CN119273872B

CN119273872B - BIM model light weight method of semantic constraint

Info

Publication number: CN119273872B
Application number: CN202411785328.9A
Authority: CN
Inventors: 张斌; 彭正伟; 乜长顺; 周品一; 姚星
Original assignee: Shenzhen Etop Information Co ltd
Current assignee: Shenzhen Etop Information Co ltd
Priority date: 2024-12-06
Filing date: 2024-12-06
Publication date: 2025-05-27
Anticipated expiration: 2044-12-06
Also published as: CN119273872A

Abstract

The present application discloses a semantically constrained BIM model lightweight method, which relates to the field of data processing technology. The method comprises: constructing a sub-high-precision model of each component according to the geometric information of each component in the BIM model to be processed; obtaining the semantic attribute information of the component, and mapping the semantic attribute information to the sub-high-precision model, and then generating a high-precision model according to the sub-high-precision model; filtering the non-main structural components in the high-precision model according to the semantic attribute information to obtain a low-precision model. The present application can perform lightweight processing on the model by semantic filtering.

Description

BIM model light weight method of semantic constraint

Technical Field

The application relates to the technical field of data processing, in particular to a semantic constraint BIM model light-weight method.

Background

The LOD (Levels of Detail) technology can dynamically adjust the precision of the model according to the viewpoint distance, screen resolution and other factors. The low-precision model is displayed on a low-resolution screen or when the viewpoint is far from the model, and the high-precision model and texture are loaded on a high-resolution screen or when the viewpoint is near the model. By constructing LOD models of different layers, the data processing amount can be reduced as much as possible on the premise of ensuring the quality of the model, and the loading speed of the model is increased.

When the BIM model is subjected to light weight processing, LOD models of different levels of detail are generally constructed by size filtering. When the set filtering threshold is too high, the reserved data contains a large amount of redundant information, so that the generated data volume is large. If the set filtering threshold is too low, although some smaller redundant data may be removed, some important but slightly larger size data is erroneously filtered out, thereby affecting the integrity and accuracy of the data.

The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art.

Disclosure of Invention

The application mainly aims to provide a semantic constraint BIM model light-weight method, which aims to solve the technical problem of how to carry out light-weight treatment on a BIM model.

In order to achieve the above object, the present application provides a semantic constraint BIM model light-weight method, which includes:

constructing a sub-high-precision model of each component according to the geometric information of each component in the BIM to be processed;

Acquiring semantic attribute information of the component, mapping the semantic attribute information into the sub high-precision model, and generating a high-precision model according to the sub high-precision model;

and filtering non-main body structural members in the high-precision model according to the semantic attribute information to obtain a low-precision model.

In one embodiment, the method further comprises:

When a model switching instruction is received, analyzing the model switching instruction to obtain current model information and target model information;

And determining a switching mode according to the current model information and the target model information.

In an embodiment, the step of determining the switching mode according to the current model information and the target model information includes:

when switching from a symbolic model to a bulk model or from a bulk model to the low-precision model, the switching mode is an alternative mode;

the switching mode is an additional mode when switching from the low-precision model to the high-precision model.

In an embodiment, the step of switching from the low-precision model to the high-precision model comprises:

determining an adding requirement and target semantic attribute information corresponding to the adding requirement according to the model switching instruction;

And determining a component to be added according to the target semantic attribute information, and overlapping the component to be added into the low-precision model to obtain the high-precision model.

In an embodiment, after the step of filtering the non-main structural components in the high-precision model according to the semantic attribute information to obtain a low-precision model, the method further includes:

According to the semantic attribute information, obtaining a pavement member in the high-precision model, and obtaining a bounding box of the pavement member;

acquiring three-dimensional coordinates of the geometric center of the bounding box, and sequencing the three-dimensional coordinates according to a route trend sequence to generate a coordinate point sequence;

And generating the symbol model according to the coordinate point sequence.

Performing triangle mesh simplification on the low-precision model, deleting or merging redundant vertexes, and performing mesh simplification;

and carrying out texture compression on the low-precision model subjected to grid simplification to obtain the body block model.

In an embodiment, the step of generating the high-precision model according to the sub-high-precision model after obtaining the semantic attribute information of the component and mapping the semantic attribute information into the sub-high-precision model includes:

Acquiring attribute information and semantic description of each component in the BIM model, and generating semantic attribute information according to the attribute information and the semantic description;

Acquiring and associating the semantic attribute information with the component in the sub-high-precision model according to the identifier of the component;

and generating a high-precision model according to the sub-high-precision model.

