Railway engineering three-dimensional model data visualization processing method and device

Abstract

The application provides a kind of railway engineering three-dimensional model data visualization processing method and device, it is related to data processing technical field, including obtaining three-dimensional model data, the attribute information corresponding to three-dimensional model data is obtained;Get vertex quantity;Based on the attribute information and vertex quantity of three-dimensional model data, the three-dimensional model data is divided, and a plurality of target data sets are obtained;Tile processing is carried out on a plurality of three-dimensional model data in each target data set, and a plurality of three-dimensional sub data are obtained;Based on the rendering engine of preset, all three-dimensional sub data are rendered and processed, and target three-dimensional image is obtained.The application reorganizes three-dimensional model data structure, ensures that tile file size is uniform, so as to improve network transmission smoothness.At the same time, based on attribute information and tile file index association is established, the visualization management structure tree conforming to railway engineering management logic is constructed, so as to improve overall data processing and visualization efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a three-dimensional model data visualization processing method and device for railway engineering. BACKGROUND

[0002] In the data organization, processing and visualization management of railway engineering three-dimensional models, there is a large tile data problem caused by unreasonable data organization methods, which can easily cause transmission blockage when loading web pages and affect the visualization effect. In addition, the current data visualization management method does not conform to the data organization logic of railway engineering projects, and the professional production data organization logic is loose, and a data organization index based on business logic needs to be established. SUMMARY

[0003] The purpose of the present application is to provide a three-dimensional model data visualization processing method and device for railway engineering to improve the above problems. In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0004] In a first aspect, the present application provides a three-dimensional model data visualization processing method for railway engineering, comprising:

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

[0006] determining the number of grid 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 tiling processing on a plurality of three-dimensional model data in each target data set to obtain a plurality of three-dimensional sub-data;

[0009] performing rendering processing on all three-dimensional sub-data 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 obtaining unit configured to obtain 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 grid vertices occupied by the three-dimensional model data based on a preset spatial data processing system to obtain the number of vertices;

[0013] a partition unit configured to partition the three-dimensional model data based on 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 configured to perform tiling processing on the plurality of three-dimensional model data in each target data set to obtain a plurality of three-dimensional sub-data;

[0015] a rendering unit configured to perform rendering processing on 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 further provides a railway engineering three-dimensional model data visualization processing device, comprising:

[0017] a memory configured to store a computer program;

[0018] a processor configured to execute the computer program to implement the steps of the railway engineering three-dimensional model data visualization processing method.

[0019] In a fourth aspect, the present application further provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the railway engineering three-dimensional model data visualization processing method.

[0020] The present application has the following beneficial effects:

[0021] The present application recombines the three-dimensional model data structure to ensure uniform tiling file size, thereby improving network transmission smoothness. Meanwhile, the attribute information and the tiling file are indexed and associated to construct a visualization management structure tree conforming to the railway engineering management logic, thereby improving overall data processing and visualization efficiency.

[0022] Other features and advantages of the present application will be illustrated in the following description, and some will become apparent from the description, or will be understood through implementation of the embodiments of the present application. The purposes and other advantages of the present application can be achieved and obtained by the structures specifically indicated in the written description, claims, and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0024] Figure 1A flowchart of a method for visualizing three-dimensional model data of a railway engineering according to an embodiment of the present application is shown in FIG. 1.

[0025] Figure 2 A structure index of three-dimensional model data according to an embodiment of the present application is shown in FIG. 2.

[0026] Figure 3 A structure index of divided three-dimensional model data according to an embodiment of the present application is shown in FIG. 3.

[0027] Figure 4 A structure diagram of a device for visualizing three-dimensional model data of a railway engineering according to an embodiment of the present application is shown in FIG. 4.

[0028] Figure 5 A structure diagram of an apparatus for visualizing three-dimensional model data of a railway engineering according to an embodiment of the present application is shown in FIG. 5.

[0029] In the figure, 10 is a first obtaining unit, 20 is a first determining unit, 30 is a dividing unit, 40 is a tiling unit, 50 is a rendering unit, 800 is an apparatus for visualizing three-dimensional model data of a railway engineering, 801 is a processor, 802 is a memory, 803 is a multimedia component, 804 is an I / O interface, and 805 is a communication component. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings of the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application but not all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

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

[0032] Embodiment 1

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

[0034] Referring to Figure 1 , the method comprises steps S10, S20, S30, S40 and S50.

