used;

[0026]

[0027] Where ω1 = ‖P-Adj(P)‖ -1

[0028] Where ω1 is a weight determined by the inverse of the norm of (P) and (Adj(P)); where ω1 = ‖P-Adj(P)‖ -1 Indicates the distance between the center point P and the adjacent points. The farther away, the smaller the weight and the smaller the influence.

[0029] In order to control the smoothing degree of smoothing, the parameter lambda is introduced to control the smoothing rate, that is, from the original execution:

[0030] Transformed into P old =P old +λ(P new -P old ),λ∈(0,1)

[0031] Where P old is the old vertex position; P new is the new vertex position, the threshold of λ is {0,1}, when λ is 0, the center position is not updated, when λ is not 0, the vertex position is updated;

[0032] At the same time, the smoothing algorithm can often be repeatedly executed on the Mesh, making the Mesh smoother and smoother. The number of iterations T is also an important parameter in the smoothing algorithm.

[0033] 4.1. Rapid modeling through geometric model data files;

[0034] 4.2. Batch processing of rapid modeling models, including collision detection, defect processing, model optimization, and material processing;

[0035] 4.3. Complete preview and output, including high-quality image rendering, business scenario simulation, and output of corresponding drawings.

[0036] Furthermore, in said S2, a model is established by generating target instructions, wherein the target instruction list includes an import model instruction, a model batch conversion instruction, a texture conversion instruction, and a structure conversion instruction;

[0037] Import model command: used to import Revit data and generate models;

[0038] Model batch conversion command: used to batch convert the imported Revit model data into the target model;

[0039] Texture conversion command: used to convert the texture data in the Revit model data and attach it to the target model;

[0040] Structural conversion command: used to convert the structural data within the Revit model data.

[0041] Furthermore, in said S3, the Revit model preprocessing includes model collision, defect processing and model optimization;

[0042] Model collision: Detect the target model through model collision detection to enhance the authenticity of the model;

[0043] Defect handling: The defect handling function is used to handle model collision detection and other detected defects;

[0044] Model optimization: The target model is processed through the model optimization function, and the model data is optimized.

[0045] Furthermore, in said S4, the models are batch processed by a model application processing module, and the model application processing module includes material processing, rendering processing, simulation processing and drawing output;

[0046] Material processing: Optimize the material of model data through the model application processing module;

[0047] Rendering processing: Rendering the model data through the model application processing module;

[0048] Model simulation: Perform secondary collision detection on model data through the model application processing module.

[0049] Furthermore, in S1, the integrity of Revit data transmission is obtained through Revit data transmission, so as to optimize the Revit data transmission path, wherein the calculation formula for detecting the integrity of Revit data transmission is:

[0050]

[0051] In the formula, S is the completeness of the data in Revit data transmission; Q1 is the weight of the first defect; S1 is the number of the first defect; Q2 is the weight of the second defect; S2 is the number of the second defect; Q3 is the weight of the third defect; S3 is the number of the third defect; Q n is the weight of the nth defect; S n is the number of n-th defect; Q Z It is the total weight of Revit data.

[0052] Furthermore, in S1, the Revit data has certain risks. The present invention determines the Revit data by calculating the risk value. The calculation formula of the Revit data risk value is:

[0053]

[0054] In the formula, is the risk coefficient of the network system security detection target; S is the resources and expenditure required for network security; P is the quality and reliability of network attack prevention measures; C is the ability of network attacks to be controlled and limited; I is the efficiency level of handling network attack incidents and responses; α is the number of vulnerabilities; β is the severity of the vulnerability; γ is the vulnerability vulnerability; δ is the scope of vulnerability impact.

