Semantically constrained BIM model lightweight method

Abstract

The invention discloses a semantic constraint BIM model lightweight method, and relates to the technical field of data processing, and the method comprises the steps: constructing a sub high-precision model of each component according to the geometric information of each component in a to-be-processed BIM model; semantic attribute information of the component is obtained, and after the semantic attribute information is mapped into the sub-high-precision model, a high-precision model is generated according to the sub-high-precision model; and according to the semantic attribute information, non-main body structure components in the high-precision model are filtered, and a low-precision model is obtained. According to the method, the model can be subjected to lightweight processing in a semantic filtering mode.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a semantically constrained BIM model lightweight method. Background Art

[0002] LOD (Levels of Detail) technology can dynamically adjust the accuracy of the model according to factors such as viewpoint distance and screen resolution. On a low-resolution screen or when the viewpoint is far away from the model, a low-precision model is displayed; on a high-resolution screen or when the viewpoint is close to the model, a high-precision model and texture are loaded. By constructing LOD models at different levels, the amount of data processing can be reduced as much as possible and the model loading speed can be accelerated while ensuring the quality of the model.

[0003] When lightweighting a BIM model, size filtering is generally used to construct LOD models at different levels of detail. When the filtering threshold is set too high, the retained data contains a large amount of redundant information, resulting in a large amount of generated data. If the filtering threshold is set too low, although some smaller redundant data can be removed, some important but slightly larger data is mistakenly filtered out, thus affecting the integrity and accuracy of the data.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a semantically constrained BIM model lightweight method, aiming to solve the technical problem of how to lightweight the BIM model.

[0006] To achieve the above objectives, the present application proposes a semantically constrained BIM model lightweight method, the method comprising: According to the geometric information of each component in the BIM model to be processed, construct a sub-high-precision model of each 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, non-main structural components in the high-precision model are filtered to obtain a low-precision model.

[0007] In one embodiment, 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.

[0008] In one 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 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.

[0009] In one embodiment, the step of switching from the low-precision model to the high-precision model includes: 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.

[0010] In one embodiment, 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, the step further 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.

[0011] In one embodiment, 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, the step further 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.

[0012] In one embodiment, after acquiring the semantic attribute information of the component and mapping the semantic attribute information to the sub-high-precision model, the step of generating a high-precision model according to the sub-high-precision model includes: 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; A high-precision model is generated according to the sub-high-precision model.

[0013] In one embodiment, 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 includes: Traversing the semantic information, if the semantic information corresponding to the component is not in the preset main structure component list, determining that the component is the non-main structure component; The non-main structural components are filtered from the high-precision model to obtain the low-precision model.

[0014] In addition, to achieve the above-mentioned objectives, the present application also proposes a semantically constrained BIM model lightweight system, the semantically constrained BIM model lightweight system comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the semantically constrained BIM model lightweight method as described above.

[0015] The present application provides a semantically constrained BIM model lightweight method, which constructs a sub-high-precision model of each component according to the geometric information of each component in the BIM model to be processed; by obtaining the geometric information of each component in the BIM model, the accuracy of the sub-high-precision model can be ensured. After obtaining the semantic attribute information of the component and mapping the semantic attribute information to the sub-high-precision model, a high-precision model is generated according to the sub-high-precision model; according to the semantic attribute information, the non-main structural components in the high-precision model are filtered to obtain a low-precision model. By mapping the semantic attribute information in the sub-high-precision model and generating a high-precision model, the non-main structural components in the high-precision model are filtered according to the semantic attribute information, thereby obtaining a simplified low-precision model. Semantic filtering can process deeper information, and can avoid the failure to effectively remove redundant information or the erroneous filtering of important information due to improper setting of the filtering threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 A flowchart diagram of Embodiment 1 of the BIM model lightweight method with semantic constraints provided in the present application; Figure 2A detailed flow chart of Embodiment 1 of the semantically constrained BIM model lightweight method provided in this application; Figure 3 A flow chart of Embodiment 2 of the semantically constrained BIM model lightweight method provided in this application; Figure 4 A flowchart diagram of Embodiment 3 of the BIM model lightweight method with semantic constraints provided in this application; Figure 5 A flowchart diagram of Embodiment 4 of the method for lightweighting a BIM model with semantic constraints provided in the present application; Figure 6 A schematic diagram of a model switching method provided in Embodiment 4 of the semantically constrained BIM model lightweight method of the present application; Figure 7 A flowchart diagram of Embodiment 4 of the method for lightweighting a BIM model with semantic constraints provided in the present application; Figure 8 Schematic diagram of the device structure of the hardware operating environment involved in the semantically constrained BIM model lightweight method in the embodiment of the present application. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The main solution of the embodiment of the present application is: construct a sub-high-precision model of each component according to the geometric information of each component in the BIM model to be processed; obtain the semantic attribute information of the component, and after mapping the semantic attribute information to the sub-high-precision model, generate a high-precision model according to the sub-high-precision model; filter the non-main structural components in the high-precision model according to the semantic attribute information to obtain a low-precision model.