[0035] Step S10. Obtain 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;

[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, stage information, professional information and mileage paragraph information are extracted from the naming information of the three-dimensional model data original file, for example, the original naming information is: project A_ station _ preliminary design _ DK000000-DK028200, the corresponding project information is project A, the stage information is preliminary design, the professional information is station, and the mileage paragraph is DK000000-DK028200; then the naming when the model object of the three-dimensional model data is established is read as the structure name information; finally, all information is combined and attached to the model object of each three-dimensional model data as attribute information according to XX project / XX stage / XX professional / XX mileage paragraph / XX structure name.

[0037] By one-to-one correspondence of the project information, stage information, professional information, mileage paragraph information and structure name, a complete hierarchical data structure is formed, which not only enhances the readability of the three-dimensional model data, but also provides an efficient data tracing mechanism.

[0038] Step S20. Determine the number of grid 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 basis can be provided for subsequent shape feature extraction of the model object in the three-dimensional model data, ensuring the smooth progress of the subsequent steps.

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

[0041] Step S21. Perform grid processing on each three-dimensional model data based on a preset spatial data processing system to obtain a plurality of network models;

[0042] Specifically, the three-dimensional model data is imported into the GIS platform software, and the attribute information added to the model object of each three-dimensional model data is ensured to be retained during the import process, thereby generating a three-dimensional network model.

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

[0044] Specifically, the three-dimensional network model is converted into a grid format by a meshing tool. After meshing is completed, the generated grid data is extracted to calculate the number of grid vertices of each grid object, and the number of vertices corresponding to the three-dimensional model data is obtained. The number of vertices can be used as a standard for evaluating the size and complexity of the model, and is helpful for representing the geometric shape features of the three-dimensional model data.

[0045] Step S30. Divide the three-dimensional model data based on the attribute information and the number of vertices of the three-dimensional model data to obtain a plurality of target data sets;

[0046] Specifically, the model objects of all three-dimensional model data are classified according to the same project, the same specialty, and the same structure name to obtain a plurality of data sets, as shown in Figure 2 and Figure 3 The three-dimensional model data is divided from the structure index of Figure 2 to the structure index shown in Figure 3 At this time, the project, specialty, and structure name information of the model objects of the three-dimensional model data in each data set are consistent, and whether the data set needs to be further split is determined according to the number of grid vertices of

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

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

[0049] Specifically, the attribute information is in an index structure, and in sequence, the first attribute is the project, the second attribute is the stage, the third attribute is the specialty, the fourth attribute is the mileage paragraph, and the fifth attribute is 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 a plurality of initial variances;

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

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

[0053] Step S35. Repeat the calculation operation, the first comparison operation and the adjustment operation until the comparison result characterizes 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 object in each total data set is calculated to measure the dispersion degree of the number of vertices in the current total data set. When the variance is large, it indicates that there is a significant geometric shape difference between the model objects with the same structure name, and the corresponding total data set needs to be further split and the attribute information thereof is updated. For example, “XX project / XX stage / XX professional / XX section / XX structure 1” and “XX project / XX stage / XX professional / XX section / XX structure 2” can be used to represent the model objects with the same structure name but large geometric modeling difference.

[0055] Through continuous splitting of the total data set, the variance of the number of vertices in all total data sets is finally less than the preset variance, and the current corresponding data set grouping is an effective grouping of model objects with different geometric shapes, which ensures the flexibility and accuracy of model object management.

