Building information intelligent interpretation method and system based on BIM
By building a component association network of the BIM model, analyzing the position weights of key groups, the problem of failure to consider the impact of components in the prior art is solved, and more efficient design optimization and cost reduction are achieved.
Patent Information
- Application Number
- CN202510713048.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-30
- Publication Date
- 2025-07-01
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art fails to fully consider the impact of components during construction, resulting in inefficient optimization and adjustment and increased costs.
By acquiring the BIM model, building a component association network, analyzing the key coefficients and connections, building a key group, and obtaining the change weight based on the position weight, the overall impact between components is considered during the optimization design process.
Improve the efficiency of design optimization, reduce costs, ensure timely adaptation of key components and related components, and obtain more comprehensive and reliable variable weight results.
Smart Images
Figure CN120234882A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of building information data analysis, and particularly to an intelligent interpretation method and system for building information based on BIM. Background Art
[0002] Intelligent interpretation of building information refers to using building information model technology and intelligent means to intelligently analyze, process, and interpret various information generated during the building design, construction, and operation processes, aiming to convert the structured data in the BIM model and the unstructured information at the construction site into understandable progress status and risk warnings. Intelligent interpretation of building information uses intelligent technology for knowledge reasoning based on the data perception of building information, so as to help the construction industry achieve better decision-making support, optimized management, and risk control in various links such as design, construction, and operation.
[0003] During the construction process of a building, design optimization is an important link. Reasonable design optimization can optimize the structure and save materials on the basis of ensuring the project quality. Existing technologies usually analyze isolatedly with a single component as the adjustment unit when performing structural optimization, without fully considering the implicated effects between components, which may lead to the need to adjust other components when optimizing and adjusting certain components, affecting the efficiency and increasing the cost. Summary of the Invention
[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent interpretation method and system for building information based on BIM.
[0005] According to the first aspect of the embodiments of the present invention, an intelligent interpretation method for building information based on BIM is provided, and the specific technical solution adopted is as follows: Obtain the BIM model of the building and extract the association relationship between components, and construct a component association network based on the association relationship; Based on the component association network, analyze the complexity of the association between each component and other components, obtain the key coefficient of each component, and then screen and obtain the key components; According to the key coefficient, combined with the association relationship in the component association network, analyze the tightness of the association between the key components and other components, obtain the connection degree between the key components and other components, and then construct a key group; Based on the BIM model, analyze the position distribution of the key group, obtain the position weight of the key group, and then obtain the change weight of the key group; According to the change weight, locate the components that can be optimized to complete the interpretation of building information.
[0006] In some embodiments of the present invention, a BIM model of a building is obtained and the association relationships between components are extracted, and a component association network is constructed based on the association relationships, including: Obtain the BIM model of the building; Use the analysis tool of the BIM software to extract the connection and dependency relationships between components; When there is a connection or dependency relationship between two components, connect the two components with a directed edge, construct the directed edge between the two components, and mark the weight of the directed edge to complete the construction of the component association network.
[0007] In some embodiments of the present invention, when there is a connection or dependency relationship between two components, connect the two components with a directed edge, construct the directed edge between the two components, and mark the weight of the directed edge, including: When there is a dependency relationship between two components, the direction of the directed edge is from the dependent component to the dependent component, and the weight of the directed edge is 1; When there is a connection relationship between two components, the direction of the directed edge is the direction of load transfer between the two components, and the weight of the directed edge is the size after normalizing the load size.
[0008] In some embodiments of the present invention, based on the component association network, analyze the association complexity between each component and other components, and obtain the key coefficient of each component, including: Based on the direction of the directed edge between any components in the component association network, obtain the out-degree and in-degree of each component in the component association network; Based on the out-degree and in-degree of the component, perform AP clustering to obtain the out-degree cluster and the in-degree cluster respectively; Analyze the relative importance of the out-degree value of the out-degree cluster where the component is located, and analyze the relative importance of the in-degree value of the in-degree cluster where the component is located, to obtain the relative importance of each component; According to the relative importance, combined with the weight of the directed edge associated with the component, obtain the key coefficient of each component.
