Three-dimensional model data visualization processing method and device for railway engineering

Abstract

The invention provides a three-dimensional model data visualization processing method and device for railway engineering, and relates to the technical field of data processing, and the method comprises the steps: obtaining three-dimensional model data, and obtaining attribute information corresponding to the three-dimensional model data; obtaining the number of vertexes; dividing the three-dimensional model data based on attribute information of the three-dimensional model data and the number of vertexes to obtain a plurality of target data sets; performing tiling processing on the multiple pieces of three-dimensional model data in each target data set to obtain multiple pieces of three-dimensional sub-data; and rendering all the three-dimensional sub-data based on a preset rendering engine to obtain a target three-dimensional image. According to the method, the uniform size of the tiled file is ensured by recombining the three-dimensional model data structure, so that the network transmission smoothness is improved. Meanwhile, index association is established based on the attribute information and the tiled file, and a visual management structure tree conforming to railway engineering management logic is constructed, so that the overall data processing and visualization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for visualizing three-dimensional model data of a railway project. Background Art

[0002] In the data organization, processing and visualization management of railway engineering 3D models, there is a problem of large tile data caused by unreasonable data organization, which easily causes transmission blockage when loading web pages and affects the visualization effect. In addition, the current data visualization management method is inconsistent with the data organization logic of railway engineering projects. The data organization logic of professional production is loose, and it is necessary to establish a data organization index based on business logic. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for visualizing the data of a three-dimensional model of a railway project to improve the above-mentioned problem. In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides a method for visualizing three-dimensional model data of a railway project, comprising:

[0005] Acquire three-dimensional model data, and perform reconstruction processing based on naming information of the three-dimensional model data to obtain attribute information corresponding to the three-dimensional model data;

[0006] Determine the number of mesh vertices occupied by the three-dimensional model data based on a preset spatial data processing system to obtain the number of vertices;

[0007] Dividing the three-dimensional model data based on the attribute information of the three-dimensional model data and the number of vertices to obtain a plurality of target data sets;

[0008] Performing tile processing on multiple three-dimensional model data in each target data set to obtain multiple three-dimensional sub-data;

[0009] All three-dimensional sub-data are rendered based on a preset rendering engine to obtain a target three-dimensional image.

[0010] In a second aspect, the present application also provides a three-dimensional model data visualization processing device for railway engineering, comprising:

[0011] A first acquisition unit is used to acquire three-dimensional model data, and perform reconstruction processing based on the naming information of the three-dimensional model data to obtain attribute information corresponding to the three-dimensional model data;

[0012] A first determining unit, configured to determine the number of mesh vertices occupied by the three-dimensional model data based on a preset spatial data processing system to obtain the number of vertices;

[0013] A division unit, configured to divide the three-dimensional model data based on the attribute information of the three-dimensional model data and the number of vertices to obtain a plurality of target data sets;

[0014] A tiling unit, used for performing tiling processing on a plurality of three-dimensional model data in each target data set to obtain a plurality of three-dimensional sub-data;

[0015] The rendering unit is used to render all three-dimensional sub-data based on a preset rendering engine to obtain a target three-dimensional image.

[0016] In a third aspect, the present application also provides a three-dimensional model data visualization processing device for railway engineering, comprising:

[0017] Memory for storing computer programs;

[0018] A processor is used to implement the steps of the three-dimensional model data visualization processing method of the railway project when executing the computer program.

[0019] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for visualizing the three-dimensional model data of the railway project are implemented.