[0022] LOD (Levels of Detail) technology can dynamically adjust the accuracy of the model according to factors such as viewpoint distance and screen resolution. On a low-resolution screen or when the viewpoint is far away from the model, a low-precision model is displayed; on a high-resolution screen or when the viewpoint is close to the model, a high-precision model and texture are loaded. By constructing LOD models at different levels, the amount of data processing can be reduced as much as possible and the model loading speed can be accelerated while ensuring the quality of the model.

[0023] When lightweighting a BIM model, size filtering is generally used to construct LOD models at different levels of detail. When the filtering threshold is set too high, the retained data contains a large amount of redundant information, resulting in a large amount of generated data. If the filtering threshold is set too low, although some smaller redundant data can be removed, some important but slightly larger data is mistakenly filtered out, thus affecting the integrity and accuracy of the data.

[0024] The present application provides a semantically constrained BIM model lightweight method, which constructs a sub-high-precision model of each component according to the geometric information of each component in the BIM model to be processed; by obtaining the geometric information of each component in the BIM model, the accuracy of the sub-high-precision model can be ensured. After obtaining the semantic attribute information of the component and mapping the semantic attribute information to the sub-high-precision model, a high-precision model is generated according to the sub-high-precision model; according to the semantic attribute information, the non-main structural components in the high-precision model are filtered to obtain a low-precision model. By mapping the semantic attribute information in the sub-high-precision model and generating a high-precision model, the non-main structural components in the high-precision model are filtered according to the semantic attribute information, thereby obtaining a simplified low-precision model. Semantic filtering can process deeper information, and can avoid the failure to effectively remove redundant information or the erroneous filtering of important information due to improper setting of the filtering threshold.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a device, etc. that can realize the above functions. The following takes the semantically constrained BIM model lightweight system as an example to illustrate this embodiment and the following embodiments.

[0026] Based on this, the embodiment of the present application provides a semantically constrained BIM model lightweight method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the semantically constrained BIM model lightweight method of the present application.

[0027] In this embodiment, the semantically constrained BIM model lightweight method includes steps S100 to S300: Step S100: constructing a sub-high-precision model of each component according to the geometric information of each component in the BIM model to be processed.

[0028] It should be noted that BIM (Building Information Modeling) refers to the process and results of digitally expressing the physical and functional characteristics of construction projects and facilities throughout their life cycle, and designing, constructing and operating them accordingly. The traffic road BIM model is a three-dimensional digital model built based on BIM technology by integrating information from multiple stages such as road design, construction, and operation and maintenance. The high-precision model, namely the LOD4 (Level of Detail 4) hierarchical model, is a complete component-level model with the highest level of sophistication, which can meet the needs of refined business scenario management and high-precision display. LOD (Levels of Detail), LOD technology refers to the allocation of resources for object rendering based on the position and importance of the nodes of the object model in the display environment, reducing the number of faces and detail of non-important objects, thereby obtaining efficient rendering operations.

