CN116503572A - Intelligent recruitment platform and space modeling method thereof - Google Patents
Intelligent recruitment platform and space modeling method thereof Download PDFInfo
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Abstract
The application discloses an intelligent recruitment platform and a space modeling method thereof. The method comprises the following steps: analyzing and sorting out related map data resources of the tendering cities through the tendering demands, and obtaining modeling data sources; performing data processing on a modeling data source, importing modeling information obtained by the data into a GIS database, and creating a space statistical model; carrying out three-dimensional modeling according to the space statistical model to obtain a three-dimensional space building model; and carrying out visualization processing on the three-dimensional space building model through the texture mapping. The method has the characteristics of low cost, simple operation and high automation degree by analyzing the modeling process and the visualization effect, and the constructed model can meet higher precision requirements, has better visualization effect and can provide a reliable technical solution for three-dimensional modeling and visualization of large-scale urban buildings.
Description
Technical Field
The application relates to the technical field of intelligent recruitment, in particular to an intelligent recruitment platform and a space modeling method thereof.
Background
Currently, smart city construction is becoming a hotspot in the geospatial information industry, and smart cities are being further developed as digital city concepts, and as such, three-dimensional modeling techniques of buildings are required as important components. With the update of data acquisition means and the rapid increase of data volume, building modeling methods gradually develop from manual to human-computer interaction, even full automation, from single building modeling to large-scale scene batch modeling. The intelligent recruiter is used as an important component of the intelligent city, GIS and big data application and analysis are introduced in the recruiter field, so that the improvement of the informatization level of the recruiter work can be promoted, and the transformation and the upgrading of the recruiter work can be promoted. And establishing a statistical relationship between the data through the space positions. And (3) establishing a space statistical model by using a statistical analysis method, and mining space autocorrelation and space variation rules from the data.
At present, the market recruitment platform system only displays a small amount of local building data of an industrial park and the like based on GIS and the like, but does not display the buildings around the industrial park and even in the whole city, and cannot fully embody the recruitment environment of the industrial park and the city. A three-dimensional model that can intuitively and stereoscopically represent a commercial urban-level building is needed. Based on the above, the invention provides an intelligent recruitment platform and a space modeling method thereof.
Disclosure of Invention
The application provides a space modeling method of an intelligent recruitment platform, which comprises the following steps:
analyzing and sorting out related map data resources of the tendering cities through the tendering demands, and obtaining modeling data sources;
performing data processing on a modeling data source, importing modeling information obtained by the data into a GIS database, and creating a space statistical model;
carrying out three-dimensional modeling according to the space statistical model to obtain a three-dimensional space building model;
and carrying out visualization processing on the three-dimensional space building model through the texture mapping.
The intelligent recruitment platform space modeling method comprises the steps of taking a high-resolution satellite image, a building outline electronic map and a panoramic image as modeling data sources; the modeling data sources include remote sensing image data, digital map information, elevation data, and oblique photography data.
According to the intelligent quotation platform space modeling method, modeling data are converted into a data format, then a coordinate system is used, registration data are registered according to a determined registration algorithm and control point information, a registration result data set consistent with the space position of a reference data set is finally obtained, and then coordinate system conversion is performed on data after conversion formatting, and the data are converted into a standard coordinate system, so that the data coordinate system is consistent.
The intelligent business platform space modeling method comprises the following steps of: and (3) importing the acquired high-resolution remote sensing image, the building electronic map and the panoramic image by using GIS software, performing registration and geometric correction pretreatment, and then respectively importing the high-resolution remote sensing image, the building electronic map and the panoramic image into an established GIS database for integration. The data processing specifically includes modeling attribute information extraction, building boundary extraction, and roof boundary extraction.
The intelligent business platform space modeling method comprises the steps of drawing the boundary of the side face of the building by combining a vector tool with a remote sensing image, extracting a texture image of the side face of the building, and simultaneously carrying out deformation treatment by combining a geometric correction tool; the method comprises the steps of utilizing a symbolizing tool to combine the threshold value adjusting function of a histogram, firstly extracting a building side texture gray level image, then carrying out binarization processing on the building side texture gray level image, and finally extracting a vector polygon of a building side window by utilizing an ENVI non-supervision classification model.