[0020] The beneficial effects of the present invention are:

[0021] The present invention reorganizes the 3D model data structure to ensure that the tiled files are of uniform size, thereby improving the smoothness of network transmission. At the same time, an index association is established based on attribute information and tiled files to build a visual management structure tree that conforms to the management logic of railway engineering, thereby improving the overall data processing and visualization efficiency.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1It is a schematic diagram of the flow of the visualization processing method of the three-dimensional model data of the railway engineering described in the embodiment of the present invention;

[0025] Figure 2 is a structural index of the three-dimensional model data described in the embodiment of the present invention;

[0026] Figure 3 is a structural index of the divided three-dimensional model data described in an embodiment of the present invention;

[0027] Figure 4 It is a schematic diagram of the structure of a three-dimensional model data visualization processing device for railway engineering according to an embodiment of the present invention;

[0028] Figure 5 It is a schematic diagram of the structure of the three-dimensional model data visualization processing equipment for railway engineering described in an embodiment of the present invention.

[0029] Markings in the figure: 10, first acquisition unit; 20, first determination unit; 30, division unit; 40, tiling unit; 50, rendering unit; 800, three-dimensional model data visualization processing equipment for railway engineering; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0032] Embodiment 1:

[0033] This embodiment provides a method for visualizing three-dimensional model data of a railway project.

[0034] See also Figure 1 , the figure shows that the method includes step S10, step S20, step S30, step S40 and step S50.

[0035] Step S10: Acquire the three-dimensional model data, and perform reconstruction processing based on the naming information of the three-dimensional model data to obtain attribute information corresponding to the three-dimensional model data;

[0036] Specifically, the three-dimensional model data in the railway engineering data is converted into a unified data format, and attribute information is added to the model object of each three-dimensional model data. The attribute information addition process is as follows: first, the project information, phase information, professional information and mileage section information are extracted through the naming information of the original file of the three-dimensional model data. For example, the original naming information is: Project A_Station_Preliminary Design_DK000000-DK028200, and the corresponding project information is Project A, the phase information is preliminary design, the professional information is station, and the mileage section is DK000000-DK028200; then, the name of the model object of the three-dimensional model data when it is established is read as the structure name information; finally, all the information is merged and attached to the model object of each three-dimensional model data as attribute information according to XX project / XX phase / XX professional / XX mileage section / XX structure name.

[0037] By matching project information, phase information, professional information, and mileage section information with structure names one by one, a complete hierarchical data structure is formed, which not only enhances the readability of the 3D model data, but also provides an efficient data traceability mechanism.

[0038] Step S20. Determine the number of mesh vertices occupied by the three-dimensional model data based on a preset spatial data processing system to obtain the number of vertices;

[0039] Specifically, by calculating the number of vertices of the three-dimensional model data, a numerical basis can be provided for the subsequent shape features of the model objects in the three-dimensional model data, thereby ensuring the smooth progress of subsequent steps.

[0040] Specifically, step S20 specifically includes step S21 and step S22:

[0041] Step S21. Gridding each 3D model data based on a preset spatial data processing system to obtain multiple network models;

[0042] Specifically, the GIS platform software is used to import the three-dimensional model data into the software, ensuring that the attribute information attached to the model object of each three-dimensional model data is retained during the import process, thereby generating a three-dimensional network model.

[0043] Step S22. Determine the number of mesh vertices occupied by the network model as the number of vertices of the corresponding three-dimensional model data;

[0044] Specifically, the 3D network model is converted into a mesh format through a meshing tool. After the meshing is completed, the generated mesh data is extracted to calculate the number of mesh vertices of each mesh object and obtain the number of vertices corresponding to the 3D model data. The number of vertices can be used as a criterion for evaluating the size and complexity of the model, and helps to characterize the geometric shape characteristics of the 3D model data.

[0045] Step S30: dividing the 3D model data based on the attribute information and the number of vertices of the 3D model data to obtain multiple target data sets;

[0046] Specifically, all the model objects of the 3D model data are classified according to the same project, the same profession, and the same structure name to obtain multiple data sets, such as Figure 2 and Figure 3 As shown, the 3D model data is converted from Figure 2 The structural index is divided into Figure 3 The structural index is shown. At this time, the project, major, and structure name information of the model objects of the 3D model data in each data set are consistent, and the number of mesh vertices is used to determine whether the data set needs to be further split, further simplify the model objects, and improve the flexibility and accuracy of management.