[0029] In addition, it should be noted that the components in the BIM model refer to the basic units that constitute infrastructure such as buildings or traffic roads. In the traffic road BIM model, the components may include road pavement, roadbed, bridges, tunnels, drainage facilities, traffic signs and markings, etc. These components are accurately represented in the BIM model through 3D modeling technology and contain rich information.

[0030] In this embodiment, the traffic road BIM model can be obtained from an online platform or database, for example, from a CIM platform (platform of city information modeling, a city-wide spatiotemporal information platform). The traffic road BIM model is opened according to BIM software (such as Revit, Civil 3D, etc.), and the measurement and query tools of the BIM software are used to extract the geometric information of each component, such as length, width, height, curvature, position coordinates, etc. The extracted geometric information is sorted to form a structured data set. The data is verified to ensure the accuracy and completeness of the information. Select appropriate modeling software (such as AutoCAD, 3ds Max, SketchUp, etc.) to build a high-precision model.

[0031] In this embodiment, the road entity can be divided into a civil structure layer, a traffic facility layer, a drainage facility layer, and a safety facility layer. The civil structure layer includes the roadbed and the pavement. The traffic facility layer includes lanes, sidewalks, central dividers, and traffic islands. The drainage facility layer includes drainage ditches and drainage pipes. The safety facility layer includes traffic signs, traffic lights, guardrails, and lighting systems.

[0032] In this embodiment, when converting the traffic road BIM model into a high-precision model, in order to ensure the consistency between the high-precision model and the traffic road BIM model, parameters are pre-set as constraints. Constraints may include: project base point coordinates, azimuth information, and model dimension units. The project base point coordinates and azimuth information are used to determine the position and orientation of the BIM model in the global coordinate system. During the conversion process, it can be ensured that the generated triangular mesh model is consistent with the original BIM model in spatial position. Model dimension units, BIM models are usually modeled using specific dimensional units (such as millimeters, meters, etc.). During the conversion process, it is necessary to ensure that these units are correctly converted or retained to avoid dimensional errors. After setting the conversion constraints, all components in the traffic road BIM model are traversed and the geometric information of each component is extracted.

[0033] Optionally, before extracting the geometric information in the BIM model, the traffic road BIM model is first verified to ensure its accuracy, completeness and consistency. The verification content includes but is not limited to: the model's geometric shape, size, positional relationship, and attribute information such as material and texture.

[0034] Optionally, for different areas and components, scan data with different resolutions or grid divisions with different densities are used. According to project requirements and design specifications, key components and important areas are set. For key components and important areas, higher resolution scan data and denser grid divisions are used to build high-precision models. For example, key components can be determined based on structural stability and safety assessments. Bridges and tunnels are key structures in traffic roads, and their stability and safety are crucial. Therefore, more detailed modeling and analysis are performed. Intersections and overpasses are prone to traffic accidents due to large traffic volumes and diverse transportation modes, and more detailed modeling and analysis are required. Key components can also be determined based on geographical environment and climatic conditions. Areas with complex geological conditions and harsh climatic conditions are considered important areas, and components in important areas are considered key components.

[0035] Optionally, in the process of building the sub-high-precision model, the accuracy change of the model is monitored according to a preset period, the geometric shape, size, and positional relationship of the model are verified, the accuracy data of each verification is recorded, and compared with the previous data, the trend and cause of the accuracy change are analyzed. When it is found that the accuracy decreases or does not meet the preset accuracy standard, the modeling parameters are adjusted and optimized.

[0036] In a feasible implementation manner, step S100 may further include the following steps: Acquire texture information of each of the components in the traffic road BIM model; The texture information is mapped to the component corresponding to the high-precision model.

[0037] In this embodiment, while obtaining the geometric information of each component in the traffic road BIM model, the texture information of each component is obtained. After the high-precision model is constructed according to the geometric information, the component to be textured is selected. For each selected component, the texture mapping tool in the BIM software is used to apply the corresponding texture image to the surface of the component. The basic model is optimized in detail, such as adding edges, chamfers, textures, etc. High-quality texture materials are used to enhance the visual effect of the model. And the simulation function of the modeling software is used to simulate the effects of lighting, shadows, materials, etc. The model is further adjusted and optimized according to the simulation results.

