CN118781284A - Railway multimodal spatiotemporal information adaptive visualization engine and method - Google Patents

Abstract

The application provides a railway multi-mode space-time information self-adaptive visualization engine and a method, wherein the engine comprises the following components: the system comprises a data layer, a calculation layer, a service layer and a display layer which are in communication connection; the data layer stores the railway space-time data in a classified mode and organizes the railway space-time data in a self-adaptive mode, the calculation layer provides calculation support for the service layer, and the service layer generates railway space-time service data aiming at a railway service scene based on a micro-service architecture; the display layer carries out railway three-dimensional visual scene construction and semantic enhancement processing according to railway space-time service data, and displays enhanced visual effect data of railway service scenes in various types of user terminals based on the obtained railway three-dimensional visual scene data. The method and the system can realize the self-adaptive visualization of the railway three-dimensional scene, improve the railway space-time data access efficiency, realize the efficient shared release and the flexible service management of the service, improve the visualization effect of the railway service scene and enrich the semantic information of the railway service scene.

Description

Railway multimodal spatiotemporal information adaptive visualization engine and method

Technical Field

The present application relates to the field of railway data service technology, and in particular to a railway multimodal spatiotemporal information adaptive visualization engine and method.

Background Art

With the development of new surveying and mapping technologies such as drone oblique photography, non-contact 3D laser scanning, measurable real-scene photography, and high-precision information collection in recent years, the railway field has continuously acquired multi-modal spatiotemporal element information with large data volume, wide sources, diverse forms, and linear strip distribution, including geography, geology, 3D models, satellite observations, tunnel point clouds, location navigation, real-scene images, etc. How to efficiently organize massive multi-modal railway spatiotemporal element information through the use of spatiotemporal information visualization technology, realize the coupled expression of cross-domain spatiotemporal elements, and at the same time convey rich scene semantic information is a difficult challenge that needs to be overcome in the railway field.

At present, the existing visualization engines are not universal and have insufficient functional support for realizing railway three-dimensional scene visualization. The railway field mainly uses existing visualization engines to realize scene visualization of a single user terminal based on a browser. It is unable to efficiently and adaptively organize multimodal spatiotemporal element information and realize adaptive visualization based on diversified visualization terminals. It also lacks scene semantic expression information, which seriously restricts users' cognitive ability of railway three-dimensional scenes.

Summary of the invention

In view of this, the embodiments of the present application provide a railway multimodal spatiotemporal information adaptive visualization engine and method to eliminate or improve one or more defects existing in the prior art.

One aspect of the present application provides a railway multimodal spatiotemporal information adaptive visualization engine, comprising: a data layer, a computing layer, a service layer, and a presentation layer that are sequentially communicatively connected;

The data layer is used to classify, store and adaptively organize railway spatiotemporal data used to represent railway multimodal spatiotemporal information;

The computing layer is used to adopt a cloud-edge collaborative computing architecture for the railway business scenario currently specified by the user, and call the railway spatiotemporal data stored in the data layer to provide computing power support for the service layer, so as to obtain spatiotemporal analysis and calculation results for the railway business scenario;

The service layer is used to generate railway spatiotemporal service data for the railway business scenario based on a preset microservice architecture and according to the spatiotemporal analysis calculation results for the railway business scenario;

The presentation layer is used to construct a railway three-dimensional visualization scene and perform semantic enhancement processing based on the railway spatiotemporal service data for the railway business scenario called from the service layer, so as to obtain the railway three-dimensional visualization scene data for the railway business scenario, and based on the railway three-dimensional visualization scene data, display the enhanced visualization effect data corresponding to the railway business scenario in various types of user terminals.

In some embodiments of the present application, the data layer includes: a railway spatiotemporal data classification storage module communicatively connected to the computing layer, and a railway spatiotemporal data adaptive organization module communicatively connected to the railway spatiotemporal data classification storage module;

The railway spatiotemporal data classification storage module is used to classify and store the railway spatiotemporal data according to the type of the railway spatiotemporal data;

The railway spatiotemporal data adaptive organization module is used to adaptively organize the railway spatiotemporal data in the railway spatiotemporal data classification storage module by using an adaptive linear octree space partitioning method.

In some embodiments of the present application, the types of the railway spatiotemporal data used to represent railway multimodal spatiotemporal information include: relational data, file data, and stream data;

Correspondingly, the railway spatiotemporal data classification storage module is provided with a relational database, a file database and a stream data memory database which are respectively connected to the computing layer for communication;

The relational database is used to store railway spatiotemporal data of the relational data type;

The railway spatiotemporal data belonging to the relational data type includes: vector data, raster data, remote sensing data, table data and log data;

The file-type database is used to store railway spatiotemporal data belonging to the file-type data type;

The railway spatiotemporal data belonging to the file-type data include: terrain tile data, point cloud data, oblique photography data, railway three-dimensional model data, document object model data and video image data, wherein the railway three-dimensional model data includes: building information model data and digital elevation model data;

The stream data memory database is used to store railway spatiotemporal data of the type belonging to the stream data;

The railway spatiotemporal data belonging to the stream data type includes: railway POI data, spatial location data, meteorological data and road condition data.

In some embodiments of the present application, the railway spatiotemporal data adaptive organization module includes: a terrain data organization unit and a three-dimensional model organization unit respectively communicatively connected to the railway spatiotemporal data classification storage module;

The terrain data organization unit is used to establish a corresponding tile map pyramid model based on a spatial octree partitioning method for the terrain tile data;

The three-dimensional model organization unit is used to stretch the three-dimensional model data into a building white mold block model based on the spatial octree partitioning method according to the model bottom surface contour of the three-dimensional model data.

In some embodiments of the present application, the computing layer includes: a cloud computing module and an edge computing module that are communicatively connected to the data layer and the service layer, respectively;

The cloud computing module and the edge computing module are both provided with multiple computing groups, each of which includes data scheduling task nodes and analysis computing task nodes that are interconnected and communicated with each other; and data synchronization transmission and result push are performed between the cloud computing module and the edge computing module;

The data scheduling task node is used to call the railway spatiotemporal data stored in the data layer according to the computing task corresponding to the railway business scenario currently specified by the user, and transmit it to the analysis and computing task node;

The analysis and calculation task node is used to calculate the calculation task corresponding to the railway business scenario currently specified by the user based on the railway spatiotemporal data transmitted by the data scheduling task node, so as to obtain the spatiotemporal analysis and calculation results for the railway business scenario, and send the spatiotemporal analysis and calculation results to the service layer.

In some embodiments of the present application, the service layer includes: a service orchestration center, a service dynamic registration and management module, and a service publishing module, which are respectively communicatively connected to the computing layer and the presentation layer;

The service orchestration center is used to perform workflow designation, service aggregation, service chain synthesis and service orchestration for each service application based on a shared analysis service container combination corresponding to a preset microservice architecture;

The service dynamic registration and management module is used to perform dynamic service registration, service evaluation, service query and service monitoring for each service application based on the shared analysis service container combination according to the spatiotemporal analysis calculation results for the railway business scenario, so as to generate railway spatiotemporal service data for the railway business scenario;

The service publishing module is used to publish the railway spatiotemporal service data for the railway business scenario to the display layer using a service interface based on a preset GIS spatial service engine.

In some embodiments of the present application, the presentation layer includes: a visualization scene construction module and a scene semantic information enhancement module respectively communicatively connected to the service layer;

The visualization scene construction module is used to construct a virtual terrain scene, a dynamic simulation scene and a ground object scene corresponding to the railway business scene according to the railway spatiotemporal service data for the railway business scene transmitted by the service layer, so as to obtain railway three-dimensional visualization scene data for the railway business scene;

The scene semantic information enhancement module is used to adopt a three-dimensional scene enhancement visualization method coordinated by dynamic and static visual variables to perform semantic information enhancement processing on the railway three-dimensional visualization scene data for the railway business scene, and send the railway three-dimensional visualization scene data after the semantic information enhancement processing to various types of user terminals for display so as to perform data interaction with each of the user terminals;

The three-dimensional scene enhanced visualization method of the dynamic and static visual variables is a method of semantically enhancing the static visual variables and the dynamic visual variables using a preset enhanced expression method.