[0047] Specifically, step S30 specifically includes step S31, step S32, step S33, step S34 and step S35:

[0048] Step S31. Classify all the three-dimensional model data based on the first attribute, the third attribute and the fifth attribute to obtain a plurality of total data sets, wherein the first attribute, the third attribute and the fifth attribute of the three-dimensional model data in each total data set are the same;

[0049] Specifically, the attribute information is in an index structure, which is the first attribute, namely the project, the second attribute, namely the stage, the third attribute, namely the profession, the fourth attribute, namely the mileage section, and the fifth attribute, namely the structure name.

[0050] Step S32. Calculation operation: Calculate the vertex variance value in each total data set based on the number of vertices to obtain multiple initial variances;

[0051] Step S33. First comparison operation: compare the initial variance with the preset variance to obtain a comparison result;

[0052] Step S34. Adjustment operation: adjusting the total data set based on the comparison result and the initial variance to obtain multiple updated total data sets;

[0053] Step S35. Repeat the calculation operation, the first comparison operation and the adjustment operation until the comparison result indicates that the initial variance is not greater than the preset variance, and the corresponding total data set is the target data set;

[0054] Specifically, the variance of the number of vertices of the model objects in each total data set is calculated to measure the discreteness of the number of vertices in the current total data set. When the variance is large, it indicates that there are significant geometric shape differences between model objects with the same structural name, and the corresponding total data set needs to be further split and its attribute information updated. For example, "XX project / XX stage / XX major / XX segment / XX structure 1" and "XX project / XX stage / XX major / XX segment / XX structure 2" can be used to represent model objects with the same structural name but large geometric shape differences.

[0055] By continuously splitting the total data set, the variance of the number of vertices in all the total data sets is eventually smaller than the preset variance. The current corresponding data set grouping is an effective grouping of model objects with different geometric shapes, ensuring the flexibility and accuracy of model object management.

[0056] Step S40: tiling the multiple three-dimensional model data in each target data set to obtain multiple three-dimensional sub-data;

[0057] Specifically, each target data set is tiled based on a quadtree or octree method, and an appropriate tile side length is selected to ultimately form a three-dimensional sub-data file, namely a tile file, with an appropriate amount of data and uniform size.

[0058] Specifically, step S40 specifically includes step S41, step S42, step S43, step S44, step S45, step S46, step S47 and step S48:

[0059] Step S41. Obtaining component distribution characteristics and component complexity of each three-dimensional model data;

[0060] Specifically, component distribution characteristics are used to characterize the spatial organization and attribute distribution of model objects in three-dimensional model data; component complexity is used to characterize the geometric and attribute complexity of model objects in three-dimensional model data. This application measures component complexity by the number of edges and the number of vertices.

[0061] Step S42. Compare the number of vertices of the three-dimensional model data with a preset number, and when the number of vertices is less than the preset number, determine the initial tile side length of the three-dimensional model data based on the component distribution characteristics and the component complexity;

[0062] Specifically, the preset number in this application is set to 5000. It is considered that the three-dimensional model object with the number of vertices not less than the preset number has a higher complexity and requires a more refined tile edge length calculation, that is, the corresponding tile edge length may be smaller. Smaller tile edge length can better manage and render complex models, thereby improving loading efficiency and performance.

[0063] Specifically, step S42 specifically includes step S421, step S422, step S423, step S424 and step S425:

[0064] Step S421. Determine a standard target quantity from each target data set, where the standard target quantity is the minimum target quantity corresponding to all three-dimensional model data in the target data set;

[0065] Step S422. Calculate the ratio of the number of vertices of each three-dimensional model data to the number of standard targets to obtain multiple initial proportions;

[0066] Step S423. Determine the complexity value corresponding to each three-dimensional model data based on the initial proportion;

[0067] Specifically, the relative complexity of the 3D model data is characterized by calculating the ratio of the number of vertices of each 3D model data to the number of standard targets, and the corresponding complexity value is determined according to the relative complexity. In this application, the correspondence is made by dividing the threshold value, that is, when the relative complexity value is [1, 1.25), the corresponding complexity value is 3; when the relative complexity value is [1.25, 1.65), the corresponding complexity value is 2; when the relative complexity value is [1.65, +∞), the corresponding complexity value is 1; the above corresponding values ​​and relationships can be adjusted accordingly for different scenes, and there is no special restriction here.