According to the intelligent business platform space modeling method, a mobile phone is used for shooting a side texture image of a building in combination with a long-focus lens leveling rod, the leveling rod reading is read, the side floor, the layer height and the roof height are extracted, and the structure type of the roof of the building is determined through the side view image.
According to the intelligent recruitment platform space modeling method, the ModelBuider visual modeling tool is utilized, and automatic construction of the three-dimensional model of the building main body structure is achieved by calling the data processing, file conversion, space analysis, three-dimensional analysis and script tools which are positioned in the ArcToolbox system tool box.
According to the intelligent business platform space modeling method, the three-dimensional difference product operation tool of the three-dimensional analysis tool box is called, three-dimensional difference product Boolean operation is conducted on the generated multi-Patch models of the side wall and the window and the roof two by two, the hollowed-out models of the side wall and the roof are respectively generated, and the three-dimensional difference product operation tool is expanded through script programming.
The invention also provides an intelligent recruitment platform, which comprises:
the data acquisition module is used for analyzing and sorting out related map data resources of the tendering cities through the tendering demands and acquiring modeling data sources;
the data processing module is used for carrying out data processing on the modeling data source, importing modeling information obtained by the data into the GIS database and creating a space statistical model;
the three-dimensional modeling module is used for carrying out three-dimensional modeling according to the space statistical model to obtain a three-dimensional space building model;
and the visualization module is used for carrying out visualization processing on the three-dimensional space building model through the texture mapping.
The beneficial effects realized by the application are as follows:
according to the invention, the related map data resources of the tendering cities are analyzed and arranged through the tendering demands, and a space statistical model is created according to the data. The automatic construction of the three-dimensional model of the building is realized through the design model, and the visualization of the model is realized through the texture mapping technology; the modeling process and the visualization effect are analyzed to realize the characteristics of low cost, simple operation and high automation degree, and the constructed model can meet higher precision requirements, has better visualization effect and can provide a reliable technical solution for three-dimensional modeling and visualization of large-scale urban buildings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flowchart of a method for spatial modeling of an intelligent recruitment platform according to an embodiment of the present application;
FIG. 2 is a schematic diagram of spatial modeling of an intelligent recruitment platform;
FIG. 3 is a schematic diagram of a boundary of a side of a building delineated by a vector tool in combination with a remote sensing image;
FIG. 4 is a schematic diagram of a model building flow;
fig. 5 is a schematic diagram of a model building method.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1 and 2, a first embodiment of the present application provides a spatial modeling method for an intelligent recruitment platform, including:
step 110, analyzing and sorting out related map data resources of the tendering cities through the tendering demands, and obtaining modeling data sources;
because the satellite remote sensing camera has long shooting distance and large field angle, the acquired remote sensing image has the advantages of macroscopicity and synthesis, and can realize continuous observation to form time sequence information; all commonly used high resolution satellite remote sensing images are mainly exemplified by Gao Fenji line images (GF), quick bird images (QuickBird) and IKONOS images. The workload of directly utilizing the remote sensing image to extract the outline of the building boundary is considered to be large, the accuracy of the extracted outline of the polygon is poor, and the electronic map of the outline of the building is adopted as a data source so as to improve the data acquisition and processing efficiency. Considering satellite images and building outline electronic maps, the electronic maps only can provide boundary outlines and texture structural features of building top views, the texture information of the side images of the buildings is limited, the geometric structural features of the side images of the buildings cannot be accurately depicted, hundred-degree panorama is adopted to collect image data of the side images of the buildings, and the image data are used as model texture maps through image processing, so that the visual effect of the models is improved.