In some embodiments of the present application, the railway business scenarios include: geological disaster scenarios, emergency rescue scenarios and train dispatch scenarios.

In some embodiments of the present application, the railway three-dimensional visualization scene data includes: a correspondence between scene categories, scene elements, enhanced expression methods, and preset enhanced visualization effect data;

Correspondingly, if the railway business scene currently specified by the user is the geological disaster scene, the scene categories in the railway three-dimensional visualization scene data corresponding to the geological disaster scene include: disaster causes, disaster objects and disaster situation objects;

The scene elements corresponding to the disaster causes include: earthquakes and heavy rainfall; the enhanced expression method corresponding to the earthquake includes: controlling the shaking of the three-dimensional scene, setting the frequency, amplitude and duration of the shaking, and using text to indicate the time and location of the earthquake; the enhanced expression method corresponding to the heavy rainfall includes: using a particle system to simulate real rainfall conditions;

The scene elements corresponding to the disaster object include: occurrence time, occurrence location, impact range, sliding speed, sliding time, sliding volume, trajectory energy, river direction, Beidou monitoring and meteorological monitoring; wherein, the enhanced expression method corresponding to the occurrence time includes: using text annotations to indicate the time of landslide occurrence; the enhanced expression method corresponding to the occurrence location includes: using text annotations to indicate the location of landslide occurrence; the enhanced expression method corresponding to the impact range includes: using a coverage area to indicate the sliding range, and at the same time with monochrome display; the enhanced expression method corresponding to the sliding speed includes: using text annotations to indicate the sliding speed; the enhanced expression method corresponding to the sliding time includes: The invention discloses a method for enhancing the landslide trajectory energy by using a one-to-one corresponding color to indicate the energy of the landslide trajectory; ...

The scene elements corresponding to the disaster objects include: damaged roads and damaged buildings; among them, the enhanced expression method corresponding to the damaged roads includes: using monochrome lines to represent the scope of the construction access roads affected by the landslide; the enhanced expression method corresponding to the damaged buildings includes: according to the severity of the damage, using block models with three monochrome colors for expression, adding highlight and flashing effects, and using self-explanatory symbols to represent the building types.

Another aspect of the present application provides a railway multimodal spatiotemporal information adaptive visualization method, which is implemented by applying the railway multimodal spatiotemporal information adaptive visualization engine. The railway multimodal spatiotemporal information adaptive visualization method includes:

The service layer receives the railway business scenario currently specified by the user and sent by the user terminal and forwarded by the presentation layer, and sends the railway business scenario currently specified by the user to the computing layer;

The computing layer adopts a cloud-edge collaborative computing architecture for the railway business scenario, calls the railway spatiotemporal data stored in the data layer to provide computing power support for the service layer, so as to obtain spatiotemporal analysis calculation results for the railway business scenario, and transmits the spatiotemporal analysis calculation results to the service layer;

The service layer generates railway spatiotemporal service data for the railway business scenario based on a preset microservice architecture according to the spatiotemporal analysis calculation results for the railway business scenario, and publishes the railway spatiotemporal service data for the railway business scenario to the presentation layer;

The presentation layer constructs a railway three-dimensional visualization scene and performs semantic enhancement processing based on the railway spatiotemporal service data for the railway business scenario called from the service layer to obtain the railway three-dimensional visualization scene data for the railway business scenario, and displays the enhanced visualization effect data corresponding to the railway business scenario in various types of user terminals based on the railway three-dimensional visualization scene data.

The railway multimodal spatiotemporal information adaptive visualization engine provided by the present application includes: a data layer, a computing layer, a service layer and a display layer that are communicatively connected in sequence; the data layer is used to classify, store and adaptively organize the railway spatiotemporal data used to represent the railway multimodal spatiotemporal information; the computing layer is used to adopt a cloud-edge collaborative computing architecture for the railway business scenario currently specified by the user, and call the railway spatiotemporal data stored in the data layer to provide computing power support for the service layer to obtain the spatiotemporal analysis calculation results for the railway business scenario; the service layer is used to generate railway spatiotemporal service data for the railway business scenario based on the preset microservice architecture according to the spatiotemporal analysis calculation results for the railway business scenario; the display layer is used to construct a railway three-dimensional visualization scene and perform semantic enhancement processing according to the railway spatiotemporal service data for the railway business scenario called from the service layer, so as to obtain the railway three-dimensional visualization scene data for the railway business scenario, and display the enhanced visualization effect data corresponding to the railway business scenario in various types of user terminals based on the railway three-dimensional visualization scene data. It can realize adaptive visualization of railway three-dimensional scenes, improve the efficiency of railway spatiotemporal data storage and access, realize efficient sharing and publishing of services and elastic and flexible service management, improve the visualization effect of railway business scenes and enrich the semantic information of railway business scenes, assist users to fully perceive and thoroughly understand dynamic and static business information, improve the three-dimensional visualization display effect of diversified railway business scenes, and support railway intelligent business applications to carry out simulation deduction, trend forecasting, and decision-making analysis.

Additional advantages, purposes, and features of the present application will be partially described in the following description, and will become partially apparent to those skilled in the art after studying the following, or may be learned from the practice of the present application. The purposes and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the specification and the drawings.

Those skilled in the art will understand that the purposes and advantages that can be achieved by the present application are not limited to the above specific description, and the above and other purposes that can be achieved by the present application will be more clearly understood based on the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a further understanding of the present application, constitute a part of the present application, and do not constitute a limitation of the present application. The components in the drawings are not drawn to scale, but are only for the purpose of illustrating the principles of the present application. In order to facilitate the illustration and description of some parts of the present application, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to the present application. In the drawings:

FIG1 is a schematic diagram of a first structure of a railway multimodal spatiotemporal information adaptive visualization engine in an embodiment of the present application.

FIG2 is a second structural diagram of a railway multimodal spatiotemporal information adaptive visualization engine in an embodiment of the present application.

FIG3 is a third structural diagram of the railway multimodal spatiotemporal information adaptive visualization engine in one embodiment of the present application.

FIG. 4 is a schematic diagram of the three-dimensional railway visualization scene data corresponding to the geological disaster scene in an embodiment of the present application.

FIG5 is a flow chart of a method for adaptively visualizing railway multimodal spatiotemporal information in an embodiment of the present application.

FIG6 is a schematic diagram of the architecture of a railway multimodal spatiotemporal information adaptive visualization engine in an application example of the present application.

FIG. 7 is a schematic diagram of a diversified organization of realistic terrain data based on image data in an application example of the present application.

FIG8 is a schematic diagram of the basic process of constructing a railway three-dimensional visualization scene in an application example of the present application.

FIG9 is a schematic diagram of an enhanced visualization expression of a railway three-dimensional scene in an application example of the present application.

Wherein, the reference numerals are:

1. Data layer;

11. Railway spatiotemporal data classification storage module;

111. Relational database;

112. File-based database;

113. Streaming data memory database;

12. Railway spatiotemporal data adaptive organization module;

121. Terrain data organization unit;

122. Three-dimensional model organization unit;

2. Computation layer;

21. Cloud computing module;

22. Edge computing module;

3. Service layer;

31. Service orchestration center;

32. Service dynamic registration and management module;

33. Service publishing module;

4. Display layer;

41. Visual scene construction module;

42. Scene semantic information enhancement module.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the implementation modes and the accompanying drawings. Here, the illustrative implementation modes and descriptions of the present application are used to explain the present application, but are not intended to limit the present application.