[0055] The beneficial effects of the design model intelligent creation method and system based on the Revit platform of the present invention are as follows:

[0056] The present invention is based on the rapid modeling technology of Revit. By importing the newly created or completed model data based on Revit into the target platform, the rapid modeling is completed, the business scenario is generated, and the application data is exported. The rapid modeling method, wherein the automatic remodeling technology based on Revit analyzes the geometric data and non-geometric data of Revit, extracts the key feature information by classification, builds the geometric model of the target platform, and then optimizes the automatically created model, performs collision detection, batch models processing, performs online display, and outputs the data conversion and visualization scheme of the target drawing data. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0058] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0060] like Figure 1 As shown, according to one aspect of the present invention, there is provided a design model intelligent creation system based on the Revit platform, including a Revit data platform, a data processing module, a target model generation module, a model processing module and a model application processing module, characterized in that: the Revit data platform: obtains geometric model data through Revit interface data, and establishes model preset relationships and texture color preset relationships through the data;

[0061] The Revit data platform creates a command table based on the data and generates a target instruction list;

[0062] Data processing module: performs data processing through data and instructions;

[0063] Generation processing module: quickly generate the target model based on data and instructions;

[0064] Model processing module: The target model is automatically processed through the model processing module, including collision detection, defect processing and model optimization;

[0065] Model application processing module: The target model is designed and applied through the model application processing module, including materials, rendering, simulation and drawing.

[0066] In this embodiment, the following steps are included:

[0067] S1. Revit model architecture construction: Based on the Revit platform technology, the project-level model architecture is constructed to obtain the Revit model, wherein the Revit geometric model data, structural data and project information are obtained through the Revit interface and file format;

[0068] S2. Generate preprocessing commands through preliminary analysis of the above information;

[0069] S3, Revit model preprocessing

[0070] 3.1. Realize the analysis of Revit geometric and non-geometric data and generate data formats supported by the target platform;

[0071] 3.2. Realize the processing and mapping relationship of Revit family library data and material data;

[0072] 3.3. Realize the collection and preparation of preprocessing instructions;

[0073] 3.4. Preprocess the above data and instructions to generate data supported by the target platform;

[0074] 3.5. Optimize the Revit model through Laplace smoothing, where the calculation formula of Laplace smoothing is:

[0075] In the formula, Adj i (P) = N(i);

[0076] N(i)=j / (i,j)∈E, indicating vertex v i The set of direct neighboring points of ;

[0077]

[0078] U (P) To calculate the displacement of vertex (i), it takes the position (v i ) minus the position (v) of its neighboring point (j) j ), and then divided by the degree (d) of vertex (i) i ), which means that vertex (i) will move in the direction of its neighbors;

[0079] d i Represents a fixed point v i The number of direct domain points of ;

[0080] represents the average position of the neighboring points of vertex (i), which converts the positions (v j ) and then divided by the degree (d) of vertex (i) i );

[0081] The smoothing algorithm is transformed. First, when calculating the average position, different weighting strategies can be used. Generally speaking, the neighboring points that are far away from the center point P can have a smaller impact on the position of P after smoothing. In this way, a Laplace smoothing with the inverse of the distance as the weight can be used;

[0082]

[0083] Where ω1 = ‖P-Adj(P)‖ -1

[0084] Where ω1 is a weight determined by the inverse of the norm of (P) and (Adj(P)); where ω1 = ‖P-Adj(P)‖ -1 Indicates the distance between the center point P and the adjacent points. The farther away, the smaller the weight and the smaller the influence.

[0085] In order to control the smoothing degree of smoothing, the parameter lambda is introduced to control the smoothing rate, that is, from the original execution:

[0086] Transformed into P old =P old +λ(P new -P old),λ∈(0,1)

[0087] Where P old is the old vertex position; P new is the new vertex position, the threshold of λ is {0,1}, when λ is 0, the center position is not updated, when λ is not 0, the vertex position is updated;

[0088] At the same time, the smoothing algorithm can often be repeatedly executed on the Mesh, making the Mesh smoother and smoother. The number of iterations T is also an important parameter in the smoothing algorithm.

[0089] S4, specific implementation of the target platform for preprocessing data and instructions;

[0090] 4.1. Rapid modeling through geometric model data files;

[0091] 4.2. Batch processing of rapid modeling models, including collision detection, defect processing, model optimization, and material processing;

[0092] 4.3. Complete preview and output, including high-quality image rendering, business scenario simulation, and output of corresponding drawings.