[0009] In some embodiments of the present invention, screening to obtain key components includes: Set a key coefficient threshold, and mark the components corresponding to the key coefficient greater than the key coefficient threshold as key components.
[0010] In some embodiments of the present invention, according to the key coefficient, combined with the association relationship in the component association network, analyze the association tightness between the key component and other components, and obtain the connection degree between the key component and other components, including: According to the key coefficient, combined with the weight of the directed edge associated with the component, obtain the transfer rate of each component; Optimize the component association network according to the transmission rate; Based on the optimized component association network, obtain the shortest path between the key component and other components; Obtain the transmission rate between all adjacent components on the shortest path, and combine the number of components passed on the shortest path to obtain the connection degree between the key component and other components.
[0011] In some embodiments of the present invention, a key group is constructed, including: Set a connection degree threshold; Use each of the key components as an initial key group; For components that have a directed edge connection with the key component and whose connection degree with the key component is greater than the connection degree threshold, add the component to the initial key group corresponding to the key component to obtain the key group corresponding to the key component.
[0012] In some embodiments of the present invention, based on the BIM model, analyze the position distribution of the key groups to obtain the position weights of the key groups, and further obtain the change weights of the key groups, including: Establish a three-dimensional coordinate system based on the plane at the lowest position of the building, and set unit cells on the three coordinate system planes; Project all components onto the three coordinate system planes respectively; Obtain the number of components projected in each unit cell, and the number of unit cells occupied by each key group after projection on the three coordinate system planes respectively, to obtain the position weights of the key groups on the three coordinate system planes; According to the position weights, and in combination with the angle between the main direction of the key group and the coordinate axis direction, obtain the weight components of the key group in the three coordinate axis directions; Calculate the proportion of the sum of the weight components of the key group in the total sum of the weight components corresponding to all key groups to obtain the change weight of the key group.
[0013] According to the second aspect of the embodiments of the present invention, a BIM-based intelligent building information interpretation system is provided, including: a memory and a processor, wherein: The memory is used to store program codes; The processor is used to read the program codes stored in the memory and execute the method described in the first aspect of the embodiments of the present invention.
[0014] In some embodiments of the present invention, the processor includes: A component association network construction module, configured to obtain the BIM model of the building and extract the association relationship between components, and construct a component association network based on the association relationship; A key component screening module, which is used to analyze the complexity of the association between each component and other components based on the component association network, obtain the key coefficient of each component, and then screen out the key components; A key group construction module, which is used to analyze the tightness of the association between the key components and other components according to the key coefficient and in combination with the association relationship in the component association network, obtain the connection degree between the key components and other components, and then construct a key group; A variable weight analysis module, which is used to analyze the position distribution of the key group based on the BIM model, obtain the position weight of the key group, and then obtain the variable weight of the key group; A building information interpretation module, which is used to locate the components that can be optimized according to the variable weight and complete the interpretation of building information.
[0015] Compared with the prior art, the BIM-based intelligent building information interpretation method and system provided by the present invention have the following beneficial effects: The present invention obtains the BIM model of a building and extracts the association relationship between components, and constructs a component association network based on the association relationship; obtains the key coefficient of each component from the connection relationship between each component and other components, and initially reflects the influence degree of each component on other components when the component changes with the key coefficient, and screens out the key components according to the key coefficient; analyzes the transmission rate between the key components and their surrounding components, optimizes the component association network, so as to remove the paths with lower transmission efficiency when analyzing the influence transmission between components, so as to obtain more accurate results when analyzing the association between two components in the follow-up, and obtains the mutual influence degree between the key components and their surrounding components based on the transmission rate, and forms the components with mutual influence into a key group. When performing the optimization design, the components with mutual influence are analyzed as a whole, avoiding the situation that the key components are analyzed in isolation during the optimization design, resulting in the design changes not being adapted to the relevant components in time; taking the key group as a unit, performing multi-directional weight analysis on each key group through the component distribution in the actual BIM model, obtaining the variable weight of each key group, fully considering the involved influence between components, retaining the integrity between the components with mutual influence, and making the obtained variable weight result more comprehensive and reliable. Description of the Drawings
[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.