[0056] Step S40. Tile processing is performed on the plurality of three-dimensional model data in each target data set to obtain a plurality of three-dimensional sub-data;

[0057] Specifically, the tile processing is performed on each target data set based on a quadtree or octree method, and an appropriate tile side length is selected to finally form three-dimensional sub-data, i.e., tile files, with appropriate data quantity 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. Obtain the component distribution feature and the component complexity of each three-dimensional model data;

[0060] Specifically, the component distribution feature is used to represent the spatial organization and attribute distribution of the model object in the three-dimensional model data, and the component complexity is used to represent the geometric and attribute complexity of the model object in the three-dimensional model data. The component complexity is measured 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 an initial tile side length of the three-dimensional model data based on the component distribution feature and the component complexity;

[0062] Specifically, the preset number in the application is set to 5000, and it is considered that the three-dimensional model object with a vertex number not less than the preset number has high complexity, and a more refined tile edge length needs to be calculated, 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 steps S421, S422, S423, S424 and S425:

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

[0065] Step S422. Calculate the ratio of the vertex number of each three-dimensional model data to the standard target number to obtain a plurality of 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 each three-dimensional model data is represented by calculating the ratio of the vertex number of each three-dimensional model data to the standard target number, and the corresponding complexity value is determined according to the relative complexity. In the application, the corresponding relationship is determined by dividing the threshold value, that is, when the relative complexity is [1, 1.25), the corresponding complexity value is 3; when the relative complexity is [1.25, 1.65), the corresponding complexity value is 2; when the relative complexity is [1.65, +∞), the corresponding complexity value is 1. The above corresponding values and relationships can be adjusted according to different scenarios, which are not particularly limited here.

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

[0069] Specifically, only the site distribution and linear distribution in the component distribution feature are considered in the application. When the component distribution feature represents the site distribution, the corresponding distribution feature value is 0.5; when the component distribution feature represents the linear distribution, the corresponding distribution feature value is 1.

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

[0071] Specifically, the initial tile length calculation formula of the three-dimensional model data with a vertex number less than 5000 is:

[0072] L 01 =P C ×P D×10 3

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

[0074] The calculation of the initial tile length takes into account multiple factors, so that the size of the tile is more suitable for the characteristics of the specific three-dimensional model data. Different three-dimensional model data has different complexity and distribution, and through this method, a tile suitable for a specific scene can be generated, ensuring that the corresponding tile size can more accurately capture the details and characteristics in the three-dimensional model data, both improving the generation efficiency of subsequent data rendering and ensuring the clarity and readability of the rendered image.

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

[0076] Specifically, the model object with the vertex number less than the preset number is considered to have low complexity, and a larger tile length can be calculated to reduce the number of tiles, optimize the management and rendering process, and thus improve the loading efficiency and performance.

[0077] Specifically, step S43 specifically includes steps S431, S432, S433 and 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 of the vertex number with the unit threshold as the base 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 initial tile length calculation formula of the three-dimensional model data with the vertex number not less than 5000 is:

[0083]

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

[0085] An appropriate tile edge length size can better capture the details in the three-dimensional 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: tile the three-dimensional model data based on the initial tile length to obtain a plurality of initial sub-data;

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

[0088] Step S46. Second comparison operation: compare the plurality of target capacities with the preset capacity to obtain a comparison result, the comparison result including the number of target capacities greater than the preset capacity;

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

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

[0091] Specifically, if there are many files greater than 5mb in the generated tiles or there are no files greater than 5mb in the generated tiles, a new initial tile length is generated based on half or double the current initial tile length, until the number of files greater than 5mb satisfies the set condition, avoiding the appearance of a large number of large files, reducing the performance bottleneck caused by loading large files in the subsequent analysis or rendering process, and improving the overall data processing efficiency.

[0092] Step S50. Rendering processing of 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, and the display strategy is related to the position 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 engineering is set to be invisible by default; the tile file corresponding to the three-dimensional model data of the aboveground structure of the railway engineering is set to be visible. For example, the internal structure in the railway station building is set to be invisible by default; the pile foundation of the bridge, roadbed, etc. is set to be invisible; the reinforcement, floor slab, etc. inside the station building is set to be invisible. By setting the underground and internal structure to be invisible, the complexity of the visualization interface is reduced, enabling users to more clearly view and analyze the aboveground structure, thereby improving the overall visualization effect and user experience.

[0094] The preset rendering engine is used to load and render display the tile file, to obtain a target three-dimensional image, and to display according to attribute information in an index structure of project-stage-specialty-milestone paragraph-structure name. Through the index structure of project-stage-specialty-milestone paragraph-structure name, three-dimensional model data can be systematically organized and managed, so that a user can more intuitively understand the hierarchical relationship and structure of the data, and data searching and accessing are facilitated.