[0093] In this embodiment, in S2, a model is established by generating target instructions, wherein the target instruction list includes an import model instruction, a model batch conversion instruction, a texture conversion instruction, and a structure conversion instruction;

[0094] Import model command: used to import Revit data and generate models;

[0095] Model batch conversion command: used to batch convert the imported Revit model data into the target model;

[0096] Texture conversion command: used to convert the texture data in the Revit model data and attach it to the target model;

[0097] Structural conversion command: used to convert the structural data within the Revit model data.

[0098] In this embodiment, in S3, the Revit model preprocessing includes model collision, defect processing and model optimization;

[0099] Model collision: Detect the target model through model collision detection to enhance the authenticity of the model;

[0100] Defect handling: The defect handling function is used to handle model collision detection and other detected defects;

[0101] Model optimization: The target model is processed through the model optimization function, and the model data is optimized.

[0102] In this embodiment, in S4, the models are batch processed by a model application processing module, and the model application processing module includes material processing, rendering processing, simulation processing and drawing;

[0103] Material processing: Optimize the material of model data through the model application processing module;

[0104] Rendering processing: Rendering the model data through the model application processing module;

[0105] Model simulation: Perform secondary collision detection on model data through the model application processing module.

[0106] In this embodiment, in S1, the integrity of Revit data transmission is obtained through Revit data transmission, so as to optimize the Revit data transmission path, wherein the calculation formula for detecting the integrity of Revit data transmission is:

[0107]

[0108] In the formula, S is the completeness of the data in Revit data transmission; Q1 is the weight of the first defect; S1 is the number of the first defect; Q2 is the weight of the second defect; S2 is the number of the second defect; Q3 is the weight of the third defect; S3 is the number of the third defect; Q n is the weight of the nth defect; S n is the number of n-th defect; Q Z It is the total weight of Revit data.

[0109] In this embodiment, in S1, the Revit data has certain risks. The present invention judges the Revit data by calculating the risk value. The calculation formula of the Revit data risk value is:

[0110]

[0111] In the formula, is the risk coefficient of the network system security detection target; S is the resources and expenditure required for network security; P is the quality and reliability of network attack prevention measures; C is the ability of network attacks to be controlled and limited; I is the efficiency level of handling network attack incidents and responses; α is the number of vulnerabilities; β is the severity of the vulnerability; γ is the vulnerability vulnerability; δ is the scope of vulnerability impact.

[0112] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention also fall within the protection scope of the present invention.

Claims

1. A design model intelligent system based on the Revit platform, comprising a Revit data platform, a data processing module, a target model generation module, a model processing module and a model application processing module, characterized in that: Revit data platform: obtain geometric model data through Revit interface data, and establish model preset relationships and texture color preset relationships through data; The Revit data platform creates a command table based on the data and generates a target instruction list; Data processing module: performs data processing through data and instructions; Generation processing module: quickly generate the target model based on data and instructions; Model processing module: The target model is automatically processed through the model processing module, including collision detection, defect processing and model optimization; Model application processing module: The target model is designed and applied through the model application processing module, including materials, rendering, simulation and drawing.

2. The method for creating a design model intelligent system based on the Revit platform according to claim 1 comprises the following steps: S1. Revit model architecture construction: Based on the Revit platform technology, the project-level model architecture is constructed to obtain the Revit model, in which: Obtain Revit geometric model data, structural data and project information through Revit interface and file format; S2. Generate preprocessing commands through preliminary analysis of the above information; S3, Revit model preprocessing 3.

1. Realize the analysis of Revit geometric and non-geometric data and generate data formats supported by the target platform; 3.