[0017] Figure 1 Schematic diagram of the basic process of a BIM-based intelligent building information interpretation method provided by an embodiment of the present invention; Figure 2 Schematic diagram of the association relationship between components provided by an embodiment of the present invention; Figure 3 Schematic diagram of the projection of a key group on the coordinate system plane provided by an embodiment of the present invention; Figure 4 Schematic diagram of the basic composition of a BIM-based intelligent building information interpretation system provided by an embodiment of the present invention. Detailed implementation manners
[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of the BIM-based intelligent building information interpretation method and system proposed according to the present invention. In the following description, different "an embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.
[0020] The following specifically describes the specific solution of a BIM-based intelligent building information interpretation method provided by the present invention with reference to the accompanying drawings.
[0021] Please refer to Figure 1 , which shows the basic process of a BIM-based intelligent building information interpretation method provided by an embodiment of the present invention.
[0022] As Figure 1 shown, the BIM-based intelligent building information interpretation method provided by an embodiment of the present invention specifically includes: S100: Obtain the BIM model of the building and extract the association relationship between components, and construct a component association network based on the association relationship.
[0023] The purpose of the present invention is to obtain the adjustable weights of each component according to the structural importance of each component in the BIM model of the building, and assist relevant personnel in the optimization design. Therefore, first, a component association network is constructed according to the BIM model of the building to intuitively represent the associated topological relationship between components.
[0024] In an embodiment of the present invention, by obtaining the BIM model of a building and extracting the association relationships between components, a component association network is constructed based on the association relationships. The specific implementation method is as follows: Obtain the BIM model of the building; use the analysis tool of the BIM software to extract the connection and dependency relationships between components; when there is a connection or dependency relationship between two components, connect the two components with a directed edge, construct a directed edge between the two components, and mark the weight of the directed edge. More specifically, when there is a dependency relationship between two components, the direction of the directed edge is from the dependent component to the dependent component, and the weight of the directed edge is 1; when there is a connection relationship between two components, the direction of the directed edge is the direction of load transfer between the two components, and the weight of the directed edge is the size after normalizing the load size; complete the construction of directed edges between all components to obtain the component association network of the building.
[0025] So far, the component association network of the building has been obtained.
[0026] When optimizing the design of building construction, the importance of different components in the entire building is different. For example, components used for connection, load-bearing, etc. that directly affect functionality and structural stability have a very large impact on the building structure. When adjusting them, changes in many other components may be involved. Therefore, when performing structural optimization, it is necessary to minimize the changes to these components as much as possible. Therefore, based on the analysis of the association situation of each component with other components, the change weight of each component is obtained. Specifically, it includes steps S200 to S400.
[0027] S200: Based on the component association network, analyze the complexity of the association between each component and other components, obtain the key coefficient of each component, and then screen to obtain key components.
[0028] The purpose of the present invention is to obtain the degree of variability during the optimization design based on the structural importance of each component in the BIM model. Therefore, it is necessary to analyze the structural and functional importance of each component to screen out the relatively key components among all the components of the building.
[0029] Key components that have a greater impact on functionality and structural stability are usually closely connected to multiple components, and the association relationships with other components are very complex. Therefore, when a key component changes, it will also affect many other components and even the entire building.
[0030] Therefore, in an embodiment of the present invention, based on the component association network, analyze the complexity of the association between each component and other components, obtain the key coefficient of each component, and then screen to obtain key components. Further, it includes: First, based on the direction of the directed edges between any components in the component association network, obtain the out-degree and in-degree of each component in the component association network; where the out-degree is the sum of the number of times a vertex serves as the starting point of an edge in a directed graph, and the in-degree is the sum of the number of times a vertex serves as the ending point of an edge in a directed graph. And, based on the out-degree and in-degree of the components, perform AP clustering to obtain the out-degree cluster and the in-degree cluster respectively.