In an embodiment, the step of filtering the non-main structural members in the high-precision model according to the semantic attribute information to obtain a low-precision model includes:

traversing the semantic information, and determining the component as the non-main body structural component if the semantic information corresponding to the component is not in a preset main body structural component list;

filtering the non-body structural member from the high-precision model to obtain the low-precision model.

In addition, in order to achieve the aim, the application also provides a BIM model lightening system of semantic constraint, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is configured to realize the steps of the BIM model lightening method of semantic constraint.

The application provides a semantic constraint BIM model light-weight method, which constructs a sub-high-precision model of each component in a BIM model to be processed according to the geometric information of each component, and can ensure the accuracy of the sub-high-precision model by acquiring the geometric information of each component in the BIM model. Acquiring semantic attribute information of the component, mapping the semantic attribute information into the sub high-precision model, and generating a high-precision model according to the sub high-precision model; and filtering non-main body structural members in the high-precision model according to the semantic attribute information to obtain a low-precision model. By mapping semantic attribute information in the sub-high-precision model and generating a high-precision model, non-main body structural members in the high-precision model are filtered according to the semantic attribute information, so that a simplified low-precision model is obtained. The method can process deeper information through semantic filtering, and can avoid failure in effectively removing redundant information due to improper setting of a filtering threshold or filtering important information by mistake.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a schematic flow chart of a BIM model lightweight method embodiment of semantic constraint;

FIG. 2 is a schematic diagram of a detailed flow provided by a first embodiment of a BIM model lightweight method for semantic constraint according to the present application;

FIG. 3 is a schematic flow chart of a second embodiment of a BIM model lightweight method for semantic constraint according to the present application;

FIG. 4 is a schematic flow chart of a third embodiment of a BIM model lightweight method for semantic constraint according to the present application;

FIG. 5 is a schematic flow chart of a light-weight BIM model method embodiment IV of the semantic constraint of the present application;

FIG. 6 is a schematic diagram of a model switching manner provided in a fourth embodiment of a BIM model lightweight method with semantic constraints according to the present application;

FIG. 7 is a schematic flow chart of a light-weight BIM model method embodiment IV of the semantic constraint of the present application;

FIG. 8 is a schematic diagram of a device structure of a hardware operating environment involved in a BIM model lightweight method of semantic constraint in an embodiment of the present application.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.

For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.

The method comprises the main steps of constructing a sub-high-precision model of each component according to geometric information of each component in a BIM to be processed, obtaining semantic attribute information of the component, mapping the semantic attribute information into the sub-high-precision model, generating a high-precision model according to the sub-high-precision model, and filtering non-main structural components in the high-precision model according to the semantic attribute information to obtain a low-precision model.

It should be noted that, the execution body of the embodiment may be a computing service device having functions of network communication and program running, such as a tablet computer, a personal computer, a mobile phone, or an electronic device, an apparatus, or the like capable of implementing the above functions. This embodiment and the following embodiments will be described below by taking a semantically constrained BIM model lightweight system as an example.

Based on this, the embodiment of the application provides a semantic constraint BIM model light-weight method, referring to fig. 1, fig. 1 is a flow diagram of a first embodiment of the semantic constraint BIM model light-weight method of the application.

In this embodiment, the semantic constraint BIM model light weight method includes steps S100-S300:

and S100, constructing a sub-high-precision model of each component according to the geometric information of each component in the BIM to be processed.

The BIM (Building Information Modeling, building information model) refers to the process and the result of digital expression of physical and functional characteristics of the BIM during the whole life of construction engineering and facilities, and design, construction and operation according to the digital expression. The traffic road BIM model is a three-dimensional digital model constructed based on BIM technology by integrating multi-stage information such as road design, construction, operation and maintenance. The high-definition model, namely a LOD4 (Level of Detail 4) hierarchical model, is a complete component Level model with the highest definition Level, and can meet the requirements of fine business scene management and high-precision display. LOD (level of Detail model) refers to determining the resource allocation of object rendering according to the position and importance of the nodes of the object model in the display environment, and reducing the number of planes and the Detail of non-important objects, so as to obtain high-efficiency rendering operation.

The components in the BIM model are basic units constituting an infrastructure such as a building or a traffic road. In a traffic road BIM model, components may include road pavement, roadbeds, bridges, tunnels, drainage facilities, traffic signs, markings, and the like. These components are accurately represented in the BIM model by three-dimensional modeling techniques and contain rich information.

In this embodiment, the traffic road BIM model may be obtained from an online platform or database, for example, from a CIM platform (platform of city information modeling, full-market space-time information platform). The traffic road BIM model is opened according to BIM software (such as Revit, civil 3D, etc.), and geometric information of each component, such as length, width, height, curvature, position coordinates, etc., is extracted by using measuring and inquiring tools of the BIM software. And sorting the extracted geometric information to form a structured data set. And the data are checked, so that the accuracy and the integrity of the information are ensured. And selecting proper modeling software (such as AutoCAD, 3ds Max, sketchUp and the like) to construct a high-precision model.