High-resolution satellite images, building outline electronic maps and panoramic images are used as modeling data sources. The modeling data sources comprise remote sensing image data, digital map information, elevation data, oblique photography data and the like. The method takes mass multi-source data such as remote sensing images, building outline electronic maps, network panoramic images and the like as modeling data sources, not only can the macro boundaries of the buildings in the city range be extracted, but also the geometric details and texture detail characteristics of the buildings can be captured. By means of the GIS modeling function, the spatial information and the attribute information of the side wall and the roof structural component of the building are realized by constructing the model, the modeling accuracy and the model precision are remarkably improved, the modeling automatic preprocessing efficiency can be remarkably improved, and a foundation is laid for realizing the fine modeling of the building.
The modeling data sources are business data with complicated sources and different formats, and comprise GIS data (elevation data (dem, tif), image data (jpeg, tif), vector data (shp)), oblique photography data (osgb), artificial model data (fbx, obj, dae, 3ds, gltf), BIM data (ifc, clm), point cloud data (las, cvs) and other data. The element data is subjected to format conversion, elevation data DEM and TIF are converted into TERRAIN slices, TERRAIN format data, image data PNG, JPEG, TIF are converted into image slices, PNG, vector data, shp points, lines and planes can be converted into GEOJSON format data, vector data, shp points can be converted into I3DM format data, vector data, shp planes can be converted into B3DM format data, model data and BIM data can be converted into B3DM format data, and data contained in point cloud data can be converted into PNTS format data. And then carrying out coordinate system unification, firstly carrying out registration data according to the determined registration algorithm and control point information to finally obtain a registration result data set consistent with the spatial position of the reference data set, and then carrying out coordinate system conversion on the data after conversion formatting, and converting the data into a national standard coordinate system CGCS2000 so as to ensure that the data coordinate systems are consistent.
Step 120, performing data processing on the modeling data source, importing modeling information obtained by the data into a GIS database, and creating a space statistical model;
the data processing is specifically as follows: and (3) importing the acquired high-resolution remote sensing image, the building electronic map and the panoramic image by using GIS software, performing registration and geometric correction pretreatment, and then respectively importing the high-resolution remote sensing image, the building electronic map and the panoramic image into an established GIS database for integration. The data processing specifically includes modeling attribute information extraction, building boundary extraction, and roof boundary extraction.
In particular, modeling is to be performedAnd fusing the high-resolution remote sensing image and the panoramic image in the data source, and importing the fused image data and the building electronic map into a GIS database. Specifically adopting the formulaFusing characteristic points in the high-resolution remote sensing image and the panoramic image; wherein W is i DW, which is a feature value of the ith image of the high-resolution remote sensing image i Depth information of the ith image of the high-resolution remote sensing image, C i DC (direct current) which is characteristic value of ith image of panoramic image i The method comprises the steps of taking the depth information of an ith image of a panoramic image, wherein the value of i is 1 to n, and i is the corresponding matched image characteristic point of a high-resolution remote sensing image and the panoramic image; mu (mu) 1 、μ 2 The influence weight of the characteristic points in the high-resolution remote sensing image and the panoramic image on the fused image points is obtained.
The method comprises the steps of drawing a boundary of a side surface of a building by combining a vector tool with a remote sensing image, extracting a texture image of the side surface of the building, and simultaneously combining a geometric correction tool to deform the texture image as shown in fig. 3; the method comprises the steps of utilizing a symbolizing tool to combine the threshold value adjusting function of a histogram, firstly extracting a building side texture gray level image, then carrying out binarization processing on the building side texture gray level image, and finally extracting a vector polygon of a building side window by utilizing an ENVI non-supervision classification model.
In addition, the mobile phone is combined with the tele lens leveling rod to shoot the side texture image of the building, and the leveling rod reading is read to extract the side floor, the floor height and the roof height. Finally, the structural type of the building roof (such as flat roof and inclined roof) is determined through the side view image.