[0038] In this embodiment, by extracting geometric information from the traffic road BIM model and selecting appropriate modeling software to construct and optimize a high-precision model, a traffic road model with high precision and realistic effects can be obtained.

[0039] Step S200: acquiring semantic attribute information of the component, 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.

[0040] In this embodiment, the semantic attribute information of each component in the traffic road BIM model, the semantic attribute information may include information such as the type, function, material and position relationship of the component. Use BIM software or related tools to extract the semantic attribute information of each component from the model. Verify the extracted semantic attribute information to ensure its accuracy and completeness. At the same time, organize the information for subsequent mapping to the high-precision model. According to the structure and component definition of the high-precision model, establish a mapping relationship with the semantic attribute information of the component in the BIM model. Assign the verified and organized semantic attribute information to the corresponding component in the high-precision model.

[0041] Optionally, verify the mapped high-precision model to ensure that the semantic attribute information has been correctly mapped to each component. Select a suitable BIM verification tool or plug-in, such as Autodesk's Navisworks, Solibri Model Checker, etc. Load the mapped high-precision model into the verification tool, check the components in the model one by one, and confirm whether their semantic attribute information (such as type, material, size, function, etc.) has been correctly mapped. First, use the batch verification function of the verification tool to quickly check whether the semantic attribute information of a large number of components is consistent and accurate. Compare the semantic attribute information in the model with the reference data to ensure their consistency. Secondly, check whether there are components in the model that lack semantic attribute information.

[0042] Identify and correct any incorrectly mapped semantic attribute information.

[0043] Please refer to Figure 2 In a feasible implementation manner, step S200 may include steps S210 to S230: Step S210, acquiring the attribute information and semantic description of each of the components in the BIM model, and generating the semantic attribute information according to the attribute information and the semantic description; Step S220, obtaining and associating the semantic attribute information with the component in the sub-high-precision model according to the identifier of the component; Step S230, generating the high-precision model according to the sub-high-precision model.

[0044] In this implementation, each component in the BIM model is traversed to read its attribute information. This includes type (such as curbstone, traffic sign), size (such as length, width, height), material (such as concrete, steel), function (such as indicating direction, providing lighting), etc. Referring to the semantic description of the road entity components, a set of semantic categories are predefined. For each component, its attribute information is matched with the predefined semantic category. One or more semantic tags are assigned to each component, which indicate the semantic category to which it belongs. Ensure that each component has a unique semantic identifier. Store the generated semantic information in an appropriate data structure. Extract the unique identifier of each component (such as UUID, ID, etc.), create a mapping table or association table, associate the identifier of the component with the semantic attribute information, and apply the semantic attribute information to the corresponding component according to the mapping table. Finally, integrate the sub-high-precision models into a complete high-precision model.

[0045] Step S300: filtering non-main structural components in the high-precision model according to the semantic attribute information to obtain a low-precision model.

[0046] It should be noted that the main structural components refer to the skeleton of the building, mainly including beams, columns, slabs, walls and other load-bearing and supporting structures. Non-main structural components refer to non-load-bearing and auxiliary structures such as building exterior walls, roofs, doors and windows, pipes, equipment, etc.

[0047] In addition, it should be noted that the low-precision model (LOD3, Level of Detail 3) represents a more detailed model level in BIM or road information model. At this level, the extraction of the main road outline becomes accurate, and the material texture information is also fully expressed to meet the needs of real material texture display.

[0048] In this embodiment, the component type in the semantic attribute information is used to determine whether the component belongs to a main structure component or a non-main structure component. Main structures include road surfaces, lanes, sidewalks, bridges, and tunnels, and non-main structures include green belts, traffic signs, street lights, traffic lights, and fences. If the type of a component belongs to a non-main structure type, it is filtered. The high-precision model is subjected to semantic filtering operations, and after filtering all non-main structure components in the high-precision model, a low-precision model is obtained. The low-precision model retains the main structure components, as well as their geometric information and material texture information, which can meet the basic requirements of appearance recognition.