In this embodiment, road entities may be divided into a civil structure layer, a traffic facility layer, a drainage facility layer, and a safety facility layer. The civil engineering structure layer comprises roadbed and road surface. The traffic facility layer includes lanes, sidewalks, central dividers, and traffic islands. The drainage facility layer comprises a drainage ditch and a drainage pipeline. The security facility layer includes traffic signs, traffic lights, guardrails, and lighting systems.

In the present embodiment, when the traffic road BIM model is converted into the high-precision model, parameters are set in advance as constraint conditions in order to ensure consistency between the high-precision model and the traffic road BIM model. Constraints may include project cardinal coordinates, azimuth information, and model size units. The project base point coordinates and the azimuth angle information are used for determining the position and the orientation of the BIM model in the global coordinate system, and in the conversion process, the generated triangular mesh model and the original BIM model can be ensured to keep consistent in spatial position. Model dimension units BIM models are typically modeled using specific dimension units (e.g., millimeters, meters, etc.). During conversion, it is necessary to ensure that these units are converted or retained correctly to avoid errors in size. After setting the conversion constraint conditions, traversing all components in the traffic road BIM model, and extracting geometric information of each component.

Optionally, before extracting the geometric information in the BIM model, the traffic road BIM model is checked first to ensure the accuracy, integrity and consistency of the traffic road BIM model. Verification content includes, but is not limited to, geometric shapes, sizes, position relations of the models, and attribute information such as materials, textures and the like.

Alternatively, scan data of different resolutions or meshing of different densities are employed for different regions and components. According to project requirements and design specifications, key components and important areas are set, and for the key components and the important areas, a high-precision model is constructed by using higher-resolution scanning data and denser grid division. For example, key components can be determined from structural stability and safety assessments, with bridges and tunnels being key structures in traffic roads, the stability and safety of which are critical. Thus, more detailed modeling and analysis is performed. Because of large traffic flow and various traffic modes, traffic accidents are easy to occur in the intersection and the overpass area, and more detailed modeling and analysis are needed. The key components can be determined according to the geographical environment and the climate conditions, and the components in the important areas are regarded as key components in areas with complex geological conditions and severe climate conditions.

Optionally, in the process of constructing the sub high-precision model, the accuracy change of the model is monitored according to a preset period, the geometric shape, the size and the position relation of the model are checked, the accuracy data of each check are recorded, the previous data are compared, and the trend and the reason of the accuracy change are analyzed. And when the accuracy is found to be reduced or the preset accuracy standard is not met, adjusting and optimizing the modeling parameters.

In a possible embodiment, step S100 may further include the steps of:

obtaining texture information of each component in the traffic road BIM;

And mapping the texture information to the component corresponding to the high-precision model.

In this embodiment, the geometric information of each component in the traffic road BIM model is acquired, and at the same time, the texture information of each component is acquired. After a high-precision model is constructed from the geometric information, a member to which texture needs to be added is selected. For each selected component, a corresponding texture picture is applied to the surface of the component using a texture mapping tool in BIM software. And carrying out detail optimization on the basic model, such as adding edges, chamfers, textures and the like. The visual effect of the model is enhanced using high quality texture materials. And the simulation function of the modeling software is utilized to simulate the effects of illumination, shadow, materials and the like on the model. And further adjusting and optimizing the model according to the simulation result.

In this embodiment, the traffic road model with high accuracy and vivid effect can be obtained by extracting geometric information from the traffic road BIM model and selecting appropriate modeling software to perform construction and optimization of the high-precision model.

Step S200, semantic attribute information of the component is obtained, the semantic attribute information is mapped into the sub-high-precision model, and then the high-precision model is generated according to the sub-high-precision model.

In this embodiment, the semantic attribute information of each component in the traffic road BIM model may include information such as a type, a function, a material, and a positional relationship of the component. Semantic attribute information for each component is extracted from the model using BIM software or related tools. And verifying the extracted semantic attribute information to ensure the accuracy and the integrity of the semantic attribute information. At the same time, the information is collated for subsequent mapping into a high-precision model. And establishing a mapping relation with the semantic attribute information of the component in the BIM according to the structure and the component definition of the high-precision model. And assigning the verified and tidied semantic attribute information to the corresponding component in the high-precision model.