It should also be noted here that in order to avoid obscuring the present application due to unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present application are shown in the accompanying drawings, while other details that are not very relevant to the present application are omitted.

It should be emphasized that the term “include/comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

3D visualization technology is a comprehensive cutting-edge technology that integrates computer data processing and image display, which was born in the mid-1980s and is widely used in all fields of geology and geophysics. At present, the commonly used engines for realizing 3D scene visualization are mainly based on WebGL technology, including CesiumJS, OpenLayers 3D, Leaflet, Three.js, etc., which support many functions such as dynamic roaming, data visualization, oblique photography, model rendering, etc. WebGL is a technology used to draw and render complex 3D graphics on web pages and allow users to interact with them. At present, the existing visualization engines are not universal and have insufficient functional support for realizing railway 3D scene visualization. The existing visualization engines in the railway field mainly use browsers to realize single PC-side scene visualization, which cannot efficiently and adaptively organize multimodal spatiotemporal element information and realize adaptive visualization based on diversified visualization terminals. In addition, the lack of scene semantic expression information seriously restricts users' cognitive ability of railway 3D scenes. Therefore, it is necessary to carry out research on adaptive visualization engines and methods for railway multimodal spatiotemporal information, to achieve efficient enhanced visualization expression of railway three-dimensional scenes, to enrich scene elements and semantic information, to assist users in fully perceiving and thoroughly understanding dynamic and static business information, to improve the three-dimensional visualization display effect of railway diversified business scenarios, and to support railway intelligent business applications in simulation deduction, trend forecasting, and decision-making analysis.

Based on this, in view of the problem that the existing visualization engines are not universal and have insufficient functional support for realizing the visualization of railway three-dimensional scenes, and facing the need for diversified enhanced visualization expression of railway three-dimensional scenes, this application studies a railway multimodal spatiotemporal information adaptive visualization engine and method. The architecture design of the railway multimodal spatiotemporal information adaptive visualization engine is proposed, and the engine architecture composition levels and relationships are clarified to provide top-level design guidance for the construction of an adaptive visualization engine; a spatiotemporal information data adaptive storage organization method is proposed to achieve efficient on-demand classified storage of spatiotemporal data, as well as data organization that adapts to multiple types of visualization terminals; a railway spatiotemporal service sharing and publishing technology is proposed to build a service framework based on a microservice architecture to achieve orderly arrangement, registration, management and publishing of spatiotemporal microservices; a railway complex three-dimensional scene enhanced visualization method is proposed to achieve scene semantic enhanced expression through the combination of dynamic and static visual variables, and take railway geological disasters as the application scenario to design the scene element composition and enhanced expression method to improve the three-dimensional visualization display effect of railway geological disaster scenes. The following embodiments are used to explain in detail.

The embodiment of the present application provides a railway multimodal spatiotemporal information adaptive visualization engine. Referring to FIG1 , the railway multimodal spatiotemporal information adaptive visualization engine specifically includes the following contents:

The data layer 1, computing layer 2, service layer 3 and presentation layer 4 are connected in communication in sequence.

The data layer 1 is used for classifying, storing and adaptively organizing railway spatiotemporal data used to represent railway multimodal spatiotemporal information.

The computing layer 2 is used to adopt a cloud-edge collaborative computing architecture for the railway business scenario currently specified by the user, and call the railway spatiotemporal data stored in the data layer 1 to provide computing power support for the service layer 3 to obtain the spatiotemporal analysis calculation results for the railway business scenario.

The service layer 3 is used to generate railway spatiotemporal service data for the railway business scenario based on a preset microservice architecture and according to the spatiotemporal analysis calculation results for the railway business scenario.

The display layer 4 is used to construct a railway three-dimensional visualization scene and perform semantic enhancement processing based on the railway spatiotemporal service data for the railway business scenario called from the service layer 3, so as to obtain the railway three-dimensional visualization scene data for the railway business scenario, and based on the railway three-dimensional visualization scene data, display the enhanced visualization effect data corresponding to the railway business scenario in various types of user terminals.

In one or more embodiments of the present application, the user terminal may include a variety of visualization devices such as a display desktop, a workstation terminal device, a mobile terminal, an AR device, and a MR device.

From the above description, it can be seen that the railway multimodal spatiotemporal information adaptive visualization engine provided in the embodiment of the present application can realize adaptive visualization of railway three-dimensional scenes, improve the efficiency of railway spatiotemporal data access, realize efficient sharing and publishing of services and elastic and flexible service management, improve the visualization effect of railway business scenes and enrich the semantic information of railway business scenes, assist users in comprehensively perceiving and thoroughly understanding dynamic and static business information, improve the three-dimensional visualization display effect of railway diversified business scenes, and support railway intelligent business applications to carry out simulation deduction, trend forecasting, and decision-making analysis.

In order to further improve the application reliability, comprehensiveness and effectiveness of the data layer 1, in an embodiment of the railway multimodal spatiotemporal information adaptive visualization engine provided in the present application, referring to FIG. 2 , the data layer 1 in the railway multimodal spatiotemporal information adaptive visualization engine specifically includes the following contents:

A railway spatiotemporal data classification storage module 11 is communicatively connected to the computing layer 2 , and a railway spatiotemporal data adaptive organization module 12 is communicatively connected to the railway spatiotemporal data classification storage module 11 .

The railway spatiotemporal data classification storage module 11 is used to classify and store the railway spatiotemporal data according to the type of the railway spatiotemporal data.

The railway spatiotemporal data adaptive organization module 12 is used to adaptively organize the railway spatiotemporal data in the railway spatiotemporal data classification storage module 11 by using an adaptive linear octree space partitioning method.

In order to further improve the application reliability and effectiveness of the railway spatiotemporal data classification storage module 11, in an embodiment of the railway multimodal spatiotemporal information adaptive visualization engine provided in the present application, the types of the railway spatiotemporal data used to represent the railway multimodal spatiotemporal information include: relational data, file data and stream data; correspondingly, referring to FIG. 3, the railway spatiotemporal data classification storage module 11 in the railway multimodal spatiotemporal information adaptive visualization engine specifically includes the following contents:

The railway spatiotemporal data classification storage module 11 is provided with a relational database 111, a file-type database 112 and a stream data memory database 113 which are respectively connected to the computing layer 2 for communication;

The relational database 111 is used to store railway spatiotemporal data of the relational data type;

The railway spatiotemporal data belonging to the relational data type includes: vector data, raster data, remote sensing data, table data and log data.

Specifically, a relational database 111 (such as PostgreSQL) is used to store structured spatiotemporal data such as vector data, raster data, remote sensing data, table data, and log data.

The data structure of vector data follows the standard specifications of OGC and is stored in the standard WKB format.

The file-type database 112 is used to store railway spatiotemporal data belonging to the file-type data type;

Among them, the railway spatiotemporal data belonging to the file-type data includes: terrain tile data, point cloud data, oblique photography data, railway three-dimensional model data, document object model data and video image data, among which the railway three-dimensional model data includes: building information model data and digital elevation model data.

Specifically, the file-based database 112 MongoDB is designed to store semi-structured or unstructured data such as terrain tile data, point cloud data, oblique photography data, three-dimensional model data (such as BIM model data and DEM model data), DOM model data and video image data. MongoDB can be combined with GirdFS to store file-based data, and scattered file-based data can be uniformly managed in MongoDB to facilitate the retrieval and analysis of spatiotemporal data.

The stream data memory database 113 is used to store railway spatiotemporal data of the type belonging to the stream data;

The railway spatiotemporal data belonging to the stream data type includes: railway POI data, spatial location data, meteorological data and road condition data.