[0031] Then, analyze the relative importance of the out-degree value of the out-degree cluster where the component is located, and analyze the relative importance of the in-degree value of the in-degree cluster where the component is located, to obtain the relative importance of each component; and, based on the relative importance, combined with the weight of the directed edge associated with the component, obtain the key coefficient of each component. The key coefficient of the th component is calculated as follows: In the formula, represents the key coefficient of the th component: represents the sum of the weights of all directed edges pointing from the th component to other components; represents the average out-degree of the out-degree cluster where the th component is located; represents the maximum value of the average out-degrees corresponding to all out-degree clusters; represents the average in-degree of the in-degree cluster where the th component is located; represents the linear normalization function.
[0032] represents the relative importance of the out-degree value of the out-degree cluster where the th component is located. The larger this value is, the more complex the association relationship between this component and other components is, and the larger the key coefficient of this component is; represents the relative importance of the in-degree value of the in-degree cluster where the th component is located. The larger this value is, the more complex the association relationship between this component and other components is, and the larger the key coefficient of this component is; The sum of the weights of all directed edges pointing from the th component to other components
[0033] The larger the value is, the more this component is relied on by other components or / and the larger the load of this component is relative to other components.
[0034] Finally, the key components are usually connecting components or load-bearing components, with relatively large key coefficients. The key components are obtained by screening based on the key coefficients. Specifically, a key coefficient threshold is set, and the value of the key coefficient threshold can be 0.7. The components with key coefficients greater than the key coefficient threshold are marked as key components.
[0035] S300: According to the key coefficients, combined with the association relationships in the component association network, analyze the degree of association tightness between key components and other components, obtain the connection degree between key components and other components, and then construct key groups.
[0036] The impacts of changes between components are mutual. If only isolated analysis is performed on key components, it may cause the lack of timely adjustment of the cooperation between components with relatively large associations during design optimization, such as in the case of directly connected relationships. Therefore, other components with a high degree of association with the components are obtained to form a whole, and the whole is analyzed to ensure the integrity of each key component of the building and its surrounding components as much as possible.
[0037] Based on the above analysis, in the embodiments of the present invention, according to the key coefficients, combined with the association relationships in the component association network, analyze the degree of association tightness between key components and other components, obtain the connection degree between key components and other components, and then construct key groups. Further included are: When a certain component changes, it will affect all components connected to it, and the connected components will transmit to their respective connected components, causing the initially occurring change to spread among the components. In the component association network, the influence association relationship between two components is manifested as the path between the two components. When the change of a component spreads in the path, it will gradually decay. When the correlation between two components is relatively low, the efficiency of the component change transmitted on this path will be relatively low.
[0038] Therefore, first, according to the key coefficients, combined with the weights of the directed edges of the component associations, obtain the transmission rate of each component; and, according to the transmission rate, optimize the component association network. The specific implementation method is: use the difference in key coefficients to reflect the similarity degree of the states between two components, and combine the connection strength of the two components to obtain the transmission rate between the two components. Take the th component and the th component connected to it in the component association network as an example, and obtain the attenuation rate from the th component to the th component (from the th component to the th component means that there is a directed edge from the th component to the th component between these two components). The transmission rate from the th component to the th component The calculation method is as follows: In the formula, represents the transfer rate from the th component to the th component: represents the key coefficient of the th component; represents the key coefficient of the th component; represents the maximum value of the weights of the directed edges pointing from the th component to other components, represents the weight of the directed edge connecting the th component and the th component.