Optionally, the mapped high-precision model is verified to ensure that the semantic attribute information is correctly mapped to each component. A suitable BIM verification tool or plug-in is selected, such as Autodesk Navisworks, solibri Model Checker, etc. Loading the mapped high-precision model into a verification tool, and checking the components in the model one by one to confirm whether the semantic attribute information (such as type, material, size, function and the like) is mapped correctly. Firstly, by utilizing the batch verification function of the verification tool, whether semantic attribute information of a large number of components is consistent and accurate is rapidly checked. And comparing semantic attribute information in the model with reference data to ensure consistency of the semantic attribute information and the reference data. Second, it is checked whether there is a member lacking semantic attribute information in the model.

Any mismapped semantic attribute information is identified and corrected.

Referring to fig. 2, in one possible embodiment, step S200 may include steps S210 to S230:

Step S210, obtaining attribute information and semantic description of each component in the BIM model, and generating semantic attribute information according to the attribute information and the semantic description;

Step S220, the semantic attribute information is obtained and related to the components in the sub-high-precision model according to the identifiers of the components;

and step S230, generating the high-precision model according to the sub-high-precision model.

In this embodiment, each component in the BIM model is traversed, and the attribute information thereof is read. Including type (e.g., curb, traffic sign), size (e.g., length, width, height), material (e.g., concrete, steel), function (e.g., direction indication, illumination provided), etc. A set of semantic categories is predefined with reference to the semantic description of the road entity components. For each component, its attribute information is matched with a predefined semantic category. Each component is assigned one or more semantic tags that represent the semantic category to which it belongs. Ensuring that each component has a unique semantic identity. The generated semantic information is stored in an appropriate data structure. The unique identifier (e.g., UUID, ID, etc.) of each component is extracted, a mapping or association table is created, the identifier of the component is associated with the semantic attribute information, and the semantic attribute information is applied to the corresponding component according to the mapping table. And finally, integrating the sub high-precision model into a complete high-precision model.

And step S300, filtering the non-main body structural members in the high-precision model according to the semantic attribute information to obtain a low-precision model.

The main structural member refers to a framework of a building and mainly comprises bearing and supporting structures such as beams, columns, plates, walls and the like. Non-main structural members refer to non-load bearing and auxiliary structures of building exterior walls, roofs, doors, windows, pipes, equipment and the like.

In addition, the low-Level of Detail model (LOD 3) represents a more detailed model Level in the BIM or road information model. At the level, the extraction of the outline of the road main body becomes accurate, and meanwhile, the texture information of the materials is fully expressed, so that the requirement of displaying the texture of the real materials is met.

In this embodiment, whether the component belongs to the main body structural component or the non-main body structural component is determined according to the component type in the semantic attribute information. The main body structure comprises a pavement, a lane, a sidewalk, a bridge, a tunnel and the like, and the non-main body structure comprises a green belt, a traffic sign, a street lamp, a traffic signal lamp, a fence and the like. If the type of one component is of a non-body construction type, it is filtered. And carrying out semantic filtering operation on the high-precision model, and filtering all non-main structural members in the high-precision model to obtain the low-precision model. The main structural member, the geometric information and the texture information of the main structural member are reserved in the low-precision model, so that the basic requirement of appearance identification can be met.

Referring to fig. 2, in one possible embodiment, step S300 may include steps S310 to S320:

Step S310, traversing the semantic information, and if the semantic information corresponding to the component is not in a preset main body structural component list, determining that the component is the non-main body structural component;

And step S320, filtering the non-main body structural member from the high-precision model to obtain the low-precision model.

In this embodiment, a list including the types of the main body structural members is preset. The BIM software is used for loading the high-precision model, so that all components in the model and semantic attribute information thereof are ensured to be loaded correctly. And traversing each component in the model, and extracting corresponding semantic attribute information. Comparing the semantic information of each component with a preset main body structural component list, if the type of the component is in the list, considering the component as a main body structural component, and if the type of the component is not in the list, determining the component as a non-main body structural component. During traversal, the components identified as non-body structural components are marked.

Based on the marking results, all non-body structural members are removed or hidden from the high-precision model. The model after removal of the non-body structural members is saved as a low-precision model.

In this embodiment, the simplified low-precision model is obtained by filtering non-main body structural members in the high-precision model according to semantic attribute information. The application efficiency and the flexibility of the model can be improved, and convenience is provided for subsequent analysis and management. Compared with a size filtering mode based on screening of physical size, semantic filtering can process deeper information, and can avoid failure in effectively removing redundant information or filtering important information by mistake due to incorrect setting of a filtering threshold.

In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the above description, and will not be repeated. On this basis, referring to fig. 3, step S300 further includes steps S400 to S500:

Step S400, performing triangle mesh simplification on the low-precision model, deleting or merging redundant vertexes, and performing mesh simplification;

And S500, performing texture compression on the low-precision model subjected to grid simplification to obtain the body block model.