Specifically, the design of the in-memory database Redis is used to store the stream data with high real-time requirements, such as railway POI data, spatial location data, meteorological data, and road condition data. In order to ensure the uniqueness of the key value of the stream data in REDIS, the combination code is used as the KEY value of the spatiotemporal stream data.

In order to further improve the application reliability and effectiveness of the railway spatiotemporal data adaptive organization module 12, in an embodiment of the railway multimodal spatiotemporal information adaptive visualization engine provided in the present application, referring to FIG3, the railway spatiotemporal data adaptive organization module 12 in the railway multimodal spatiotemporal information adaptive visualization engine specifically includes the following contents:

A terrain data organization unit 121 and a three-dimensional model organization unit 122 respectively connected to the railway spatiotemporal data classification storage module 11 for communication;

The terrain data organizing unit 121 is used to establish a corresponding tile map pyramid model based on the spatial octree partitioning method for the terrain tile data.

Specifically, taking into account the differences in terminal performance, screen size and other parameters of PC, mobile, VR, large-screen terminals, and changes in network environment, a variety of smaller terrain tile data are pre-built, such as 32×32 DEM tiles and 128×128 image tiles, and corresponding multi-resolution and multi-level tile pyramids are established, so that when visualizing on terminals with weaker rendering performance, smaller tiles can be selected within the same LOD level, thereby reducing the amount of data drawing and improving rendering efficiency.

The three-dimensional model organization unit 122 is used to stretch the three-dimensional model data into a building white model block model based on the spatial octree partitioning method according to the model bottom surface contour of the three-dimensional model data.

Specifically, the model is stretched into a block white model based on the bottom contour of the model to form LODn level data. If the performance of the visualization terminal and the network transmission efficiency gradually decrease, the value of n will gradually increase, and the number of LOD levels formed will increase. Model data with fewer levels of detail can be selected to meet the needs of diversified terminals.

In one or more embodiments of the present application, the spatial octree partitioning method may also be referred to as an adaptive linear octree spatial partitioning method, and its implementation process may be divided into the following three steps:

(1) The spatial topological relationship between each model and the edge of the linear octree partition space is determined through the bounding box of the entity model. When the bounding box of the entity model intersects with the edge of the partition space, the partition space range where the model is located is appropriately enlarged until all entity models at the edge are included.

(2) By setting a threshold, the number of entity models in each linear octree partition space is controlled. If the number of models exceeds the set threshold, the area size of the current octree partition space is appropriately modified to ensure that the number of entity models in each partition space is basically balanced.

(3) Find the bounding box and quantity of the entity models in the eight subspaces of the current octree partition space, and repeat the above steps until the spatial relationship between all entity models in the entire partition space and the edges of the spatial region and its subspace region, the number of entity models and other indicators meet the requirements, thus completing the entire adaptive linear octree space partition process.

In order to further improve the application reliability and effectiveness of the calculation layer 2, in an embodiment of the railway multimodal spatiotemporal information adaptive visualization engine provided in the present application, referring to FIG. 2 or FIG. 3, the calculation layer 2 in the railway multimodal spatiotemporal information adaptive visualization engine specifically includes the following contents:

The cloud computing module 21 and the edge computing module 22 are respectively connected to the data layer 1 and the service layer 3 for communication, and the cloud computing module 21 and the edge computing module 22 constitute the cloud-edge collaborative computing architecture of the computing layer 2.

The cloud computing module 21 and the edge computing module 22 are each provided with multiple computing groups, each computing group includes data scheduling task nodes and analysis computing task nodes that are interconnected for communication; and data synchronization transmission and result push are performed between the cloud computing module 21 and the edge computing module 22.

It is understandable that the data scheduling task node and the analysis and computing task node can both adopt client devices or servers, and can be specifically configured according to actual application scenarios.

The data scheduling task node is used to call the railway spatiotemporal data stored in the data layer 1 according to the computing task corresponding to the railway business scenario currently specified by the user, and transmit it to the analysis and computing task node.

The analysis and calculation task node is used to calculate the calculation task corresponding to the railway business scenario currently specified by the user based on the railway spatiotemporal data transmitted by the data scheduling task node, so as to obtain the spatiotemporal analysis and calculation results for the railway business scenario, and send the spatiotemporal analysis and calculation results to the service layer 3.

It can be understood that the data scheduling task node first goes to the data layer 1 to retrieve the railway spatiotemporal data required for the computing task content corresponding to the railway business scenario pre-specified by the user, and then transmits the retrieved data to the analysis and computing task node, so that the analysis and computing task node performs the calculation specified by the computing task content on the railway spatiotemporal data according to the computing task content corresponding to the railway business scenario pre-specified by the user. The result data obtained by the calculation is the spatiotemporal analysis calculation result, which may refer to the calculation result obtained by a single analysis and computing task node, or may refer to the integrated result data composed of the calculation results obtained by all the analysis and computing task nodes on the cloud and the edge.

In order to further improve the application reliability and effectiveness of the service layer 3, in an embodiment of the railway multimodal spatiotemporal information adaptive visualization engine provided in the present application, referring to FIG. 2 or FIG. 3, the service layer 3 in the railway multimodal spatiotemporal information adaptive visualization engine specifically includes the following contents:

A service orchestration center 31, a service dynamic registration and management module 32, and a service publishing module 33, which are respectively connected to the computing layer 2 and the presentation layer 4 in communication;

The service orchestration center 31 is used to perform workflow designation, service aggregation, service chain synthesis and service orchestration for each service application based on a shared analysis service container combination corresponding to a preset microservice architecture;

The service dynamic registration and management module 32 is used to perform dynamic service registration, service evaluation, service query and service monitoring for each service application based on the shared analysis service container combination and the spatiotemporal analysis calculation results for the railway business scenario, so as to generate railway spatiotemporal service data for the railway business scenario.

Specifically, the service orchestration center 31 and the service dynamic registration and management module 32 are based on the microservice architecture, and a multimodal spatiotemporal scene sharing analysis service container combination is designed to achieve decentralized deployment, loose coupling between services, and decentralized governance in a highly scalable cloud environment, which significantly improves the agility and maintainability of the iteration of the railway spatiotemporal information sharing analysis service system. By adopting the "break the whole into parts" microservice architecture, the traditional integrated service application architecture is decomposed into multi-granularity spatiotemporal scene service clusters that can be deployed, maintained and orchestrated independently. According to the differentiated needs of multi-level railway spatiotemporal information applications, through the service orchestration center 31, the call dependency between services is dynamically managed, tracked and orchestrated, and the aggregation of multimodal spatiotemporal information data services, the construction of service chains, and the efficient management of multi-granularity services with complex associations are realized, ensuring the high performance, high availability and high scalability of the multimodal spatiotemporal data service environment.

The service publishing module 33 is used to publish the railway spatiotemporal service data for the railway business scenario to the display layer 4 using a service interface based on a preset GIS spatial service engine.

Specifically, the service publishing module 33 can adopt service GIS technology, and realize the publishing of diversified spatiotemporal information sharing services that are more suitable for railway business scenarios through the GIS spatial service engine, and provide standard service interfaces to the outside world. The types of service interfaces mainly include the latest OGC service interfaces, SOAP service interfaces and REST service interfaces, etc., to support the visualization engine display layer 4 to call spatiotemporal data services, functional services and other services.

In order to further improve the application reliability and effectiveness of the display layer 4, in an embodiment of the railway multimodal spatiotemporal information adaptive visualization engine provided in the present application, referring to FIG. 2 or FIG. 3, the display layer 4 in the railway multimodal spatiotemporal information adaptive visualization engine specifically includes the following contents:

A visualization scene construction module 41 and a scene semantic information enhancement module 42 which are respectively connected to the service layer 3 in communication.