[0039] represents the difference in key coefficients between the th component and the th component, reflecting the similarity of the states between the two components. The smaller this value is, the more similar the two components are. By adding 0.1, the calculation problem caused by the denominator being 0 is avoided; The larger the value is, the larger the key coefficient of the th component is, and at the same time, the smaller the difference in key coefficients between the th component and the th component is. Therefore, the key coefficients of the th component and the th component are both larger, indicating that the transfer rate between these two components is larger; The larger the value is, the relatively larger the weight of the directed edge between the th component and the th component is, indicating that the connection strength between these two components is larger, and the transfer rate between these two components is larger.
[0040] Similarly, the transfer rate between each pair of components is obtained. When the transfer rate is small, it means that although there is a connection or functional relationship between the two components, the transfer efficiency of the influence between these two components is relatively low.
[0041] The average value of the transfer rates between all pairs of components is obtained. Among all the components pointed to by the th component, if the following conditions are met: the transfer rate from the th component to this component is the smallest and less than the average value of all transfer rates, then the corresponding directed edge is removed in the component association network. As Figure 2 shown, for example, among the three components a, b, and c pointed to by the th component is the smallest and , then the A directed edge connecting a component to the a-th component.
[0042] Similarly, all eligible directed edges are removed to optimize the component association network. When analyzing the influence transmission between components, removing paths with lower transmission efficiency can obtain more accurate results when analyzing the relevance between two components later.
[0043] Then, based on the optimized component association network, obtain the shortest paths between key components and other components, that is, in the optimized component association network, taking each key component as the starting point, obtain the shortest paths (paths with the fewest number of components passed) between each key component and all other components; and, obtain the transmission rates between all adjacent components on the shortest paths, and combine the number of components passed on the shortest paths to obtain the connection degree between key components and other components. The connection degree between the -th key component and the -th component is calculated as follows: In the formula, represents the connection degree between the -th key component and the -th component: represents the -th key component to the -th component on the shortest path, the transmission rate from the -th component to its next component. When , that is, the transmission rate from the -th key component to the first component on this path ; represents the -th key component to the -th component, the number of components passed on the shortest path; represents the linear normalization function.
[0044] The -th key component to the -th component, the number of components passed on the shortest path The larger the value, the closer the association between the -th component and the -th key component, indicating a greater connection degree between the two components; represents the -th key component to the -th component, the product of the transmission rates between all adjacent components on the shortest path, representing the -th key component to the The transfer rate between components. The larger this value is, the closer the association between the two components, indicating a greater degree of connection between the two components.
[0045] Similarly, obtain the connection degrees of each key component to other components.
[0046] Finally, based on the connection degrees between key components and other components, construct key groups. The specific implementation method is as follows: Set a connection degree threshold, and the value of the connection degree threshold can be 0.5; Take each key component as the initial key group; For the components that have a directed edge connection with the key component and the connection degree between the component and the key component is greater than the connection degree threshold, add the component to the initial key group corresponding to the key component to obtain the key group corresponding to the key component. Taking the initial key group constructed by the th key component as an example, for the components that have a directed edge connection with the th key component and the connection degree between the th key component and this component is greater than the preset connection threshold of 0.5, add this component to the initial key group corresponding to the th key component to obtain the key group corresponding to the th key component, denoted as the th key group.
[0047] It should be noted that when the same component is assigned to multiple key groups, if the component is not a key component, take the key group corresponding to the key component with the largest connection degree with it as the key group to which the component belongs; if the component is a key component, merge the key groups that contain each other (containing each other means that the th key group contains the th key component, and the th key group contains the th key component).
[0048] Obtain all key groups. When conducting the optimization design, analyze the components that affect each other as a whole to avoid the situation where isolated analysis of key components during the optimization design leads to the failure of the design changes to be adapted to related components in a timely manner.
[0049] S400: Based on the BIM model, analyze the position distribution of key groups to obtain the position weights of key groups, and then obtain the change weights of key groups.
[0050] In the BIM model of a building, the areas where components are more concentrated are usually the positions with more complex structures and functions. Then, the components in these positions have a greater impact when changing their design structures. A key group contains several components that are closely related and affect each other, and analyze with a key group as a unit of the optimization design. Therefore, based on the position of each key group in the BIM model of the actual building, obtain the position weights of each key group.