[0049] Please refer to Figure 2 In a feasible implementation manner, step S300 may include steps S310 to S320: Step S310, traversing the semantic information, if the semantic information corresponding to the component is not in the preset main structure component list, determining that the component is the non-main structure component; Step S320: Filter the non-main structural components from the high-precision model to obtain the low-precision model.

[0050] In this embodiment, a list containing the types of main structure components is preset. Use BIM software to load the high-precision model to ensure that all components in the model and their semantic attribute information are correctly loaded. Traverse each component in the model and extract its corresponding semantic attribute information. Compare the semantic information of each component with the preset list of main structure components. If the type of component is in the list, it is considered that the component is a main structure component; if it is not in the list, it is determined that the component is a non-main structure component. During the traversal process, components determined to be non-main structure components are marked.

[0051] According to the marking results, remove or hide all non-main structural components from the high-precision model. Save the model after removing non-main structural components as a low-precision model.

[0052] In this embodiment, a simplified low-precision model is obtained by filtering non-main structural components in a high-precision model according to semantic attribute information. This can improve the application efficiency and flexibility of the model and facilitate subsequent analysis and management. Compared with the size filtering method based on physical size, semantic filtering can process deeper information and avoid the failure to effectively remove redundant information or the erroneous filtering of important information due to improper setting of the filtering threshold.

[0053] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3, after step S300, steps S400 to S500 are also included: Step S400, simplifying the triangulated network of the low-precision model, deleting or merging redundant vertices, and simplifying the mesh; Step S500, performing texture compression on the low-precision model after mesh simplification to obtain the block model.

[0054] In this embodiment, the low-precision model is subjected to lightweight processing such as triangulated mesh simplification, texture merging and compression to obtain a block model, thereby reducing the complexity and file size of the component. Triangulated mesh simplification is an effective lightweight processing technology that reduces the complexity and file size of the model by reducing the number of triangular facets in the three-dimensional model. When simplifying the triangulated mesh, first, vertex downsampling is performed, and the important vertices in the low-precision model are retained by vertex downsampling algorithms such as Quadric Error Metric (QEM) and Vertex Clustering, and other redundant vertices are deleted or merged to reduce the number of vertices. Secondly, mesh simplification is performed to reduce the number of facets by merging and collapsing the facets in the mesh. Finally, unnecessary details are removed, the internal structure of the model is removed, and the overlapping faces between models are reduced. Texture merging and compression technology reduces the file size by merging and compressing the texture of the model. When performing texture merging and compression, the original texture image can be downsampled (i.e., the image size is reduced), and the texture is simplified by reducing the resolution of the texture image. Texture mapping optimization can also be performed, and the texture coordinates are mapped to the model surface using UV mapping technology to reduce texture seams and repeated areas to reduce texture duplication and waste. Mipmapping can also be used to reduce aliasing and jagged edges of texture details at different distances, thereby improving rendering performance.

[0055] Optionally, the mesh simplification method can selectively simplify the mesh based on the curvature of the mesh, by calculating the curvature value of each vertex in the mesh, sorting them according to the size of the curvature value, deleting the vertices with smaller curvature and their adjacent facets, thereby achieving mesh simplification. The mesh simplification method can also selectively simplify the mesh based on the normal rate of the mesh, by calculating the normal direction of each vertex in the mesh, sorting them according to the degree of change of the normal direction, deleting the vertices with smaller normal direction changes and their adjacent facets, thereby achieving mesh simplification.

[0056] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 , after step S300, steps S600 to S800 are also included: Step S600, obtaining a pavement component in the high-precision model according to the semantic attribute information, and obtaining a bounding box of the pavement component; It should be noted that the bounding box refers to a simple geometric shape (usually a cube or a cuboid) that completely surrounds the pavement component and is consistent with the pavement component in shape and position.

[0057] In this embodiment, in the high-precision model, each element (such as roads, buildings, trees, etc.) has specific semantic attribute information. This information describes the type, location, size, etc. of the element. There are many types of pavement components, including but not limited to: lanes (including main lanes, auxiliary lanes, bus lanes, bicycle lanes, etc.), sidewalks, shoulders, and intersections (including crossroads, T-shaped intersections, roundabouts, etc.).