The visualization scene construction module 41 is used to construct virtual terrain scenes, dynamic simulation scenes and terrain scenes corresponding to the railway business scenes according to the railway spatiotemporal service data for the railway business scenes transmitted by the service layer 3, so as to obtain railway three-dimensional visualization scene data for the railway business scenes.

Specifically, the process of building a virtual terrain scene can be: adding DEM and image data to an online global virtual terrain scene or using these data to build a virtual terrain scene. First, the DEM data and remote sensing image data of the study area are sliced and processed in layers and blocks to establish terrain models and image models at different levels of detail; then, the DEM data and remote sensing image data are superimposed, and the terrain and images at multiple levels of detail are scheduled in real time according to the user's viewpoint changes, and loaded and drawn in real time, so as to display the three-dimensional terrain scene under the current viewpoint.

The dynamic simulation scene construction process can be: a process of dynamically simulating continuously changing things. The simulation process needs to be supported by the calculation layer 2 in the visualization engine, and the drawing and updating of the scene objects are realized based on the changes in the real-time analysis calculation results.

The process of building a terrain scene can be as follows: placing the analysis and calculation information at a specified three-dimensional coordinate position and overlaying it with the terrain for display and interactive query. According to the characteristics of the analysis and calculation information, color, text, point symbols, line symbols, and surface symbols are used for plotting, thereby assisting in the rich and comprehensive expression of various types of analysis information and improving user cognitive efficiency.

The scene semantic information enhancement module 42 is used to adopt a three-dimensional scene enhanced visualization method that coordinates dynamic and static visual variables to perform semantic information enhancement processing on the railway three-dimensional visualization scene data for the railway business scenario, and send the railway three-dimensional visualization scene data after semantic information enhancement processing to various types of user terminals for display so as to interact with each of the user terminals.

The three-dimensional scene enhanced visualization method of the dynamic and static visual variables is a method of semantically enhancing the static visual variables and the dynamic visual variables using a preset enhanced expression method.

Specifically, the scene semantic information enhancement module 42 proposes a diversified visual variable semantic enhancement method according to the complex characteristics of the railway business scene, and realizes the enhanced expression of the railway business scene semantic level through the coordinated combination of diversified visual variables. The three-dimensional scene enhancement visualization method of the dynamic and static visual variables is shown in the following formula:

In the formula, M{} represents diversified visual variables, S(s ₁ , s ₂ , ... s _w1 ) represents static visual variables. In an example, if w1=6, then: s ₁ represents color; s ₂ represents brightness; s ₃ represents size; s ₄ represents shape; s ₅ represents direction; and s ₆ represents texture.

D (d ₁ , d ₂ , ... d _w2 ) represents a dynamic visual variable. In an example, if w2=5, then: d ₁ represents order; d ₂ represents synchronization; d ₃ represents duration; d ₄ represents frequency; and d ₅ represents amplitude.

f( _x1 , _x2 , ... _xw3 ) represents an enhanced expression method. In an example, if w3=5, then: _x1 represents order; _x2 represents movement, _x3 represents flashing, _x4 represents sound effect, _x5 represents symbol or highlight, etc.; E{} represents feature information of scene objects, P(x, y, z) represents spatial orientation, T(y, m, d) represents time information, A( _a1 , _a2 , ...) represents attribute information, for example, _a1 represents the scope of geological disasters, _a2 represents disaster losses, etc., R( _r1 , _r2 , ...) represents association relationships, for example, _r1 represents causal association relationships, _r2 represents temporal association relationships, etc.

In one or more embodiments of the present application, the railway business scenarios include: geological disaster scenarios, emergency rescue scenarios and train scheduling scenarios.

The railway three-dimensional visualization scene data includes: a correspondence between scene categories, scene elements, enhanced expression methods and preset enhanced visualization effect data;

Correspondingly, referring to FIG4 , if the railway business scene currently specified by the user is the geological disaster scene, the scene categories in the railway three-dimensional visualization scene data corresponding to the geological disaster scene include: disaster causes, disaster objects, and disaster situation objects;

The scene elements corresponding to the disaster causes include: earthquakes and heavy rainfall; the enhanced expression method corresponding to the earthquake includes: controlling the shaking of the three-dimensional scene, setting the frequency, amplitude and duration of the shaking, and using text to indicate the time and location of the earthquake; the enhanced expression method corresponding to the heavy rainfall includes: using a particle system to simulate real rainfall conditions;

The scene elements corresponding to the disaster object include: occurrence time, occurrence location, impact range, sliding speed, sliding time, sliding volume, trajectory energy, river direction, Beidou monitoring and meteorological monitoring; wherein, the enhanced expression method corresponding to the occurrence time includes: using text annotations to indicate the time of landslide occurrence; the enhanced expression method corresponding to the occurrence location includes: using text annotations to indicate the location of landslide occurrence; the enhanced expression method corresponding to the impact range includes: using a coverage area to indicate the sliding range, and at the same time with monochrome display; the enhanced expression method corresponding to the sliding speed includes: using text annotations to indicate the sliding speed; the enhanced expression method corresponding to the sliding time includes: The invention discloses a method for enhancing the landslide trajectory energy by using a one-to-one corresponding color to indicate the energy of the landslide trajectory; ...

The scene elements corresponding to the disaster objects include: damaged roads and damaged buildings; among them, the enhanced expression method corresponding to the damaged roads includes: using monochrome lines to represent the scope of the construction access roads affected by the landslide; the enhanced expression method corresponding to the damaged buildings includes: according to the severity of the damage, using block models with three monochrome colors for expression, adding highlight and flashing effects, and using self-explanatory symbols to represent the building types.

Based on the railway multimodal spatiotemporal information adaptive visualization engine provided in the above embodiment, the present application also provides an embodiment of a railway multimodal spatiotemporal information adaptive visualization method, which is implemented by applying the above railway multimodal spatiotemporal information adaptive visualization engine. Referring to FIG5 , the railway multimodal spatiotemporal information adaptive visualization method specifically includes the following contents:

Step 100: The service layer receives the railway business scenario currently specified by the user and sent by the user terminal and forwarded by the presentation layer, and sends the railway business scenario currently specified by the user to the computing layer.

Step 200: The computing layer adopts a cloud-edge collaborative computing architecture for the railway business scenario, calls the railway spatiotemporal data stored in the data layer to provide computing power support for the service layer, so as to obtain the spatiotemporal analysis and calculation results for the railway business scenario, and transmit the spatiotemporal analysis and calculation results to the service layer.

Step 300: The service layer generates railway spatiotemporal service data for the railway business scenario based on a preset microservice architecture and in accordance with the spatiotemporal analysis calculation results for the railway business scenario, and publishes the railway spatiotemporal service data for the railway business scenario to the presentation layer.

Step 400: The presentation layer constructs a railway three-dimensional visualization scene and performs semantic enhancement processing according to the railway spatiotemporal service data for the railway business scenario called from the service layer to obtain the railway three-dimensional visualization scene data for the railway business scenario, and displays the enhanced visualization effect data corresponding to the railway business scenario in various types of user terminals based on the railway three-dimensional visualization scene data.

From the above description, it can be seen that the railway multimodal spatiotemporal information adaptive visualization method provided in the embodiment of the present application can realize the adaptive visualization of railway three-dimensional scenes, improve the efficiency of railway spatiotemporal data access, realize efficient sharing and publishing of services and elastic and flexible service management, improve the visualization effect of railway business scenes and enrich the semantic information of railway business scenes, assist users in comprehensively perceiving and thoroughly understanding dynamic and static business information, improve the three-dimensional visualization display effect of railway diversified business scenes, and support railway intelligent business applications to carry out simulation deduction, trend forecasting, and decision-making analysis.