[0068] Step S424. Determine the corresponding distribution characteristic value based on the component distribution characteristics;

[0069] Specifically, in this application, only the site distribution and linear distribution of the component distribution characteristics are considered. When the distribution characteristics are constructed to characterize the site distribution, the corresponding distribution characteristic value is 0.5; when the distribution characteristics are constructed to characterize the linear distribution, the corresponding distribution characteristic value is 1.

[0070] Step S425. Calculate the product of the complexity value, the distribution characteristic value and the unit threshold to obtain the initial tile side length;

[0071] Specifically, the calculation formula for the initial tile length of 3D model data with less than 5000 vertices is:

[0072] L 01 =P C ×P D×10 3

[0073] Among them, L 01 is the initial tile length of the three-dimensional model data whose vertex number is less than the preset number; C is the distribution characteristic value; P D is the complexity value.

[0074] The calculation of the initial tile side length takes into account multiple factors, making the tile size more adaptable to the characteristics of specific 3D model data. Different 3D model data has different complexity and distribution. This method can generate tiles suitable for specific scenes, ensuring that the corresponding tile size can more accurately capture the details and features in the 3D model data, which not only improves the generation efficiency of subsequent data rendering, but also ensures the clarity and readability of the rendered image.

[0075] Step S43. When the number of vertices is not less than the preset number, determining the initial tile length of the three-dimensional model data based on the component distribution characteristics, the component complexity and the number of vertices;

[0076] Specifically, it is considered that the model object with a vertex number less than a preset number has a lower complexity, and a larger tile edge length can be used for calculation, which can effectively reduce the number of tiles, optimize the management and rendering process, and thus improve loading efficiency and performance.

[0077] Specifically, step S43 specifically includes step S431, step S432, step S433 and step S434:

[0078] Step S431. Calculate the product of the complexity value and the distribution characteristic value to obtain a first value;

[0079] Step S432. Calculate the logarithmic function value with the unit threshold as the base and the number of vertices as the exponent to obtain a second value;

[0080] Step S433. Calculate the product of the first value and the unit threshold to obtain a third value;

[0081] Step S434. Calculate the ratio of the third value to the second value to obtain the initial tile length;

[0082] Specifically, the calculation formula for the initial tile length of 3D model data with a vertex number of not less than 5000 is:

[0083]

[0084] Among them, L 02 is the initial tile length of the three-dimensional model data whose vertex number is not less than the preset number; C is the distribution characteristic value; P Dis the complexity value; Vn is the number of vertices.

[0085] The appropriate tile edge length can better capture the details in the 3D model data, thereby enhancing the reliability of the analysis results and reducing the consumption of computing resources in the subsequent rendering process.

[0086] Step S44. Processing operation: tiling the three-dimensional model data based on the initial tile length to obtain a plurality of initial sub-data;

[0087] Step S45. Determine operation: determine the storage capacity of each initial sub-data to obtain multiple target capacities;

[0088] Step S46. A second comparison operation: comparing a plurality of target capacities with the preset capacity to obtain a comparison result, the comparison result including the amount by which the target capacity is greater than the preset capacity;

[0089] Step S47. Update operation: when the comparison result does not meet the set threshold range, update the initial tile side length;

[0090] Step S48. Repeat the processing operation, the determination operation, the second comparison operation and the update operation until the comparison result satisfies the set threshold range, and the corresponding initial sub-data is used as the three-dimensional sub-data;

[0091] Specifically, if there are many files larger than 5mb in the generated tiles or there are no files larger than 5mb in the generated tiles, it is necessary to halve or double the current initial tile length to generate a new initial tile length until the number of files larger than 5mb meets the set conditions, thereby avoiding the appearance of a large number of large files, reducing the performance bottleneck caused by loading large files in subsequent analysis or rendering, and improving the overall data processing efficiency.