2. Realize the processing and mapping relationship of Revit family library data and material data; 3.

3. Realize the collection and preparation of preprocessing instructions; 3.

4. Preprocess the above data and instructions to generate data supported by the target platform; 3.

5. Optimize the Revit model through Laplace smoothing, where the calculation formula of Laplace smoothing is: In the formula, Adj i (P) = N(i); N(i)=j / (i,j)∈E, indicating vertex v i The set of direct neighboring points of ; U (P) To calculate the displacement of vertex (i), it takes the position (v i ) minus the position (v) of its neighboring point (j) j ), and then divided by the degree (d i ), which means that vertex (i) will move in the direction of its neighbors; d i Represents a fixed point v i The number of direct domain points of ; represents the average position of the neighboring points of vertex (i), which converts the positions (v j ) and then divided by the degree (d) of vertex (i) i ); The smoothing algorithm is transformed. First, when calculating the average position, different weighting strategies can be used. Generally speaking, the neighboring points that are far away from the center point P can have a smaller impact on the position of P after smoothing. In this way, a Laplace smoothing with the inverse of the distance as the weight can be used; Where ω1 = ‖P-Adj(P)‖ -1 Where ω1 is a weight determined by the inverse of the norm of (P) and (Adj(P)); where ω1 = ‖P-Adj(P)‖ -1 Indicates the distance between the center point P and the adjacent points. The farther away, the smaller the weight and the smaller the influence. In order to control the smoothing degree of smoothing, the parameter lambda is introduced to control the smoothing rate, that is, from the original execution: Transformed into P old =P old +λ(P new -P old ),λ∈(0,1) Where P old is the old vertex position; P new is the new vertex position, the threshold of λ is {0,1}, when λ is 0, the center position is not updated, when λ is not 0, the vertex position is updated; At the same time, the smoothing algorithm can often be repeatedly executed on the Mesh to make the Mesh smoother and smoother. The number of iterations T is also an important parameter in the smoothing algorithm. S4, specific implementation of the target platform for preprocessing data and instructions; 4.

1. Rapid modeling through geometric model data files; 4.

2. Batch processing of rapid modeling models, including collision detection, defect processing, model optimization, and material processing; 4.

3. Complete preview and output, including high-quality image rendering, business scenario simulation, and output of corresponding drawings.

3. The method for creating a design model intelligent system based on the Revit platform according to claim 2, characterized in that: In S2, a model is established by generating target instructions, wherein the target instruction list includes an import model instruction, a model batch conversion instruction, a texture conversion instruction, and a structure conversion instruction; Import model command: used to import Revit data and generate models; Model batch conversion command: used to batch convert the imported Revit model data into the target model; Texture conversion command: used to convert the texture data in the Revit model data and attach it to the target model; Structural conversion command: used to convert the structural data within the Revit model data.

4. The method for creating a design model intelligent system based on the Revit platform according to claim 2, characterized in that: In S3, Revit model preprocessing includes model collision, defect processing and model optimization; Model collision: Detect the target model through model collision detection to enhance the authenticity of the model; Defect handling: The defect handling function is used to handle model collision detection and other detected defects; Model optimization: The target model is processed through the model optimization function, and the model data is optimized.

5. The method for creating a design model intelligent system based on the Revit platform according to claim 2, characterized in that: In S4, the models are batch processed by a model application processing module, and the model application processing module includes material processing, rendering processing, simulation processing and drawing; Material processing: Optimize the material of model data through the model application processing module; Rendering processing: Rendering the model data through the model application processing module; Model simulation: Perform secondary collision detection on model data through the model application processing module.

6. The method for creating a design model intelligent system based on the Revit platform according to claim 2, characterized in that: In S1, the integrity of Revit data transmission is obtained through Revit data transmission, so as to optimize the Revit data transmission path, wherein the calculation formula for detecting the integrity of Revit data transmission is: In the formula, S is the completeness of the data in Revit data transmission; Q1 is the weight of the first defect; S1 is the number of the first defect; Q2 is the weight of the second defect; S2 is the number of the second defect; Q3 is the weight of the third defect; S3 is the number of the third defect; Q n is the weight of the nth defect; S n is the number of n-th defect; Q Z It is the total weight of Revit data.

7. The method for creating a design model intelligent system based on the Revit platform according to claim 2, characterized in that: In S1, the Revit data has certain risks. The present invention judges the Revit data by calculating the risk value. The calculation formula of the Revit data risk value is: In the formula, Risk factor for network system security detection target; S is the resources and expenditure required for network security; P is the quality and reliability of cyber attack prevention measures; C is the ability to control and limit cyber attacks; I is the efficiency level of handling cyber attack incidents and responses; α is the number of vulnerabilities; β is the severity of the vulnerability; γ is the vulnerability to attack; δ is the scope of vulnerability impact.

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