To further illustrate the above embodiments, this application also provides a specific application example of a railway multimodal spatiotemporal information adaptive visualization engine, see Figure 6, in view of the characteristics of railway multimodal spatiotemporal information and diverse visualization environments such as desktops/workstations, mobile terminals, AR/MR devices, and facing the needs of various business scenarios such as safety management, equipment facilities, operation management, station and train services, and business development, this application example designs a railway multimodal spatiotemporal information adaptive visualization engine. The engine architecture is built with a multi-layer system, which is divided from bottom to top into data layer, computing layer, service layer, and display layer.

The description of each layer is as follows:

(1) Data layer: Based on the diversified spatiotemporal database and the characteristics of the railway multimodal spatiotemporal data structure and access requirements, an on-demand classified storage structure suitable for relational data such as vector data, raster data, three-dimensional model data, tile data, location data, file data, and stream data is designed to realize on-demand classified storage of spatial data; the spatiotemporal data organization method proposed in this application is adopted to realize data organization that is suitable for different terminals and ensure efficient reading and analysis of data.

(2) Computing layer: In response to the differentiated needs of railway-specific business scenarios (such as geological disaster scenarios, emergency rescue scenarios, train scheduling scenarios, etc.), a cloud-edge collaborative computing architecture is adopted to realize scenario data resource scheduling, spatial analysis and calculation, spatiotemporal big data analysis, etc., to provide computing power support for service release and obtain spatiotemporal analysis and calculation results.

(3) Service layer: Establish a service orchestration center to dynamically manage, track, and orchestrate the calling dependencies between services, realize the efficient management of multimodal spatiotemporal data service publishing and multi-granularity services with complex associations, and ensure the high performance, high availability, and high scalability of the multimodal spatiotemporal information service environment.

(4) Display layer: A scene-enhanced visualization method that coordinates dynamic and static visual variables is used to achieve semantically enhanced display of railway business scenarios and interaction with users, improve users' perception and cognitive ability of scenes, and support visualization based on a variety of terminals such as desktops/workstations, mobile terminals, and AR/MR devices.

In view of the above architecture, the application example of this application also provides an explanation of the adaptive storage and organization method of multimodal railway spatiotemporal data:

(1) Classification and storage of railway spatiotemporal data

1) Relational Data Storage

The relational database PostgreSQL is used to store structured spatiotemporal data such as vector data, raster data, remote sensing data, table data and log data.

The data structure of vector data follows the standard specifications of OGC and is stored in standard WKB format. Using PostGIS to extend PostgreSQL, most geometric types (Cartesian coordinate system) can be supported. Point in 2D coordinate space is defined by (X, Y), Point in 3D coordinate space is defined by (X, Y, Z), Point in 2DM space (generally distinguished by PointM geometry type) is defined by (X, Y, M), and Point in 3DM space (generally distinguished by PointMZ geometry type) is defined by (X, Y, Z, M). Storing larger raster data objects in PostgreSQL is achieved through a new type. This new data class consists of SRID, type, and a sequence of bytes.

2) File-based data storage

The file-based database MongoDB is designed to store semi-structured or unstructured data such as terrain tile data, point cloud data, oblique photography data, 3D model data (such as BIM model data and DEM model data), DOM model data and video image data. MongoDB can be combined with GirdFS to store file-based data, and scattered file-based data can be uniformly managed in MongoDB to facilitate the retrieval and analysis of spatiotemporal data.

3) Streaming Data Storage

For the stream data with high real-time requirements such as railway POI data, spatial location data, meteorological data and road condition data, the memory database Redis is used for storage. In order to ensure the uniqueness of the key value of the stream data in REDIS, the combination code is used as the KEY value of the spatiotemporal stream data.

(2) Adaptive organization of railway spatiotemporal data

Traditional spatiotemporal data organization methods usually use spatial indexing and partitioning methods such as quadtree, R-tree, and grid alone or in combination to process data into tile data with multi-level continuous LOD features, forming a multi-resolution multi-level tile pyramid to achieve data hierarchical and block organization. In order to improve the visualization effect and efficiency of 3D scenes for diversified visualization terminals, an adaptive linear octree spatial partitioning method is proposed for terrain and 3D model data in 3D scenes.

1) Terrain data organization: Considering the differences in performance, screen size and other parameters of various terminals such as PC, mobile, VR, and large-screen terminals, as well as changes in network environment, a variety of smaller-sized terrain tile data are pre-built, such as 32×32 DEM tiles and 128×128 image tiles, and corresponding multi-resolution and multi-level tile pyramids are established, so that when visualizing on terminals with weaker rendering performance, smaller tiles can be selected within the same LOD level, reducing the amount of data drawing and improving rendering efficiency. Taking terrain image data as an example, the diversified organization of realistic terrain data based on image data is shown in Figure 7.

2) 3D model organization: Based on the bottom contour of the model, the model is stretched into a block white model to form LODn level data. If the performance of the visualization terminal and the network transmission efficiency gradually decrease, the value of n will gradually increase, and the number of LOD levels formed will increase. Model data with fewer levels of detail can be selected to meet the needs of diversified terminals. The adaptive linear octree space partitioning process is divided into the following three steps:

First, the spatial topological relationship between each model and the edge of the linear octree partition space is determined through the bounding box of the entity model. When the bounding box of the entity model intersects with the edge of the partition space, the partition space range where the model is located is appropriately enlarged until all entity models at the edge are included.

Second, by setting a threshold, the number of entity models in each linear octree partition space is controlled. If the number of models exceeds the set threshold, the area size of the current octree partition space is appropriately modified to ensure that the number of entity models in each partition space is basically balanced.

Third, find the bounding box and quantity of the entity models in the eight subspaces under the current octree partitioning space, and repeat the above steps until the spatial relationship between all entity models in the entire partitioning space and the edges of the spatial region and its subspace region, the number of entity models and other indicators meet the requirements, completing the entire adaptive linear octree space partitioning process.

In view of the above architecture, this application example also provides an explanation of the railway spatiotemporal service sharing technology based on the microservice framework:

(1) Service Orchestration

Based on the microservice architecture, a multimodal spatiotemporal scenario sharing analysis service container combination is designed to achieve decentralized deployment, loose coupling between services, and decentralized governance in a highly scalable cloud environment, significantly improving the agility and maintainability of the railway spatiotemporal information sharing and analysis service system iteration. By adopting the "break the whole into parts" microservice architecture, the traditional integrated service application architecture is decomposed into multi-granularity spatiotemporal scenario service clusters that can be deployed, maintained, and orchestrated independently. According to the differentiated needs of multi-level railway spatiotemporal information applications, through the service orchestration center, the call dependencies between services are dynamically managed, tracked, and orchestrated, to achieve multimodal spatiotemporal information data service aggregation, service chain construction, and efficient management of multi-granular services with complex associations, ensuring high performance, high availability, and high scalability of the multimodal spatiotemporal data service environment.

(2) Service Release

Service GIS technology is adopted to realize the release of diversified spatiotemporal information sharing services that are more suitable for railway business scenarios through the GIS spatial service engine, and standard service interfaces are provided to the outside world. The types of service interfaces mainly include the latest OGC service interface, SOAP service interface and REST service interface, etc., which support the visualization engine display layer to call spatiotemporal data services, functional services and other services.