[0092] Step S50: Rendering all three-dimensional sub-data based on a preset rendering engine to obtain a target three-dimensional image;

[0093] Specifically, in the processed tile file, i.e., the three-dimensional sub-data, a display strategy is set for the tiled file based on the attribute information. The display strategy is related to the location of the model object in the three-dimensional model data, i.e., the tile file corresponding to the three-dimensional model data of the underground structure and internal structure of the railway project is set to be invisible by default; the tile file corresponding to the three-dimensional model data of the ground structure of the railway project is set to be visible. For example, the internal structure in the railway station building is set to be invisible by default; the pile foundations of bridges, roadbeds, etc. are set to be invisible; the steel bars and floor slabs inside the station building are set to be invisible. By setting the underground and internal structures to be invisible, the complexity of the visualization interface is reduced, allowing users to view and analyze the ground structure more clearly, improving the overall visualization effect and user experience.

[0094] The preset rendering engine is used to load and render the tile file to obtain the target three-dimensional image, and then it is displayed according to the attribute information in the index structure of project-phase-discipline-mileage paragraph-structure name. Through the index structure of project-phase-discipline-mileage paragraph-structure name, the three-dimensional model data can be systematically organized and managed, so that users can understand the hierarchical relationship and structure of the data more intuitively, which is convenient for data search and access.

[0095] Embodiment 2:

[0096] like Figure 4 As shown, this embodiment provides a three-dimensional model data visualization processing device for railway engineering, the device comprising:

[0097] A first acquisition unit is used to acquire the three-dimensional model data, and perform reconstruction processing based on the naming information of the three-dimensional model data to obtain attribute information corresponding to the three-dimensional model data;

[0098] A first determining unit is used to determine the number of mesh vertices occupied by the three-dimensional model data based on a preset spatial data processing system to obtain the number of vertices;

[0099] A division unit, used for dividing the three-dimensional model data based on the attribute information and the number of vertices of the three-dimensional model data to obtain multiple target data sets;

[0100] A tiling unit, used for performing tiling processing on a plurality of three-dimensional model data in each target data set to obtain a plurality of three-dimensional sub-data;

[0101] The rendering unit is used to render all three-dimensional sub-data based on a preset rendering engine to obtain a target three-dimensional image.

[0102] In a specific implementation manner disclosed in the present application, the first determining unit includes:

[0103] A first processing unit is used to perform grid processing on each three-dimensional model data based on a preset spatial data processing system to obtain multiple network models;

[0104] The second determining unit is used to determine the number of mesh vertices occupied by the network model as the number of vertices of the corresponding three-dimensional model data.

[0105] In a specific implementation disclosed in the present application, the division unit includes:

[0106] A classification unit, used for classifying all the three-dimensional model data based on the first attribute, the third attribute and the fifth attribute to obtain a plurality of total data sets, wherein the first attribute, the third attribute and the fifth attribute of the three-dimensional model data in each total data set are the same;

[0107] A first calculation unit is used for calculation operation: calculating the vertex variance value in each total data set based on the number of vertices to obtain a plurality of initial variances;

[0108] A first comparison unit, configured for a first comparison operation: comparing the initial variance with the preset variance to obtain a comparison result;

[0109] An adjustment unit, used for an adjustment operation: adjusting the total data set based on the comparison result and the initial variance to obtain a plurality of updated total data sets;

[0110] The first repeating unit is used to repeat the calculation operation, the first comparison operation and the adjustment operation until the comparison result indicates that the initial variance is not greater than the preset variance, and the corresponding total data set is the target data set.