[0058] In this embodiment, the high-precision model is traversed to obtain the semantic attribute information of all components in the high-precision model. The semantic attribute information includes the type information (such as roads, buildings, trees, etc.) and attribute information (such as the number of lanes, width, and material of roads; the number of floors, height, and purpose of buildings, etc.) of each component in the high-precision model. After obtaining the semantic attribute information, the semantic tags related to the pavement components are located. For example, the semantic tags belonging to the pavement components may include "lane", "sidewalk", "intersection", etc. Check whether each component matches the semantic label of the pavement component. If it matches, the component is considered to be a pavement component, and the identified pavement components are classified and sorted. For example, lanes, sidewalks, and shoulders can be classified separately, and a list or database containing all relevant elements is created for each category.

[0059] In this embodiment, first, after determining the pavement component in the high-precision model, the geometric data of the pavement component is extracted from the high-precision model, including vertex coordinates, edge information, face information, etc. The extracted geometric data is used to construct a geometric model of the pavement component. The geometric model can be a polygonal mesh, a triangular facet, etc., depending on the representation method and accuracy requirements of the high-precision model. Secondly, an empty bounding box object is created. The bounding box object is a cube or a 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 can be set to negative infinity for subsequent updates. 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 less than the minimum coordinate point of the current bounding box, the coordinate point is updated to the new minimum coordinate point. If a certain coordinate of the vertex is greater than the maximum coordinate point of the current bounding box, the coordinate point is updated to the 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 component.

[0060] Optionally, the bounding box is verified to ensure that the bounding box correctly encloses the pavement component. The boundary of the bounding box is compared with the boundary of the geometric model of the target object. Ensure that the boundary of the bounding box completely contains the boundary of the target object, and no vertices or edges exceed the scope of the bounding box. An intersection test can also be performed to verify the validity of the bounding box. For example, methods such as ray casting or the separating axis theorem are used to detect whether the bounding box and the target object intersect. If the intersection test result is negative (i.e., no intersection), it indicates that the bounding box does not correctly enclose the target object and needs to be adjusted. The bounding box is adjusted and optimized based on the verification results until a bounding box that meets the requirements is obtained.

[0061] Step S700, 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; Step S800: generating the symbol model according to the coordinate point sequence.

[0062] It should be noted that the symbolic model (LOD1, Level of Detail 1) is a level used to express the macroscopic features of the model in 3D modeling. At this level, the model is expressed in the form of 3D vector lines, mainly used to show the position and direction of the entire road. The route direction refers to the continuous and orderly spatial arrangement direction of the pavement components in the high-precision model.

[0063] In this embodiment, for each pavement component, the minimum and maximum coordinate points of the bounding box are calculated according to its geometric representation (such as vertex coordinates, edge information, etc.), and the three-dimensional coordinates of its geometric center are calculated according to the average value of the minimum and maximum coordinate points. The route direction of the pavement component is determined according to the route information or navigation data of the high-precision model. According to the route direction, the three-dimensional coordinate points of the geometric center of the bounding box are sorted. Exemplarily, these coordinates are arranged in the order in which vehicles or pedestrians may pass. The sorted three-dimensional coordinate points are stored in a data structure, such as an array, a list or a database, etc., to obtain a coordinate sequence.

[0064] In this embodiment, a vector symbol model of a line type is fitted based on a sequence formed by the 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, appropriate fitting parameters (such as the order of the polynomial, the number of segments of the spline curve, etc.) are selected. The point set is fitted using the selected fitting method and parameters. The fitting result is converted into a vector symbol, usually one or more continuous lines. Finally, the fitting result is further smoothed or adjusted to ensure that it meets the requirements of the actual application scenario.

[0065] Optionally, fit according to the least squares method. Sort the point set according to the route direction and store it as a list of (x, y, z) coordinate points. For two-dimensional fitting (ignore the z coordinate or assume that the z coordinate is constant), select a straight line or a quadratic curve as the fitting model. For three-dimensional fitting, select a plane or a quadratic surface. Use the least squares algorithm to calculate the parameters of the fitting model. Based on the calculated fitting model parameters, generate a vector symbol representing the fitting result (such as a straight line segment, a plane, etc.).