In view of the above architecture, the application example of this application also provides an explanation of the enhanced visualization method of complex three-dimensional railway scenes with the coordination of dynamic and static visual variables:

(1) Visualization scene construction process

The basic process of building a railway 3D visualization scene is shown in Figure 8, which mainly includes the construction of virtual terrain scenes, dynamic simulation scenes, and ground object scenes. The premise of scene construction is the unification of the spatial coordinate system of all visualization data. The construction process of each scene will be explained below:

1) Construction of virtual terrain scene: Add DEM and image data to the online global virtual terrain scene or use these data to build a virtual terrain scene. First, the DEM data and remote sensing image data of the study area are sliced and processed in layers and blocks to establish terrain models and image models with different levels of detail; then, the DEM data and remote sensing image data are superimposed, and the terrain and images with multiple levels of detail are scheduled in real time according to the user's viewpoint changes, and loaded and drawn in real time to display the three-dimensional terrain scene under the current viewpoint.

2) Dynamic simulation scene construction: The process of dynamically simulating continuously changing objects. The simulation process needs to be supported by the calculation layer in the visualization engine, and the drawing and updating of scene objects are realized based on the changes in real-time analysis of calculation results.

3) Landform scene construction: Place the analysis and calculation information at the specified three-dimensional coordinate position and overlay it with the terrain for display and interactive query. According to the characteristics of the analysis and calculation information, use color, text, point symbols, line symbols and surface symbols for plotting, so as to assist in the rich and comprehensive expression of various types of analysis information and improve user cognitive efficiency.

(2) Scene semantic information enhancement method

According to the complex characteristics of railway business scenarios, a semantic enhancement method of diversified visual variables is proposed. Through the collaborative combination of diversified visual variables, the enhanced expression of railway business scenarios at the semantic level is achieved.

In the formula, M represents diversified visual variables, S (s ₁ , s ₂ , …) represents static visual variables, D (d ₁ , d ₂ , …) represents dynamic visual variables, f (x ₁ , x ₂ , …) represents enhanced expression methods, such as flickering, highlighting, movement, sound effects, etc.; E represents the feature information of scene objects, P (x, y, z) represents spatial orientation, T (y, m, d) represents time information, A (a ₁ , a ₂ , …) represents attribute information, such as the scope of geological disasters, disaster losses, etc., and R (r ₁ , r ₂ , …) represents correlation relationships, mainly including causal correlation relationships, temporal correlation relationships, etc.

Through the combination of static visual variables and dynamic visual variables, in addition to displaying conventional static attribute information, it can also dynamically reveal the temporal and spatial change patterns of scene objects and enhance user cognition.

In a specific example, the enhanced visualization expression of the railway three-dimensional scene implemented based on the three-dimensional scene enhanced visualization method coordinated with the dynamic and static visual variables is shown in FIG9 .

If a combination of sequential and synchronous visual variables is used, the causal relationship of scene events can be expressed. For example, the loosening of geological structures due to railway construction, coupled with short-term heavy rainfall and other factors, can induce the occurrence of geological disasters.

If a combination of visual variables such as duration, shape, and direction is used, users can visually perceive the starting position, end position, and evolution direction of scene events;

If a combination of time, sound, color and frequency is used, the time, place, location and event description of the scene event can be emphasized, allowing users to quickly perceive the dynamic changes in the scene.

In general, static visual variables can form a variety of schematic symbols of scene objects, while dynamic visual variables can enhance the description of these self-explanatory features, thereby helping users quickly capture scene semantic information.

In addition, in a specific example, taking the three-dimensional railway geological disaster scene as an example, by integrating terrain with multiple levels of detail, three-dimensional models of railway infrastructure, disaster dynamic simulation processes, building models, etc., and giving enhanced visualization expression effects to each disaster scene element, an enhanced visualization reality expression scene of railway landslide geological disasters is constructed, and the expression method of scene elements is shown in Figure 4.

The entire disaster dynamic process can be controlled through a progress bar, including control of start, pause, playback, etc., thereby achieving dynamic interaction with users. In terms of the causes of disasters, since the occurrence of earthquakes and heavy rainfall induced landslide geological disasters, the occurrence of earthquakes was expressed in the form of shaking of three-dimensional scenes. At the same time, the frequency, amplitude and duration of the shaking were set, and the time and place of the earthquake were indicated with text. The particle system was used to simulate the actual rainfall. In terms of disaster objects, the energy of the landslide motion trajectory was mapped one by one with the visualized color of the landslide to reflect the magnitude of the landslide impact force. A relatively obvious yellow coverage was used to represent the overall coverage of the landslide, and arrows were used to represent the direction of the river, while reflecting the flow effect of the river. In terms of disaster objects, orange lines were used to represent road damage, and white lines were used to represent intact roads. The damaged buildings were expressed using simple block models with red, orange and green colors to reflect different degrees of damage, and highlight and flashing effects were added to highlight the expression. At the same time, self-explanatory symbols were used to indicate the type of building, and as much semantic information about the disaster as possible was conveyed on the basis of retaining the intuitiveness of the symbols. Part of the landslide body eventually impacted the river and buried the river channel. A yellow coverage was used in advance to represent the scope of the buried river channel, the flooded area used the color of the landslide, and the unflooded area was represented by blue, providing auxiliary decision support for emergency rescue personnel.

Therefore, the application example of this application proposes an adaptive visualization engine architecture for railway multimodal spatiotemporal information, and the architecture is built with a multi-layer system, including a data layer, a computing layer, a service layer, and a display layer. The application example of this application proposes an on-demand classification storage method for railway spatiotemporal data, divides railway multimodal spatiotemporal data into relational data, file data, and stream data, designs appropriate database storage methods for different types of data, and improves data access efficiency. The application example of this application proposes an adaptive organization method for railway terrain data, which adapts to multiple types of terminals by establishing a corresponding multi-resolution and multi-level tile pyramid method. The application example of this application proposes an adaptive organization method for railway three-dimensional model data, which adapts to multiple types of terminals by establishing a linear octree spatial partitioning method for three-dimensional model data. The application example of this application proposes a railway spatiotemporal service sharing technology based on a microservice framework, and uses Docker and Kubernetes containerization technology to design a multimodal spatiotemporal scene sharing analysis service container framework; using service GIS (Service GIS) technology, the GIS spatial service engine realizes the release of diversified spatiotemporal information sharing services that are more suitable for railway business scenarios. The application example of this application proposes a process for constructing a three-dimensional visualization scene for railways, which includes three steps: virtual terrain scene construction, dynamic simulation scene construction, and ground feature scene construction. The application example of this application proposes an enhanced visualization method for complex three-dimensional railway scenes, which realizes enhanced expression at the semantic level of railway business scenes through the coordinated combination of dynamic and static visual variables. The application example of this application proposes a three-dimensional geological disaster scene visualization application design for railways, which integrates terrain with multiple levels of detail, three-dimensional models of railway infrastructure, dynamic simulation processes of disasters, building models, etc., and gives enhanced visualization expression effects to each disaster scene element, to construct an enhanced visualization reality expression scene for railway landslide geological disasters.

In summary, the railway multimodal spatiotemporal information adaptive visualization engine proposed in the application example of this application has the following beneficial effects:

1. The architecture of the railway multimodal spatiotemporal information adaptive visualization engine is introduced in detail, providing design guidance for the construction of the adaptive visualization engine and an effective technical solution for realizing the adaptive visualization of railway three-dimensional scenes.

2. Based on the characteristics of the railway multimodal spatiotemporal data structure and access requirements, classify the spatiotemporal data and design different types of data storage modes to achieve on-demand classified storage of data and improve data access efficiency.

3. The adaptive organization method of railway spatiotemporal information elements can realize efficient spatiotemporal information organization and provide effective data support for scene presentation based on various visualization terminals.

4. Propose railway spatiotemporal service sharing technology based on the microservice framework to achieve efficient service sharing and elastic and flexible service management.

5. The basic process of constructing railway 3D visualization scenes is clarified, and the scene construction process is split to provide a systematic and standardized construction idea for scene construction.