[0095] Embodiment 2

[0096] As shown in Figure 4 The embodiment provides a three-dimensional model data visualization processing device for railway engineering, and the device comprises:

[0097] A first acquisition unit is configured to 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.

[0098] A first determination unit is configured to determine a number of grid 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 is 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.

[0100] A tiling unit is configured to perform 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] A rendering unit is configured to perform rendering processing on all three-dimensional sub-data based on a preset rendering engine, to obtain a target three-dimensional image.

[0102] In one specific embodiment disclosed in the present application, the first determination unit comprises:

[0103] A first processing unit is configured to perform meshing processing on each three-dimensional model data based on a preset spatial data processing system, to obtain a plurality of network models.

[0104] A second determination unit is configured to determine a number of grid vertices occupied by the network model as the number of vertices of the corresponding three-dimensional model data.

[0105] In one specific embodiment disclosed in the present application, the division unit comprises:

[0106] A classification unit is configured to classify all three-dimensional model data based on the first attribute, the third attribute and the fifth attribute, to obtain a plurality of total data sets, and 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] The first computing unit is configured to perform a computing operation of calculating a vertex variance value in each total data set based on the number of vertices to obtain a plurality of initial variances.

[0108] The first comparing unit is configured to perform a first comparing operation of comparing the initial variances with preset variances to obtain a comparison result.

[0109] The adjusting unit is configured to perform an adjusting operation of adjusting the total data sets based on the comparison result and the initial variances to obtain a plurality of updated total data sets.

[0110] The first repeating unit is configured to repeat the computing operation, the first comparing operation and the adjusting operation until the comparison result indicates that the initial variances are not greater than the preset variances, and the corresponding total data set is a target data set.

[0111] In one specific embodiment disclosed in the present application, the tiling unit comprises:

[0112] The second obtaining unit is configured to obtain a component distribution feature and a component complexity of each three-dimensional model data.

[0113] The second comparing unit is configured 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 an initial tile length of the three-dimensional model data based on the component distribution feature and the component complexity.

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

[0115] The second processing unit is configured to perform a processing operation of performing a tiling processing on the three-dimensional model data based on the initial tile length to obtain a plurality of initial sub-data.

[0116] The fourth determining unit is configured to perform a determining operation of determining a storage capacity of each initial sub-data to obtain a plurality of target capacities.

[0117] The third comparing unit is configured to perform a second comparing operation of comparing the plurality of target capacities with preset capacities to obtain a comparison result, and the comparison result comprises a number of target capacities greater than the preset capacities.

[0118] The updating unit is configured to perform an updating operation of updating the initial tile length when the comparison result does not satisfy a set threshold range.

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

[0120] In an embodiment disclosed in the application, the second comparison unit comprises:

[0121] The second calculation unit is configured to calculate a sum of edge target quantity and vertex quantity of each three-dimensional model data to obtain a target quantity;

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

[0123] The third calculation unit is configured to calculate a ratio of each target quantity and the standard target quantity in each target data set to obtain a plurality of initial proportions;

[0124] The sixth determination unit is configured to determine a complexity value corresponding to the initial proportions;

[0125] The seventh determination unit is configured to determine a distribution feature value corresponding to the component distribution feature;

[0126] The fourth calculation unit is configured to calculate a product of the complexity value, the distribution feature value and the unit threshold to obtain an initial tile edge length.

[0127] In an embodiment disclosed in the application, the third determination unit comprises:

[0128] The fifth calculation unit is configured to calculate a product of the complexity value and the distribution feature value to obtain a first numerical value;

[0129] The sixth calculation unit is configured to calculate a logarithmic function value of the vertex quantity with the unit threshold as the base to obtain a second numerical value;

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

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

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

[0133] Embodiment 3:

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

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

[0136] The processor 801 is configured to control the overall operation of the railway engineering three-dimensional model data visualization processing device 800 to complete all or part of the steps of the railway engineering three-dimensional model data visualization processing method described above. The memory 802 is configured to store various types of data to support the operation of the railway engineering three-dimensional model data visualization processing device 800, which can include, for example, instructions for any application or method operating on the railway engineering three-dimensional model data visualization processing device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, and the like. 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 storage, flash memory, magnetic disk or optical disk. The multimedia component 803 can include a screen and an audio component. The screen can be a touch screen, for example, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 802 or transmitted through 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, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 805 is configured to enable wired or wireless communication between the railway engineering three-dimensional model data visualization processing device 800 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 a Wi-Fi module, a Bluetooth module, and an NFC module.