130, performing three-dimensional modeling according to the space statistic model to obtain a three-dimensional space building model;
the main structure of the building comprises side wall surfaces, doors and windows, roofs and other structures, the geometric shapes and the space of the building are generally regularly arranged, the building is very suitable for batch generation of programs and modeling, the labor intensity of operators can be remarkably reduced, and the modeling efficiency can be effectively improved. And (3) utilizing a ModelBuider visual modeling tool to automatically construct a three-dimensional model of the building main body structure by calling a data processing, file conversion, space analysis, three-dimensional analysis and script tool which are positioned in an ArcToolbox system tool box. In addition, since each model can only contain one iterator, the defects of the iterators of the model are overcome by writing a script program and creating a script tool by combining an ArcPy site package through a Python object-oriented programming language and combining an ArcToolbox tool box. Wherein:
modeling a side wall: in view of the fact that the geometric shapes and the spatial arrangements of the side walls and the doors and windows of the building are regular, and the building is built in layers, in order to realize batch construction of models, a building main structure part modeling scheme based on building classification, layered surface modeling and solid stretching modeling is provided, a layered automatic generation script program of the building main structure solid models such as the side walls and the doors and windows of the building is designed, meanwhile, a Phython object-oriented language is adopted to combine with an ArcPy site package, and the three-dimensional analysis tools of TIN surface modeling and TIN stretching in an ArcToolbox tool box are called to write and realize, and the algorithm and the program implementation mainly comprise the following 3 steps:
(1) according to polygonal points, polygonal files and output working area folders of input building components, firstly utilizing the query function of an ArcPy query cursor (SearchCursor), storing attribute field information such as the name, floor number, layer height, roof height and the like of the building by reading the polygonal point files and combining global variables, and simultaneously utilizing makedirs functions of an os library to create a building structural component name folder which is used for storing and generating a polyhedral element shpfile file of each layer of side wall of each building;
(2) copying and adjusting polygon points on the upper and lower sides of a building to the corresponding floor height respectively by calling a copy features_management () function and an Adjust3DZ_management () function by using a cyclic program, simultaneously creating TIN surfaces of each layer of the building respectively by using a CreateTin_3d () function, and then generating entity models of side walls and doors and windows of each layer by using an extradobetweeten () function;
(3) after inputting the boundary polygons of the bottom surfaces of the buildings, generating outer wall polygons through Buffer and erase, then connecting a side wall script program by combining a copy element (CopyFeatures) and an element node turning point (featureVerticeToPoints) through an element selection iterator (iterfeatefeatureselection), and automatically generating each layer of side wall three-dimensional models of each building through a model construction flow, wherein the model construction flow is shown in figure 4. In addition, the calculation performance of the side wall model is analyzed through the model operation report, and the execution time of the iterator and each tool is classified, counted and summarized through the input building polygon parameters.
(II) modeling of doors and windows: the three-dimensional model of the door and window component of the building is generated by combining the script program with modeling, and the three-dimensional model is similar to the algorithm design and implementation of the side wall script program, except that the input parameter name, the intermediate variable name and the output parameter name are changed, and the upper polygon point and the lower polygon point of the door and window of the building are respectively copied and adjusted to the corresponding floor height and the reference height by circularly combining the copy features_management () function and the Adjust3DZ_management () function; then, creating TIN surfaces of doors and windows of each layer of a building respectively by using a CreateTin_3d () function, and further generating a solid model of each layer of doors and windows by using an extradinBetveen () function, wherein the main code implementation is similar to the side wall implementation, and the description is omitted here; finally, after inputting the boundary polygon of the bottom surface of the building, generating an outer wall polygon through Buffer and erase, connecting a side wall script program by combining an element selection iterator (iterrateefeatureselection) with a copy element (CopyFeatures) and an element node point (featureVerticestopoints), and automatically generating a three-dimensional model of each layer of window of each building through a building model, wherein the model building method is shown in fig. 5.
(III) roof modeling: the roof structure of each building mainly comprises 2 structural types of flat tops and pitched tops, and forms of the two structural types are different, but each building usually only comprises one layer of roof, namely the roof is not a layered structure, therefore, a three-dimensional model of the flat tops or the pitched tops of each building can be generated by creating a model, and the main construction steps of the model comprise: the polygon model of each building roof is generated by combining the copy elements (CopyFeatures), element node points (featureverctiesto points), add fields (addfields), calculate fields (calcultefield), create TIN (CreateTin) and TIN stretching tools (extradobetween) through an element iterator (itedoefeatureselection) with the model input.