[0066] Optionally, perform polynomial fitting. Select a suitable polynomial order based on the complexity of the point set and the required fitting accuracy. Based on the selected order, construct a polynomial function, which will serve as the fitting model. Use numerical methods (such as Gaussian elimination, QR decomposition, etc.) to solve the coefficients of the polynomial so that the sum of squared errors of the polynomial function on the point set is minimized. Based on the solved polynomial function, generate a vector symbol (such as a curve segment) representing the fitting result.

[0067] Optionally, a spline curve is fitted. The number of segments of the spline curve is determined based on the characteristics of the point set and the required fitting accuracy. The locations of the segmentation points are selected or calculated in the point set. These points will serve as the 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 smoothness conditions at the segment connection points. The coefficients of each polynomial segment are solved using a numerical method while ensuring smoothness at the segment connection points. Based on the solved polynomial segments, a spline curve vector symbol representing the fitting result is generated.

[0068] Based on the first embodiment of the present application, in the fourth embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above description, and will not be described in detail later. Figure 5 The method may further include steps A100 to A200: Step A100, when a model switching instruction is received, the model switching instruction is parsed to obtain current model information and target model information; Step A200: Determine a switching mode according to the current model information and the target model information.

[0069] In this embodiment, a model switching instruction is received from a user or an upper layer application. The received instruction includes current model information and target model information. The instruction is parsed to extract the current model information and target model information. The current model information includes the identifier, type, version, etc. of the model currently in use. The target model information is the identifier, type, version, etc. of the model to be switched to specified in the instruction. The strategy is determined according to the model type before and after the switch.

[0070] Please refer to Figure 6In a feasible implementation manner, step A200 may include steps A210 to A220: Step A210, when switching from the symbol model to the block model, or switching from the block model to the low-precision model, the switching mode is a substitution mode; Step A220, when switching from the low-precision model to the high-precision model, the switching method is an additional method.

[0071] In this implementation, when switching from a symbolic model to a block model, or from a block model to a low-precision model, since these models may have significant differences in representation methods and data structures, a substitution strategy is adopted. First, the current model is unloaded, and then the target model is loaded and initialized. The resources occupied by the current model, such as memory, graphics rendering resources, etc., are released, and the target model is loaded and initialized according to the target model information. The view and data structure are updated to a state that matches the target model.

[0072] In this embodiment, when switching from a low-precision model to a high-precision model, since the high-precision model usually adds more details and precision on the basis of the low-precision model, an overlay strategy is adopted. While keeping the low-precision model unchanged, the details of the high-precision model are gradually added, and while keeping the low-precision model unchanged, the details of the high-precision model are loaded. The details of the high-precision model are merged 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.

[0073] Please refer to Figure 7 In this implementation, after obtaining the high-precision model, low-precision model, and block model, a combination of ADD and REPLACE is used to organize three-dimensional tiles with multiple levels of detail according to the 3DTiles data service standard and model visualization features. 3DTiles is a geospatial data format used to store and distribute geospatial data. It is used to transmit and load massive heterogeneous three-dimensional geospatial data sets. When the append strategy is adopted, the level of detail of the model is increased by appending new tiles based on the existing tiles. When the replacement strategy is adopted, the original tiles are replaced with new tiles to achieve model updates or replacement of detail levels.

[0074] In this embodiment, by parsing the semantic description information of the traffic road BIM model, using the semantic information to filter the component geometry information, constructing a multi-level of detail LOD model, and optimizing the data organization of the LOD model, a standard 3D Tiles data service is generated, which improves the expression efficiency of the traffic road BIM model in the visualization platform (such as WebGIS).

[0075] In a feasible implementation manner, step A220 may include the following steps: 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.

[0076] In this embodiment, components that need to be added to the low-precision model are screened out from the high-precision model through semantic filtering, and these screened out components are superimposed on the low-precision model at the LOD3 level in an additional manner to form a high-precision model at the LOD4 level.