[0137] In an example embodiment, the railway engineering three-dimensional model data visualization processing device 800 can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic elements for executing the railway engineering three-dimensional model data visualization processing method described above.

[0138] In another example embodiment, a computer readable storage medium including program instructions is also provided, which when executed by a processor, implements the steps of the railway engineering three-dimensional model data visualization processing method described above. For example, the computer readable storage medium can be the memory 802 described above including program instructions, which can be executed by the processor 801 of the railway engineering three-dimensional model data visualization processing device 800 to complete the railway engineering three-dimensional model data visualization processing method described above.

[0139] Embodiment 4:

[0140] Corresponding to the method embodiments above, in this embodiment, a readable storage medium is also provided, which can be referred to in combination with the railway engineering three-dimensional model data visualization processing method described above.

[0141] A readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the railway engineering three-dimensional model data visualization processing method of the method embodiments described above.

[0142] The readable storage medium can be specifically a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various readable storage media that can store program codes.

[0143] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0144] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0144] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0144] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0144] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0144] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in

Claims

1. A method for visualizing three-dimensional model data of a railway project, 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; Determining 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 multiple target data sets; Performing tile processing on multiple 3D model data in each target data set to obtain multiple 3D sub-data; Rendering all 3D sub-data based on a preset rendering engine to obtain a target 3D image; The 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 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; 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 comparison result satisfies a set threshold range, and the corresponding initial sub-data is used as the three-dimensional sub-data.

2. The method for visualizing the three-dimensional model data of a railway project according to claim 1, characterized in that ,determine the number of mesh vertices occupied by the three-dimensional model data based on a preset spatial data processing system, 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 , dividing the three-dimensional model data based on the attribute information of the three-dimensional model data and the number of vertices 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 three-dimensional model data based on the first attribute, the third attribute, and the fifth attribute to obtain multiple total data sets, wherein the three-dimensional model data in each total data set have the same first attribute, third attribute, and fifth attribute; 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 multiple 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. A three-dimensional model data visualization processing device for railway engineering, characterized in that: include: a first acquiring unit, configured to 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; A first determining unit is 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 dividing 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 is used to perform tiling processing on multiple 3D model data in each target data set to obtain multiple 3D sub-data; A rendering unit, configured to render all three-dimensional sub-data based on a preset rendering engine to obtain a target three-dimensional image; The tiling unit includes: A second acquisition unit is used to acquire component distribution characteristics and component complexity of each three-dimensional model data; a second comparing unit, configured 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 an 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 configured to perform a tiling process 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 configured to determine a storage capacity of each initial sub-data to obtain a plurality of target capacities; a third comparing unit, configured 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; 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 configured 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 use the corresponding initial sub-data as the three-dimensional sub-data.

5. The device for visualizing three-dimensional model data of railway engineering according to claim 4, characterized in that: The first determining unit includes: A first processing unit is configured to perform gridding 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 configured to determine the number of mesh vertices occupied by the network model as the number of vertices of the corresponding three-dimensional model data.

6. The device for visualizing three-dimensional model data of railway engineering according to claim 4, characterized in that: 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, and the division unit includes: a classification unit, configured to classify all 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 three-dimensional model data in each total data set have the same first attribute, third attribute, and fifth attribute; A first calculation unit is configured to calculate a 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 is configured to perform a first comparison operation: comparing the initial variance with a preset variance to obtain a comparison result; an adjusting unit, configured to adjust 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 configured to repeat the calculation operation, the first comparison operation, and the adjustment operation until the comparison result indicates that the initial variance is no greater than the preset variance, and the corresponding total data set is the target data set.

7. 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 method for visualizing three-dimensional model data of a railway project as claimed in any one of claims 1 to 3 when executing the computer program.

8. 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 3.

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