[0111] In a specific embodiment disclosed in the present application, the tiling unit includes:

[0112] A second acquisition unit is used to acquire component distribution characteristics and component complexity of each three-dimensional model data;

[0113] A second comparison unit is used to compare the number of vertices of the three-dimensional model data with a preset number, and when the number of vertices is less than the preset number, determine the initial tile side length of the three-dimensional model data based on the component distribution characteristics and the component complexity;

[0114] A third determining unit, configured to determine, when the number of vertices is not less than the preset number, an initial tile length of the three-dimensional model data based on the component distribution characteristics, the component complexity and the number of vertices;

[0115] The second processing unit is used for processing operations: tiling the three-dimensional model data based on the initial tile length to obtain a plurality of initial sub-data;

[0116] A fourth determining unit is used for determining an operation: determining a storage capacity of each initial sub-data to obtain a plurality of target capacities;

[0117] A third comparison unit is used for a second comparison operation: comparing the plurality of target capacities with the preset capacity to obtain a comparison result, wherein the comparison result includes an amount by which the target capacity is greater than the preset capacity;

[0118] An updating unit, used for the updating operation: when the comparison result does not meet the set threshold range, the initial tile side length is updated;

[0119] The second repeating unit is used to repeat the processing operation, the determining operation, the second comparing operation and the updating operation until the comparing result satisfies the set threshold range, and the corresponding initial sub-data is used as the three-dimensional sub-data.

[0120] In a specific implementation disclosed in the present application, the second comparison unit includes:

[0121] The second calculation unit is used to calculate the sum of the number of edge targets and the number of vertices of each three-dimensional model data to obtain the number of targets;

[0122] A fifth determining unit, configured to determine a standard target quantity from each target data set, where the standard target quantity is a minimum target quantity corresponding to all three-dimensional model data in the target data set;

[0123] A third calculation unit is used to calculate the ratio of the number of each target in each target data set to the number of standard targets to obtain multiple initial proportions;

[0124] A sixth determining unit, configured to determine a corresponding complexity value based on the initial proportion;

[0125] A seventh determining unit, configured to determine a corresponding distribution characteristic value based on the component distribution characteristic;

[0126] The fourth calculation unit is used to calculate the product of the complexity value, the distribution characteristic value and the unit threshold to obtain the initial tile side length.

[0127] In a specific implementation manner disclosed in the present application, the third determining unit includes:

[0128] A fifth calculation unit, used for calculating the product of the complexity value and the distribution characteristic value to obtain a first value;

[0129] a sixth calculation unit, configured to calculate a logarithmic function value with a unit threshold as a base and the number of vertices as an exponent to obtain a second value;

[0130] a seventh calculation unit, configured to calculate the product of the first value and the unit threshold value to obtain a third value;

[0131] The eighth calculation unit is used to calculate the ratio of the third value to the second value to obtain the initial tile length.

[0132] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0133] Embodiment 3:

[0134] Corresponding to the above method embodiment, this embodiment also provides a three-dimensional model data visualization processing device for railway engineering. The three-dimensional model data visualization processing device for railway engineering described below and the three-dimensional model data visualization processing method for railway engineering described above can be referenced to each other.

[0135] Figure 5 FIG. 8 is a block diagram of a three-dimensional model data visualization processing device 800 for railway engineering according to an exemplary embodiment. Figure 5 As shown, the 3D model data visualization processing device 800 for railway engineering may include: a processor 801 and a memory 802. The 3D model data visualization processing device 800 for railway engineering may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0136] The processor 801 is used to control the overall operation of the railway engineering 3D model data visualization processing device 800 to complete all or part of the steps in the above-mentioned railway engineering 3D model data visualization processing method. The memory 802 is used to store various types of data to support the operation of the railway engineering 3D model data visualization processing device 800, and these data may include, for example, instructions for any application or method operating on the railway engineering 3D model data visualization processing device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the three-dimensional model data visualization processing device 800 of the railway project and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0137] In an exemplary embodiment, the three-dimensional model data visualization processing device 800 for railway engineering can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processing Device, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components, and are used to execute the above-mentioned three-dimensional model data visualization processing method for railway engineering.