[0077] In this embodiment, first, the received switching instruction is parsed to determine the addition requirements such as the type, quantity, and position of the components to be added when switching from the low-precision model to the high-precision model. According to the addition requirements, the target semantic attribute information corresponding to the components to be added is determined, including the type of components (such as street lights, traffic signs, drainage facilities, etc.), materials, sizes, colors, functions, etc. According to the target semantic attribute information, select qualified components to be added in the component library. These components can be predefined models, components, or parametric objects. According to specific needs, the parameters of the components to be added are adjusted, such as size, position, direction, etc., to ensure that they are perfectly integrated with the low-precision model. The components to be added are added to the low-precision model in an overlaid manner, and the geometric shape, material properties, and other information of the components are merged with the low-precision model. The superimposed model view and data structure are updated to the state of the high-precision model to obtain a high-precision model.

[0078] The present application provides a semantically constrained BIM model lightweight system, which includes: at least one processor; and a memory communicatively connected to 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 semantically constrained BIM model lightweight method in the above-mentioned embodiment 1.

[0079] Reference below Figure 8 , which shows a structural schematic diagram of a BIM model lightweight system suitable for implementing the semantic constraints of the embodiment of the present application. The BIM model lightweight system with semantic constraints in the embodiment of the present application may include but is not limited to mobile terminals such as laptop computers PDA (Personal Digital Assistant), PAD (portable android device: tablet computer), etc., and fixed terminals such as desktop computers, etc. Figure 8The semantically constrained BIM model lightweight system shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0080] like Figure 8 As shown, the semantically constrained BIM model lightweight system may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the semantically constrained BIM model lightweight system are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the semantically constrained BIM model lightweight system to communicate wirelessly or wired with other devices to exchange data. Although the BIM model lightweight system with semantic constraints of various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0081] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0082] The semantically constrained BIM model lightweight system provided by the present application adopts the semantically constrained BIM model lightweight method in the above embodiment, which can solve the technical problem of how to improve the lightweight processing of BIM models in long documents. Compared with the prior art, the beneficial effects of the semantically constrained BIM model lightweight system provided by the present application are the same as the beneficial effects of the semantically constrained BIM model lightweight method provided by the above embodiment, and the other technical features of the semantically constrained BIM model lightweight system are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0083] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0084] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0085] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the semantically constrained BIM model lightweighting method in the above-mentioned embodiment.

[0086] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0087] The computer-readable storage medium may be included in the semantically constrained BIM model lightweight system; or may exist independently without being assembled into the semantically constrained BIM model lightweight system.

[0088] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the semantically constrained BIM model lightweight system, the semantically constrained BIM model lightweight system: constructs a sub-high-precision model of each component according to the geometric information of each component in the BIM model to be processed; obtains semantic attribute information of the component, and after mapping the semantic attribute information to the sub-high-precision model, generates a high-precision model according to the sub-high-precision model; and filters non-main structural components in the high-precision model according to the semantic attribute information to obtain a low-precision model.

[0089] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0090] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0091] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0092] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned semantically constrained BIM model lightweighting method, and can solve the technical problem of how to improve the lightweight processing of BIM models for long documents. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the semantically constrained BIM model lightweighting method provided in the above-mentioned embodiment, and will not be repeated here.

[0093] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A semantically constrained BIM model lightweight method, characterized in that: The method includes: According to the geometric information of each component in the BIM model to be processed, construct a sub-high-precision model of each 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, non-main structural components in the high-precision model are filtered to obtain a 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, characterized in that: 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, the following step further comprises: 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, the following step further comprises: 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: After acquiring the semantic attribute information of the component and mapping the semantic attribute information to the sub-high-precision model, the step of generating a high-precision model according to the sub-high-precision model includes: 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.

8. The semantically constrained BIM model lightweight method according to claim 1, characterized in that: 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 comprises: Traversing the semantic information, if the semantic information corresponding to the component is not in the preset main structure component list, determining that the component is the non-main structure component; The non-main structural components are filtered from the high-precision model to obtain the low-precision model.

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