In this embodiment, the low-precision model is subjected to lightweight processing such as triangle mesh simplification and texture merging compression, so as to obtain a bulk model, and the complexity of components and the file size are reduced. Triangle mesh simplification is an effective lightweight processing technique that reduces model complexity and file size by reducing the number of triangle patches in a three-dimensional model. When the triangle network is simplified, firstly, vertex downsampling is carried out, and important vertices in the low-precision model are reserved through vertex downsampling algorithms such as Quadric Error Metric (QEM) and Vertex Clustering, and other redundant vertices are deleted or combined to reduce the number of the vertices. Secondly, mesh simplification is performed, reducing the number of patches by merging and collapsing the patches in the mesh. Finally, unnecessary details are removed, the internal structure of the models is removed, and the overlapped surfaces between the models are reduced. Texture merge compression techniques reduce file size by merging and compressing the textures of the model. When texture merge compression is performed, the original texture image may be downsampled (i.e., reduced in image size), simplifying the texture by reducing the resolution of the texture image. Texture mapping optimization can also be performed, and a UV mapping technology is used for mapping texture coordinates to a model surface, so that texture seams and repeated areas are reduced, and the repetition and waste of textures are reduced. The aliasing and jagged edges of texture details at different distances can also be reduced through MIPMAPPING, and rendering performance can be improved.

Optionally, the grid simplifying method can selectively simplify based on the curvature of the grid, and the curvature value of each vertex in the grid is calculated, the vertices with smaller curvature and the adjacent patches are deleted according to the size of the curvature value, so that the grid is simplified. The grid simplification method can also be used for selectively simplifying based on the normal rate of the grid, and the grid is simplified by calculating the normal direction of each vertex in the grid, sorting according to the change degree of the normal direction, and deleting the vertex with smaller change of the normal direction and the adjacent surface patches.

In the third embodiment of the present application, the same or similar content as the first embodiment of the present application can be referred to the above description, and the description thereof will not be repeated. On this basis, referring to fig. 4, step S300 further includes steps S600 to S800:

step S600, according to the semantic attribute information, obtaining a pavement component in the high-precision model, and obtaining a bounding box of the pavement component;

It should be noted that a bounding box refers to a simple geometric shape (typically a cube or cuboid) that completely encloses and conforms in shape and position to the pavement elements.

In this embodiment, in the high-precision model, each element (such as a road, a building, a tree, etc.) has specific semantic attribute information. Such information describes the type, location, size, etc. of the element. Road surface members come in a variety of types including, but not limited to, lanes (including main lanes, auxiliary lanes, bus lanes, bike lanes, etc.), sidewalks, shoulders, and intersections (including crossroads, T-junctions, loop-junctions, etc.).

In this embodiment, the high-precision model is traversed to obtain semantic attribute information of all components in the high-precision model, where the semantic attribute information includes type information (such as roads, buildings, trees, etc.) and attribute information (such as number of lanes, width, materials of roads, number of floors of buildings, height, use, etc.) of each component in the high-precision model. After the semantic attribute information is obtained, a semantic tag associated with the pavement component is located, e.g., the semantic tag belonging to the pavement component may include "lanes," "walkways," "intersections," and the like. Checking whether each component is matched with the semantic tag of the pavement component, if so, considering the component as the pavement component, and classifying and sorting the identified pavement component. For example, lanes, sidewalks and shoulders may be categorized separately and a list or database containing all relevant elements may be created for each category.

In this embodiment, first, after the road surface member in the high-precision model is determined, geometric data of the road surface member including vertex coordinates, side information, surface information, and the like is extracted from the high-precision model. Using the extracted geometric data, a geometric model of the pavement member is constructed, which may be a polygonal mesh, a triangular patch, or the like, depending on the representation and accuracy requirements of the high-precision model. Next, an empty bounding box object is created, which is a cube or cuboid with minimum and maximum coordinate points, the minimum coordinate point of the bounding box can be set to positive infinity and the maximum coordinate point to negative infinity for subsequent updating. Then, all vertices or edges in the geometric model of the pavement component are traversed, and for each vertex or edge, the minimum and maximum coordinate points of the bounding box are updated. If a certain coordinate of the vertex is smaller than the minimum coordinate point of the current bounding box, updating the coordinate point to be a new minimum coordinate point. If a certain coordinate of the vertex is larger than the maximum coordinate point of the current bounding box, updating the coordinate point to be a new maximum coordinate point. After the traversal is completed, the minimum and maximum coordinate points of the bounding box define the bounding box of the pavement member.