[0051] In an embodiment of the present invention, based on the BIM model, the position distribution of the key groups is analyzed to obtain the position weights of the key groups, and then the change weights of the key groups are obtained. The specific implementation method is as follows: First, a three-dimensional coordinate system is established based on the plane at the lowest position of the building, and unit grids are set on the three coordinate system planes, with a unit area of 1 meter by 1 meter as one unit grid; all components are projected onto the three coordinate system planes respectively. As Figure 3 shown, it is a schematic diagram of the projection of a key group on one coordinate system plane, where dark gray represents the projection of the key group, and light gray represents all the unit grids involved in the projection of the key group; the number of components projected in each unit grid is obtained, as well as the number of unit grids occupied by each key group after projection on the three coordinate system planes respectively, to obtain the position weights of the key groups on the three coordinate system planes, where the three coordinate system planes are the xOy plane, the xOz plane, and the yOz plane. Taking the th key group's projection on the xOy plane as an example, the number of components of the th key group projected in each unit grid on the xOy plane is obtained, as well as the number of unit grids occupied by the th key group after projection on the xOy plane, to obtain the position weight of the th key group on the xOy plane. The position weight of the th key group on the xOy plane is calculated as follows: In the formula, represents the position weight of the th key group on the xOy plane; represents the number of unit grids occupied by the th key group after projection on the xOy plane; represents the number of components of the th key group projected in the th unit grid after projection on the xOy plane.
[0052] The smaller the value of the number of unit grids occupied by the th key group after projection on the xOy plane, the more concentrated the position of the components in the key group projected on the xOy plane, indicating that the position weight of the th key group on the xOy plane is greater; represents the total number of components projected in all the unit grids occupied by the th key group after projection on the xOy plane. The larger this value, the larger the number of components in the key group and the more concentrated the position projected on the xOy plane, indicating that the position weight of the th key group on the xOy plane is greater.
[0053] Similarly, obtain the position weights of the th key group in the xOz and yOz planes, and obtain the position weights of all key groups in the three coordinate system planes.
[0054] The position weight reflects the structural complexity weight of the position of the key group on the corresponding projection plane. Further, according to the position weight, combined with the angle between the main direction of the key group and the coordinate axis direction (reflecting the inclination of the main direction to the projection plane), obtain the weight components of the key group in the three coordinate axis directions. Among them, make a vector according to the association relationship in the component association network (the direction of the directed edge is the direction of the vector, and the weight of the associated edge is the magnitude of the vector), and add up all the vectors of the key group, and record the obtained result as the main direction of the key group. Take the th key group as an example for the weight component in the z-axis direction (corresponding to the xOy plane), the th key group's weight component in the z-axis direction is , where represents the angle between the main direction of the th key group and the z-axis. Similarly, obtain the weight components of the th key group in the x and y axis directions and .
[0055] Finally, calculate the proportion of the sum of the weight components of the key group in the sum of the weight components corresponding to all key groups to obtain the change weight of the key group. Specifically, integrate the weight components of the th key group in each axis direction to obtain the integrated weight of the th key group, and then calculate the ratio of to the sum of the integrated weights corresponding to all key groups to obtain the change weight of the th key group, that is: In the formula, the change weight of the th key group; represents the integrated weight of the th key group; represents the sum of the integrated weights corresponding to all key groups.
[0056] Similarly, obtain the change weights of all key groups.
[0057] The change weight is obtained based on key groups. A multi-directional weight analysis is performed on each key group through the component distribution in the actual BIM model to obtain the change weight of each key group. The integrity between the components that influence each other is retained, making the obtained change weight results more comprehensive and reliable.
[0058] S500: According to the change weight, locate the components that can be optimized and complete the interpretation of building information.