[0138] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned railway engineering 3D model data visualization processing method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the railway engineering 3D model data visualization processing device 800 to complete the above-mentioned railway engineering 3D model data visualization processing method.

[0139] Embodiment 4:

[0140] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the three-dimensional model data visualization processing method of railway engineering described above can refer to each other.

[0141] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for visualizing three-dimensional model data of railway engineering in the above-mentioned method embodiment.

[0142] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0143] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0144] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for visualizing three-dimensional model data of railway engineering, characterized in that: include: Acquire three-dimensional model data, and perform reconstruction processing based on naming information of the three-dimensional model data to obtain attribute information corresponding to the three-dimensional model data; Determine the number of mesh vertices occupied by the three-dimensional model data based on a preset spatial data processing system to obtain the number of vertices; Dividing the three-dimensional model data based on the attribute information of the three-dimensional model data and the number of vertices to obtain a plurality of target data sets; Performing tile processing on multiple three-dimensional model data in each target data set to obtain multiple three-dimensional sub-data; All three-dimensional sub-data are rendered based on a preset rendering engine to obtain a target three-dimensional image.

2. The method for visualizing the three-dimensional model data of a railway project according to claim 1, characterized in that , based on a preset spatial data processing system, determine the number of mesh vertices occupied by the three-dimensional model data, and obtain the number of vertices, including: Gridding each 3D model data based on a preset spatial data processing system to obtain multiple network models; The number of mesh vertices occupied by the network model is determined as the number of vertices of the corresponding three-dimensional model data.

3. The method for visualizing the three-dimensional model data of a railway project according to claim 1, characterized in that , based on the attribute information of the three-dimensional model data and the number of vertices, the three-dimensional model data is divided to obtain multiple target data sets, wherein the attribute information is in an index structure, which is the first attribute, the second attribute, the third attribute, the fourth attribute and the fifth attribute in sequence, including: Classifying all the three-dimensional model data based on the first attribute, the third attribute and the fifth attribute to obtain a plurality of total data sets, wherein the first attribute, the third attribute and the fifth attribute of the three-dimensional model data in each total data set are the same; Calculation operation: calculating the vertex variance value in each total data set based on the number of vertices to obtain multiple initial variances; First comparison operation: comparing the initial variance with a preset variance to obtain a comparison result; Adjustment operation: adjusting the total data set based on the comparison result and the initial variance to obtain a plurality of updated total data sets; The calculation operation, the first comparison operation and the adjustment operation are repeated until the comparison result indicates that the initial variance is not greater than the preset variance, and the corresponding total data set is the target data set.

4. The method for visualizing the three-dimensional model data of a railway project according to claim 1, characterized in that ,Multiple 3D model data in each target data set are tiled to obtain multiple 3D sub-data, including: Obtain component distribution characteristics and component complexity of each 3D model data; Comparing the number of vertices of the three-dimensional model data with a preset number, and when the number of vertices is less than the preset number, determining an initial tile side length of the three-dimensional model data based on the component distribution characteristics and the component complexity; When the number of vertices is not less than the preset number, determining an initial tile length of the three-dimensional model data based on the component distribution characteristics, the component complexity and the number of vertices; Processing operation: performing tile processing on the three-dimensional model data based on the initial tile length to obtain a plurality of initial sub-data; Determine operation: determine the storage capacity of each initial sub-data to obtain multiple target capacities; A second comparison operation: comparing a plurality of target capacities with a preset capacity to obtain a comparison result, wherein the comparison result includes an amount by which the target capacity is greater than the preset capacity; Update operation: when the comparison result does not meet the set threshold range, update the initial tile side length; The processing operation, the determining operation, the second comparing operation and the updating operation are repeated until the comparing result satisfies a set threshold range, and the corresponding initial sub-data is used as the three-dimensional sub-data.