Optionally, the bounding box is verified to ensure that the bounding box properly encloses the pavement member. The bounding box boundaries are compared to the geometric model boundaries of the target object. It is ensured that the bounding box's boundaries completely contain the boundary of the target object, with no vertices or edges out of the bounding box's range. Intersection tests may also be performed to verify the validity of the bounding box. For example, whether the bounding box intersects the target object is detected using a method such as ray casting or separation theorem. If the intersection test result is negative (i.e., does not intersect), it indicates that the bounding box does not correctly enclose the target object, requiring adjustment. And adjusting and optimizing the bounding box according to the verification result until the bounding box meeting the requirement is obtained.

Step S700, obtaining three-dimensional coordinates of the geometric center of the bounding box, and sequencing the three-dimensional coordinates according to a route trend sequence to generate a coordinate point sequence;

and step S800, generating the symbol model according to the coordinate point sequence.

The symbolic model (LOD 1, level of Detail 1) is a hierarchy for expressing macro features of the model in three-dimensional modeling. On the level, the model is expressed in a three-dimensional vector linear form and is mainly used for showing the position and trend of the whole line of the road. The course direction refers to the continuous, ordered spatial arrangement of pavement elements in the high-definition model.

In this embodiment, for each road surface member, the minimum and maximum coordinate points of the bounding box are calculated from the geometric representation thereof (such as vertex coordinates, side information, etc.), and the three-dimensional coordinates of the geometric center thereof are calculated from the average value of the minimum and maximum coordinate points. And determining the route trend of the pavement component according to the route information or the navigation data of the high-precision model. And sorting the three-dimensional coordinate points of the geometric center of the bounding box according to the route trend. The coordinates are illustratively arranged in the order in which the vehicle or pedestrian may pass. And storing the ordered three-dimensional coordinate points in a data structure, such as an array, a list or a database, and the like, so as to obtain a coordinate sequence.

In this embodiment, a line-type vector symbol model is generated by fitting according to a sequence formed by three-dimensional coordinates of the geometric center of the bounding box. According to the characteristics of the point set and the requirements of the fitting method, proper fitting parameters (such as the order of a polynomial, the number of segments of a spline curve and the like) are selected. The point set is fitted using the selected fitting method and parameters. The fit results are converted into vector symbols, typically one or more continuous lines. And finally, carrying out further smoothing treatment or adjustment on the fitting result to ensure that the fitting result meets the requirements of actual application scenes.

Optionally, fitting is according to least squares. The point set is ordered according to the course trend and stored as a list of (x, y, z) coordinate points. For a two-dimensional fit (ignoring the z-coordinate or assuming the z-coordinate is constant), a straight line or a conic is chosen as the fitting model. For three-dimensional fitting, a plane or quadric is selected. The parameters of the fitting model are calculated using a least squares algorithm. And generating vector symbols (such as straight line segments, planes and the like) representing fitting results according to the calculated fitting model parameters.

Optionally, a polynomial fit is performed. A suitable polynomial order is selected based on the complexity of the point set and the required fitting accuracy. Based on the selected order, a polynomial function is constructed which will act as a fitting model. The coefficients of the polynomial are solved using numerical methods (e.g., gaussian elimination, QR decomposition, etc.) such that the sum of squares of the errors of the polynomial function over the point set is minimized. And generating vector symbols (such as curve segments) representing fitting results according to the polynomial function obtained by solving.

Optionally, spline curve fitting is performed. And determining the number of segments of the spline curve according to the characteristics of the point set and the required fitting precision. The location of the segmentation point is selected or calculated in the set of points. These points will be segment connection points of the spline curve. Within each segment, a polynomial function is constructed as a fitting model. These polynomial segments need to meet the smoothness condition at the segment junction. The coefficients of each polynomial segment are solved numerically while ensuring smoothness at the segment junction. And generating spline curve vector symbols representing fitting results according to the polynomial segments obtained by solving.

In the fourth embodiment of the present application, the same or similar content as the first embodiment of the present application can be referred to the above description, and the description thereof will not be repeated. On this basis, referring to fig. 5, the method may further include steps a100 to a200:

step A100, when a model switching instruction is received, analyzing the model switching instruction to obtain current model information and target model information;

And step A200, determining a switching mode according to the current model information and the target model information.

In this embodiment, a model switching instruction from a user or an upper layer application is received. The received instructions contain current model information and target model information. And analyzing the instruction to extract the current model information and the target model information. The current model information includes an identifier, type, version, etc. of the model currently being used. The target model information is an identifier, type, version, etc. of the model to be switched to specified in the instruction. And determining a strategy according to the model types before and after switching.

Referring to fig. 6, in one possible embodiment, step a200 may include steps a210 to a220:

step a210, when switching from the symbol model to the bulk model or from the bulk model to the low-precision model, the switching mode is an alternative mode;

Step a220, when switching from the low-precision model to the high-precision model, the switching mode is an additional mode.