And 140, performing visualization processing on the three-dimensional space building model through the texture mapping.
In order to enhance the stereoscopic impression and visual effect of the model, the model can reflect the effect that the illumination shadow and the external light irradiate indoors for visual analysis. By calling a three-dimensional difference product operation tool of a three-dimensional analysis toolbox, performing three-dimensional difference product Boolean operation on a multi-Patch model and a roof which generate side walls and windows in advance, respectively generating hollowed-out models of the side walls and the roof, and simultaneously, expanding the three-dimensional difference product operation tool through script programming to improve hollowed-out processing efficiency, wherein the implementation of a script program mainly comprises 2 steps:
(1) inputting a reduced polyhedron element, a reduced polyhedron element class Sppfile list and an output working area respectively, and storing the reduced polyhedron element, the reduced polyhedron element class Sppfile list and the output working area in a list1 and a list2 respectively by utilizing a list container and combining a circulating program;
(2) and reading the reduced polyhedron elements and the reduced polyhedron element class Sppfile names in the list1 and the list2 through a circulation program, generating a Difference product output file Sppfile name according to a circulation variable i, and carrying out side wall three-dimensional Difference product operation by using a Difference3D_3d () function layer by layer. Meanwhile, the running performance of the report analysis model is output through model refinement, and the execution time of the script generation tool is classified, counted and summarized through inputting the three-dimensional model of each building side wall and window.
Texture mapping is performed on the model: and an image map function for extracting the side texture of the building from the panoramic image map. Considering that part of side texture of a building can be shielded by ground objects such as street trees, street lamps and the like, the shielding part is needed to be cut off by utilizing image processing software, and the image without shielding is selected as texture of a building side wall, a door window and a roof model part. And then, importing the side wall, door and window and roof component models generated by the models by using ArcGIS Pro, and endowing corresponding texture images for the side wall, the door and window and the roof by using the multi-patch texture editing function.
Example two
The second embodiment of the invention provides an intelligent recruiting platform, which comprises:
the data acquisition module is used for analyzing and sorting out related map data resources of the tendering cities through the tendering demands and acquiring modeling data sources;
the data processing module is used for carrying out data processing on the modeling data source, importing modeling information obtained by the data into the GIS database and creating a space statistical model;
the three-dimensional modeling module is used for carrying out three-dimensional modeling according to the space statistical model to obtain a three-dimensional space building model;
and the visualization module is used for carrying out visualization processing on the three-dimensional space building model through the texture mapping.
Corresponding to the above embodiment, an embodiment of the present invention provides an intelligent vendor platform, including: at least one memory and at least one processor;
the memory is used for storing one or more program instructions;
and the processor is used for running one or more program instructions for executing a space modeling method of the intelligent recruitment platform.
In accordance with the foregoing embodiments, a computer-readable storage medium is provided, where the computer-readable storage medium contains one or more program instructions for execution by a processor of a method for spatial modeling of an intelligent recruitment platform.
The disclosed embodiments provide a computer readable storage medium having stored therein computer program instructions that, when executed on a computer, cause the computer to perform a smart off platform space modeling method as described above.
In the embodiment of the invention, the processor may be an integrated circuit chip with signal processing capability. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP for short), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), a field programmable gate array (FieldProgrammable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The processor reads the information in the storage medium and, in combination with its hardware, performs the steps of the above method.
The storage medium may be memory, for example, may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable ROM (Electrically EPROM, EEPROM), or a flash Memory.
The volatile memory may be a random access memory (Random Access Memory, RAM for short) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (Double Data RateSDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (directracram, DRRAM).
The storage media described in embodiments of the present invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the present invention may be implemented in a combination of hardware and software. When the software is applied, the corresponding functions may be stored in a computer-readable medium or transmitted as one or more instructions or code on the computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention in further detail, and are not to be construed as limiting the scope of the invention, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the invention.