5. A three-dimensional model data visualization processing device for railway engineering, characterized in that: include: A first acquisition unit is used to acquire three-dimensional model data, and perform reconstruction processing based on the naming information of the three-dimensional model data to obtain attribute information corresponding to the three-dimensional model data; A first determining unit, configured to determine the number of mesh vertices occupied by the three-dimensional model data based on a preset spatial data processing system to obtain the number of vertices; A division unit, configured to divide the three-dimensional model data based on the attribute information of the three-dimensional model data and the number of vertices to obtain a plurality of target data sets; A tiling unit, used for performing tiling processing on a plurality of three-dimensional model data in each target data set to obtain a plurality of three-dimensional sub-data; The rendering unit is used to render all three-dimensional sub-data based on a preset rendering engine to obtain a target three-dimensional image.

6. The three-dimensional model data visualization processing device for railway engineering according to claim 5, characterized in that: The first determining unit includes: A first processing unit is used to perform grid processing on each three-dimensional model data based on a preset spatial data processing system to obtain multiple network models; The second determining unit is used to determine the number of mesh vertices occupied by the network model as the number of vertices of the corresponding three-dimensional model data.

7. The three-dimensional model data visualization processing device for railway engineering according to claim 5, characterized in that: The attribute information is in an index structure, which is a first attribute, a second attribute, a third attribute, a fourth attribute and a fifth attribute in sequence, and the division unit includes: a classification unit, configured to classify all the three-dimensional model data based on the first attribute, the third attribute and the fifth attribute to obtain a plurality of total data sets, wherein the first attribute, the third attribute and the fifth attribute of the three-dimensional model data in each total data set are the same; A first calculation unit is used for calculation operation: calculating the vertex variance value in each total data set based on the number of vertices to obtain a plurality of initial variances; A first comparison unit, configured for a first comparison operation: comparing the initial variance with a preset variance to obtain a comparison result; An adjusting unit, configured to perform an adjustment operation: adjusting the total data set based on the comparison result and the initial variance to obtain a plurality of updated total data sets; The first repeating unit is used to repeat the calculation operation, the first comparison operation and the adjustment operation until the comparison result indicates that the initial variance is not greater than the preset variance, and the corresponding total data set is the target data set.

8. The three-dimensional model data visualization processing device for railway engineering according to claim 5, characterized in that: The tiling unit comprises: A second acquisition unit is used to acquire component distribution characteristics and component complexity of each three-dimensional model data; A second comparison unit is used to compare the number of vertices of the three-dimensional model data with a preset number, and when the number of vertices is less than the preset number, determine the initial tile side length of the three-dimensional model data based on the component distribution characteristics and the component complexity; A third determining unit, configured to determine, when the number of vertices is not less than the preset number, an initial tile length of the three-dimensional model data based on the component distribution characteristics, the component complexity and the number of vertices; A second processing unit is used for processing: performing tile processing on the three-dimensional model data based on the initial tile length to obtain a plurality of initial sub-data; A fourth determining unit is used for determining an operation: determining a storage capacity of each initial sub-data to obtain a plurality of target capacities; A third comparison unit, configured for a second comparison operation: comparing a plurality of target capacities with a preset capacity to obtain a comparison result, wherein the comparison result includes an amount by which the target capacity is greater than the preset capacity; An updating unit, configured to update the initial tile side length when the comparison result does not meet a set threshold range; The second repeating unit is used to repeat the processing operation, the determining operation, the second comparing operation and the updating operation until the comparison result satisfies a set threshold range, and the corresponding initial sub-data is used as the three-dimensional sub-data.

9. A three-dimensional model data visualization processing device for railway engineering, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the three-dimensional model data visualization processing method for railway engineering as claimed in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for visualizing three-dimensional model data of a railway project as claimed in any one of claims 1 to 4.

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