In this embodiment, when switching from a symbolic model to a volumetric model, or from a volumetric model to a low-precision model, an alternative strategy is employed because these models may have large differences in representation methods and data structures. The current model is unloaded, and then the target model is loaded and initialized. Releasing resources occupied by the current model, such as memory, graphic rendering resources and the like, and loading and initializing the target model according to the target model information. Updating the view and data structure to a state that matches the target model.

In this embodiment, when switching from a low-precision model to a high-precision model, a superposition strategy is used, since the high-precision model is usually based on the low-precision model with more detail and precision added. And gradually adding details of the high-precision model under the condition that the low-precision model is kept unchanged, and loading detail parts of the high-precision model under the condition that the low-precision model is kept unchanged. The detail portion of the high-precision model is combined with the data of the low-precision model to generate a complete high-precision model. Finally, the view is updated to the state of the high-precision model.

Referring to fig. 7, in the present embodiment, after obtaining the high-precision model, the low-precision model, and the body block model, the multi-detail level three-dimensional tile is organized by adopting a combination of ADD (additional) and REPLACE (alternative) according to the data service standard of 3DTiles and the model visualization feature. 3DTiles is a geospatial data format for storing and distributing geospatial data. For transmitting and loading massive heterogeneous three-dimensional geospatial datasets. When an additional strategy is employed, the level of detail of the model is increased by adding new tiles on top of existing ones. When a replacement strategy is adopted, the original tile is replaced by a new tile to realize the updating of the model or the replacement of the detail level.

In this embodiment, by analyzing semantic description information of the traffic road BIM model, the semantic information is used to screen geometric information of components, construct a LOD model with multiple levels of detail, optimize data organization of the LOD model, and generate standard 3DTiles data services. The expression efficiency of the traffic road BIM model in a visual platform (such as WebGIS) is improved.

In one possible embodiment, step a220 may comprise the steps of:

In this embodiment, components added to the low-precision model are required to be selected from the high-precision model by semantic filtering. These screened components are superimposed in an additive fashion onto the low-precision model of the LOD3 level, forming a high-precision model of the LOD4 level.

In the present embodiment, first, the received switching command is analyzed to determine the type, number, position, and other addition requirements of the components to be added when switching from the low-precision model to the high-precision model. According to the adding requirement, the target semantic attribute information corresponding to the components to be added is determined, wherein the target semantic attribute information comprises the types (such as street lamps, traffic signs, drainage facilities and the like), materials, sizes, colors, functions and the like of the components. And selecting the components to be added which meet the conditions from the component library according to the target semantic attribute information. These components may be predefined models, components, or parameterized objects. Parameters of the components to be added, such as size, position, orientation, etc., are adjusted according to specific requirements to ensure their perfect fusion with the low-precision model. And adding the component to be added into the low-precision model in a superposition mode, and combining the information such as the geometric shape, the material property and the like of the component with the low-precision model. And updating the superimposed model view and data structure to the state of the high-precision model to obtain the high-precision model.

The application provides a BIM model lightening system of semantic constraint, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the BIM model lightening method of the semantic constraint in the first embodiment.

Referring now to FIG. 8, a schematic diagram of a BIM model lightweight system suitable for use in implementing semantic constraints of embodiments of the present application is shown. The semantically constrained BIM model lightweight system in embodiments of the present application may include, but is not limited to, mobile terminals such as notebook PDAs (Personal DIGITAL ASSISTANT: personal digital assistants), PADs (portable android device: tablet computers), and the like, and fixed terminals such as desktop computers, and the like. The semantically constrained BIM model lightweight system shown in fig. 8 is only one example and should not impose any limitation on the functionality and scope of use of embodiments of the present application.

As shown in fig. 8, the semantically constrained BIM model lightweight system may include a processing device 1001 (e.g., a central processor, a graphics processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data required for the operation of the semantically constrained BIM model lightweight system are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. Communication means 1009 may allow the semantically constrained BIM model lightweight system to communicate wirelessly or wiredly with other devices to exchange data. While a BIM model lightweight system is shown with semantic constraints for various systems, it should be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.

The BIM model light-weight system adopting the semantic constraint in the embodiment can solve the technical problem of how to improve the light-weight processing of the BIM model in the text. Compared with the prior art, the beneficial effects of the semantic constraint BIM model light-weight system provided by the application are the same as those of the semantic constraint BIM model light-weight method provided by the embodiment, and other technical features in the semantic constraint BIM model light-weight system are the same as those disclosed by the method of the embodiment, so that details are not repeated.