[0059] According to the change weight, locate the components that can be optimized and complete the interpretation of building information. The specific implementation method is: visualize the change weight of each key group on the BIM model, so that relevant personnel can quickly screen out the adjustable components when optimizing the design. That is, divide the components into several groups according to the key group (components without belonging to the key group are a single group); color each group according to the change weight of the key group (change weight from high to low, color from red to yellow to green, components without belonging to the key group are white); complete the coloring of all components and generate a change weight map; relevant personnel can quickly locate the optimizable components by referring to the change weight map when optimizing the design.
[0060] At this point, intelligent interpretation of building information based on BIM is achieved.
[0061] Based on the same inventive concept as the above method, this embodiment also provides a BIM-based building information intelligent interpretation system.
[0062] See also Figure 4 , which shows the basic composition of a BIM-based building information intelligent interpretation system provided by an embodiment of the present invention.
[0063] like Figure 4 As shown, a BIM-based building information intelligent interpretation system includes: a memory 10 and a processor 20, wherein: A memory 10, used for storing program codes; The processor 20 is used to read the program code stored in the memory 10, and execute to obtain the BIM model of the building and extract the association relationship between the components, and build a component association network based on the association relationship; based on the component association network, analyze the complexity of the association between each component and other components, obtain the key coefficient of each component, and then screen and obtain the key components; according to the key coefficient, combined with the association relationship in the component association network, analyze the degree of association between the key component and other components, obtain the connection between the key component and other components, and then build a key group; based on the BIM model, analyze the position distribution of the key group, obtain the position weight of the key group, and then obtain the change weight of the key group; according to the change weight, locate the optimizeable components to complete the interpretation of the building information.
[0064] Further, the processor 20 includes a component association network construction module 21, a key component screening module 22, a key group construction module 23, a change weight analysis module 24, and a building information interpretation module 25. Among them: The component association network construction module 21 is configured to obtain the BIM model of the building and extract the association relationships between components, and construct a component association network based on the association relationships; The key component screening module 22 is configured to analyze the complexity of the association between each component and other components based on the component association network, obtain the key coefficient of each component, and further screen to obtain key components; The key group construction module 23 is configured to analyze the tightness of the association between key components and other components according to the key coefficient and in combination with the association relationships in the component association network, obtain the connection degree between key components and other components, and further construct a key group; The change weight analysis module 24 is configured to analyze the position distribution of the key group based on the BIM model, obtain the position weight of the key group, and further obtain the change weight of the key group; The building information interpretation module 25 is configured to locate the components to be optimized according to the change weight and complete the building information interpretation.
[0065] It should be noted that: the above-mentioned sequence of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0066] The various embodiments in this specification are all described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.
Claims
1. An intelligent interpretation method of building information based on BIM, characterized in that, The method includes: Obtain the BIM model of a building and extract the association relationships between components, and construct a component association network based on the association relationships; Based on the component association network, analyze the association complexity between each component and other components, obtain the key coefficient of each component, and then screen to obtain key components; According to the key coefficient, combined with the association relationships in the component association network, analyze the association tightness between the key components and other components, obtain the connection degree between the key components and other components, and then construct key groups; Based on the BIM model, analyze the location distribution of the key groups, obtain the location weight of the key groups, and then obtain the change weight of the key groups; According to the change weight, locate the components that can be optimized to complete the interpretation of building information.
2. The BIM-based intelligent building information interpretation method according to claim 1, wherein, Obtain the BIM model of a building and extract the association relationships between components, and construct a component association network based on the association relationships, including: Obtain the BIM model of a building; Use the analysis tool of BIM software to extract the connection and dependency relationships between components; When there is a connection or dependency relationship between two components, connect the two components with a directed edge, construct the directed edge between the two components, and mark the weight of the directed edge to complete the construction of the component association network.
3. The BIM-based intelligent building information interpretation method according to claim 2, wherein When there is a connection or dependency relationship between two components, connect the two components with a directed edge, construct the directed edge between the two components, and mark the weight of the directed edge, including: When there is a dependency relationship between two components, the direction of the directed edge is from the dependent component to the dependent component, and the weight of the directed edge is 1; When there is a connection relationship between two components, the direction of the directed edge is the direction of load transfer between the two components, and the weight of the directed edge is the size after normalizing the load size.