Claims (9)
1. The intelligent recruitment platform space modeling method is characterized by comprising the following steps of:
analyzing and sorting out related map data resources of the tendering cities through the tendering demands, and obtaining modeling data sources;
performing data processing on a modeling data source, importing modeling information obtained by the data into a GIS database, and creating a space statistical model;
carrying out three-dimensional modeling according to the space statistical model to obtain a three-dimensional space building model;
and carrying out visualization processing on the three-dimensional space building model through the texture mapping.
2. The intelligent recruitment platform space modeling method of claim 1, wherein high resolution satellite images, building outline electronic maps and panoramic images are used as modeling data sources; the modeling data sources include remote sensing image data, digital map information, elevation data, and oblique photography data.
3. The intelligent recruitment platform space modeling method of claim 1, wherein modeling data is converted into a data format, then a coordinate system is unified, registration data is carried out according to a determined registration algorithm and control point information to finally obtain a registration result data set consistent with the space position of a reference data set, and then coordinate system conversion is carried out on the data after conversion formatting, and the data is converted into a standard coordinate system, so that the data coordinate system is consistent.
4. The intelligent recruitment platform space modeling method of claim 1, wherein the data processing is specifically: and (3) importing the acquired high-resolution remote sensing image, the building electronic map and the panoramic image by using GIS software, performing registration and geometric correction pretreatment, and then respectively importing the high-resolution remote sensing image, the building electronic map and the panoramic image into an established GIS database for integration. The data processing specifically includes modeling attribute information extraction, building boundary extraction, and roof boundary extraction.
5. The intelligent recruitment platform space modeling method of claim 1, wherein a vector tool is used to delineate the boundary of the side of the building in combination with the remote sensing image, the texture image of the side of the building is extracted therefrom, and the deformation processing is performed on the texture image in combination with a geometric correction tool; the method comprises the steps of utilizing a symbolizing tool to combine the threshold value adjusting function of a histogram, firstly extracting a building side texture gray level image, then carrying out binarization processing on the building side texture gray level image, and finally extracting a vector polygon of a building side window by utilizing an ENVI non-supervision classification model.
6. The intelligent recruitment platform space modeling method according to claim 1, wherein a mobile phone is used for shooting a side texture image of a building in combination with a tele lens leveling rod, the leveling rod reading is read, the side floor, the layer height and the roof height are extracted, and the structure type of the roof of the building is determined through a side view image.
7. The intelligent recruitment platform space modeling method of claim 1, wherein the automatic construction of the three-dimensional model of the building main body structure is realized by calling a model builder visual modeling tool, and by calling a default data processing, file conversion, space analysis, three-dimensional analysis and script tool in an ArcToolbox system tool box.
8. The intelligent recruitment platform space modeling method of claim 1, wherein three-dimensional difference product boolean operations are performed on the generated multi-patch models of the side wall and the window and the roof two by calling a three-dimensional difference product operation tool of a three-dimensional analysis tool box, hollow models of the side wall and the roof are respectively generated, and the three-dimensional difference product operation tool is expanded through script programming.
9. An intelligent recruiting platform, comprising:
the data acquisition module is used for analyzing and sorting out related map data resources of the tendering cities through the tendering demands and acquiring modeling data sources;
the data processing module is used for carrying out data processing on the modeling data source, importing modeling information obtained by the data into the GIS database and creating a space statistical model;
the three-dimensional modeling module is used for carrying out three-dimensional modeling according to the space statistical model to obtain a three-dimensional space building model;
and the visualization module is used for carrying out visualization processing on the three-dimensional space building model through the texture mapping.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116958476A (en) * | 2023-07-31 | 2023-10-27 | 深圳嘉瑞建设信息科技有限公司 | Building visual modeling method and system based on BIM data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116958476A (en) * | 2023-07-31 | 2023-10-27 | 深圳嘉瑞建设信息科技有限公司 | Building visual modeling method and system based on BIM data |
| CN116958476B (en) * | 2023-07-31 | 2024-03-15 | 深圳嘉瑞建设信息科技有限公司 | Building visual modeling method and system based on BIM data |
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