It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

The present application provides a computer readable storage medium having computer readable program instructions (i.e., a computer program) stored thereon for performing the semantically constrained BIM model weight reduction method of the above embodiments.

The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.

The computer readable storage medium may be contained in the semantically constrained BIM model weight system or may be a BIM model weight system which exists alone and is not assembled into the semantically constrained BIM model weight system.

The computer readable storage medium carries one or more programs, when the one or more programs are executed by a semantically constrained BIM model lightening system, the semantically constrained BIM model lightening system is used for constructing sub-high-precision models of all components in a BIM model to be processed according to geometric information of the components, acquiring semantic attribute information of the components, mapping the semantic attribute information into the sub-high-precision models, generating a high-precision model according to the sub-high-precision models, and filtering non-main structural components in the high-precision model according to the semantic attribute information to obtain a low-precision model.

Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.

The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer program) for executing the BIM model light weight method of semantic constraint, so that the technical problem of how to improve the light weight processing of the BIM model in the text can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the BIM model light weight method of semantic constraint provided by the embodiment, and are not repeated here.

The foregoing description is only a partial embodiment of the present application, and is not intended to limit the scope of the present application, and all the equivalent structural changes made by the description and the accompanying drawings under the technical concept of the present application, or the direct/indirect application in other related technical fields are included in the scope of the present application.

Claims

1. A semantically constrained BIM model lightweight method, characterized in that the method comprises:

Taking the project base point coordinates, azimuth information and model size units as constraint conditions, traverse each component in the BIM model to be processed, obtain and construct a sub-high-precision model of each component based on the geometric information of the component, including: determining the grid division strategy for constructing the sub-high-precision model based on whether the component is a key component; detecting the accuracy change of the sub-high-precision model according to a preset period, and adjusting the modeling parameters when the accuracy change does not meet the preset accuracy standard; the grid density of the key component is greater than that of the non-key component;

Acquiring semantic attribute information of the component, mapping the semantic attribute information to the sub-high-precision model, and generating a high-precision model according to the sub-high-precision model;

According to the semantic attribute information, the non-main structure components in the high-precision model are filtered to obtain a low-precision model, which includes: traversing the semantic attribute information, if the component type in the semantic attribute information corresponding to the component is not in the preset main structure component list, then determining that the component is the non-main structure component; filtering the non-main structure components from the high-precision model to obtain the low-precision model.

2. The semantically constrained BIM model lightweight method according to claim 1, characterized in that the method further comprises:

When a model switching instruction is received, the model switching instruction is parsed to obtain current model information and target model information;

A switching mode is determined according to the current model information and the target model information.

3. The semantically constrained BIM model lightweight method according to claim 2, characterized in that the step of determining the switching mode according to the current model information and the target model information comprises:

When switching from a symbolic model to a block model, or from a block model to the low-precision model, the switching mode is a substitution mode;

When switching from the low-precision model to the high-precision model, the switching mode is an additional mode.

4. The semantically constrained BIM model lightweight method according to claim 3, wherein the step of switching from the low-precision model to the high-precision model comprises:

Determining, according to the model switching instruction, an adding requirement and target semantic attribute information corresponding to the adding requirement;

The component to be added is determined according to the target semantic attribute information, and the component to be added is superimposed on the low-precision model to obtain the high-precision model.

5. The semantically constrained BIM model lightweight method according to claim 3, characterized in that after the step of filtering the non-main structural components in the high-precision model according to the semantic attribute information to obtain the low-precision model, it also includes:

According to the semantic attribute information, a pavement component is obtained in the high-precision model, and a bounding box of the pavement component is obtained;

Obtaining the three-dimensional coordinates of the geometric center of the bounding box, and sorting the three-dimensional coordinates according to the order of the route to generate a coordinate point sequence;

The symbol model is generated according to the coordinate point sequence.

6. The semantically constrained BIM model lightweight method according to claim 3, characterized in that after the step of filtering the non-main structural components in the high-precision model according to the semantic attribute information to obtain the low-precision model, it also includes:

Simplifying the triangulated network of the low-precision model, deleting or merging redundant vertices, and simplifying the mesh;

The low-precision model after mesh simplification is texture compressed to obtain the block model.

7. The semantically constrained BIM model lightweight method according to claim 1, characterized in that the step of obtaining the semantic attribute information of the component and mapping the semantic attribute information to the sub-high-precision model, and then generating a high-precision model according to the sub-high-precision model comprises:

Acquire the attribute information and semantic description of each of the components in the BIM model, and generate the semantic attribute information according to the attribute information and the semantic description;

Acquire and associate the semantic attribute information with the component in the sub-high-precision model according to the identifier of the component;

The high-precision model is generated according to the sub-high-precision model.