4. The BIM-based intelligent building information interpretation method according to claim 2, wherein, Based on the component association network, analyze the association complexity between each component and other components, obtain the key coefficient of each component, including: Based on the direction of the directed edge between any components in the component association network, obtain the out-degree and in-degree of each component in the component association network; Based on the out-degree and in-degree of the component, perform AP clustering to obtain the out-degree cluster and in-degree cluster respectively; Analyze the relative importance of the out-degree value of the out-degree cluster where the component is located, and analyze the relative importance of the in-degree value of the in-degree cluster where the component is located, to obtain the relative importance of each component; According to the relative importance, combined with the weight of the directed edge associated with the component, obtain the key coefficient of each component.
5. The BIM-based intelligent building information interpretation method according to claim 4, wherein, Screen to obtain key components, including: Set a key coefficient threshold, and mark the component corresponding to the key coefficient greater than the key coefficient threshold as a key component.
6. The BIM-based intelligent building information interpretation method according to claim 2, wherein According to the key coefficient, combined with the association relationships in the component association network, analyze the association tightness between the key components and other components, obtain the connection degree between the key components and other components, including: According to the key coefficient, combined with the weight of the directed edge associated with the component, obtain the transmission rate of each component; According to the transmission rate, optimize the component association network; Based on the optimized component association network, obtain the shortest path between the key components and other components; Obtain the transmission rate between all adjacent components on the shortest path, and combine the number of components passed on the shortest path to obtain the connection degree between the key component and other components.
7. The BIM-based intelligent building information interpretation method according to claim 6, characterized in that Construct a key group, including: Set a connection degree threshold; Use each of the key components as an initial key group; For the components connected by a directed edge to the key component, and if the connection degree between the component and the key component is greater than the connection degree threshold, then add the component to the initial key group corresponding to the key component to obtain the key group corresponding to the key component.
8. The BIM-based intelligent building information interpretation method according to claim 1, wherein Based on the BIM model, analyze the position distribution of the key groups to obtain the position weights of the key groups, and further obtain the change weights of the key groups, including: Establish a three-dimensional coordinate system based on the plane of the lowest position of the building, and set unit cells on the three coordinate system planes; Project all components onto the three coordinate system planes respectively; Obtain the number of components projected in each unit cell, and the number of unit cells occupied by each key group after projection onto the three coordinate system planes respectively, to obtain the position weights of the key groups on the three coordinate system planes; According to the position weights, and in combination with the angle between the main direction of the key group and the coordinate axis direction, obtain the weight components of the key group in the three coordinate axis directions; Calculate the proportion of the sum of the weight components of the key group in the total sum of the weight components corresponding to all key groups to obtain the change weight of the key group.
9. An intelligent building information interpretation system based on BIM, characterized in that, The system includes: a memory and a processor, where: The memory is used to store program codes; The processor is used to read the program codes stored in the memory and execute the method according to any one of claims 1 to 8.
10. The BIM-based intelligent building information interpretation system according to claim 9, characterized in that, The processor includes: A component association network construction module, which is used to obtain the BIM model of the building and extract the association relationship between components, and construct a component association network based on the association relationship; A key component screening module, which is used to analyze the complexity of the association between each component and other components based on the component association network, obtain the key coefficient of each component, and further screen to obtain key components; A key group construction module, which is used to analyze the tightness of the association between the key component and other components according to the key coefficient and in combination with the association relationship in the component association network, obtain the connection degree between the key component and other components, and further construct a key group; A change weight analysis module, which is used to analyze the position distribution of the key groups based on the BIM model, obtain the position weights of the key groups, and further obtain the change weights of the key groups; A building information interpretation module, which is used to locate the components that can be optimized according to the change weight to complete the building information interpretation.