6. Propose an enhanced visualization method for complex three-dimensional railway scenes to achieve enhanced visualization of scenes, improve scene visualization effects, enrich scene semantic information, and help improve user cognitive ability.

7. The enhanced visualization design of railway three-dimensional geological disaster scenes further verified the effectiveness of the enhanced visualization method.

It should be understood by those of ordinary skill in the art that the exemplary components, systems and devices described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is specifically performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different devices to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

It should be clear that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of simplicity, a detailed description of known devices is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the device process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

In the present application, features described and/or illustrated for one embodiment may be used in the same manner or in a similar manner in one or more other embodiments, and/or combined with features of other embodiments or replace features of other embodiments.

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

Claims (10)

1. A railway multi-modal spatio-temporal information adaptive visualization engine, comprising: the system comprises a data layer, a calculation layer, a service layer and a display layer which are sequentially in communication connection;

The data layer is used for classifying, storing and self-adaptively organizing railway space-time data used for representing railway multi-mode space-time information;

The computing layer is used for providing computing power support for the service layer by adopting cloud-edge cooperative computing architecture aiming at the railway service scene currently designated by the user and calling the railway space-time data stored by the data layer so as to obtain a space-time analysis computing result aiming at the railway service scene;

The service layer is used for correspondingly generating railway space-time service data aiming at the railway service scene according to a space-time analysis calculation result aiming at the railway service scene based on a preset micro-service architecture;

The display layer is used for carrying out railway three-dimensional visual scene construction and semantic enhancement processing according to railway space-time service data which are called from the service layer and aim at the railway service scene so as to obtain railway three-dimensional visual scene data which aim at the railway service scene, and displaying enhancement visual effect data corresponding to the railway service scene in various types of user terminals based on the railway three-dimensional visual scene data.

2. The railway multi-modal temporal information adaptive visualization engine of claim 1, wherein the data layer comprises: the railway space-time data classification storage module is in communication connection with the calculation layers, and the railway space-time data self-adaptive organization module is in communication connection with the railway space-time data classification storage module;

The railway space-time data classification storage module is used for classifying and storing the railway space-time data according to the type of the railway space-time data;

The railway space-time data self-adaptive organization module is used for self-adaptively organizing the railway space-time data in the railway space-time data classification storage module by adopting a self-adaptive linear octree space subdivision mode.

3. The railway multi-modal temporal information adaptive visualization engine of claim 2, wherein the types of railway temporal and spatial data used to represent railway multi-modal temporal information comprises: relational data, file type data, and stream data;

Correspondingly, a relational database, a file database and a stream data memory database which are respectively in communication connection with the calculation layers are arranged in the railway space-time data classification storage module;

the relational database is used for storing railway space-time data with the type belonging to the relational data;

Wherein the railway space-time data of which the type belongs to the relational data comprises: vector data, raster data, remote sensing data, form data, and log data;

the file type database is used for storing railway space-time data with the type belonging to the file type data;

Wherein the railway space-time data of which the type belongs to the file type data comprises: terrain tile data, point cloud data, oblique photography data, railway three-dimensional model data, document object model data, and video image data, wherein the railway three-dimensional model data comprises: building information model data and digital elevation model data;

the stream data memory database is used for storing railway space-time data with the type belonging to the stream data;

wherein the railway spatiotemporal data of the type belonging to the stream data comprises: railway POI data, spatial location data, weather data and road condition data.

4. The railway multi-modal temporal information adaptive visualization engine of claim 3, wherein the railway temporal data adaptive organization module comprises: the topographic data organization unit and the three-dimensional model organization unit are respectively in communication connection with the railway space-time data classification storage modules;

The terrain data organization unit is used for establishing a corresponding tile map pyramid model based on a space octree subdivision mode aiming at the terrain tile data;

The three-dimensional model organization unit is used for stretching the three-dimensional model data into a building white mould block model based on the space octree subdivision mode according to the model bottom surface outline of the three-dimensional model data.

5. The railway multi-modal temporal information adaptive visualization engine of claim 1, wherein the computing layer comprises: the cloud computing module and the side computing module are respectively in communication connection with the data layer and the service layer;

The cloud computing module and the side computing module are respectively provided with a plurality of computing groups, and each computing group comprises a data scheduling task node and an analysis computing task node which are mutually connected in a communication mode; the cloud computing module and the side computing module synchronously transmit data and push results;

The data scheduling task node is used for calling the railway space-time data stored in the data layer according to a calculation task corresponding to a railway service scene currently designated by a user and transmitting the railway space-time data to the analysis calculation task node;

the analysis calculation task node is used for calculating a calculation task corresponding to a railway service scene currently designated by a user based on the railway space-time data transmitted by the data scheduling task node so as to obtain a space-time analysis calculation result aiming at the railway service scene, and transmitting the space-time analysis calculation result to the service layer.

6. The railway multi-modal temporal information adaptive visualization engine of claim 1, wherein the service layer comprises: the service arrangement center, the service dynamic registration and management module and the service release module are respectively in communication connection with the computing layer and the display layer;

The service arrangement center is used for carrying out workflow assignment, service aggregation, service chain synthesis and service arrangement on each service application based on a shared analysis service container combination corresponding to a preset micro-service architecture;

the service dynamic registration and management module is used for carrying out dynamic service registration, service evaluation, service inquiry and service monitoring on each service application based on the shared analysis service container combination according to the space-time analysis calculation result on the railway service scene so as to generate railway space-time service data on the railway service scene;

the service release module is used for releasing the railway space-time service data aiming at the railway service scene to the display layer by adopting a service interface based on a preset GIS space service engine.

7. The railway multi-modal temporal information adaptive visualization engine of claim 1, wherein the presentation layer comprises: the visual scene construction module and the scene semantic information enhancement module are respectively in communication connection with the service layers;

The visual scene construction module is used for respectively constructing a virtual terrain scene, a dynamic simulation scene and a ground feature scene corresponding to the railway service scene according to the railway space-time service data which are transmitted by the service layer and aim at the railway service scene so as to obtain railway three-dimensional visual scene data which are aim at the railway service scene;

The scene semantic information enhancement module is used for carrying out semantic information enhancement processing on railway three-dimensional visual scene data aiming at the railway service scene by adopting a three-dimensional scene enhancement visual mode of dynamic and static visual variable coordination, and sending the railway three-dimensional visual scene data subjected to the semantic information enhancement processing to a plurality of types of user terminals for display so as to carry out data interaction with each user terminal;

the three-dimensional scene enhancement visualization mode of the dynamic and static visual variable cooperation is a mode of carrying out semantic enhancement processing on the static visual variable and the dynamic visual variable by a preset enhancement expression method.

8. The railway multi-modality spatio-temporal information adaptive visualization engine of any of claims 1 to 7, wherein the railway traffic scenario includes: geological disaster scenes, emergency rescue scenes and train scheduling scenes.

9. The railway multi-modal spatiotemporal information adaptive visualization engine of claim 8, wherein the railway three-dimensional visualization scene data comprises: the corresponding relation among the scene category, the scene element, the enhancement expression mode and the preset enhancement visual effect data;

Correspondingly, if the railway service scene currently designated by the user is the geological disaster scene, the scene categories in the railway three-dimensional visual scene data corresponding to the geological disaster scene include: disaster causes, disaster objects and disaster situation objects;

Wherein the scene elements corresponding to the disaster causes include: earthquake and heavy rainfall; the method for enhancing the expression corresponding to the earthquake comprises the following steps: controlling the shaking of the three-dimensional scene, and simultaneously setting the frequency, amplitude and duration of the shaking and representing the time and place of the occurrence of the earthquake by matching with characters; the method for enhancing expression corresponding to the heavy rainfall comprises the following steps: simulating real rainfall conditions by using a particle system;

The scene elements corresponding to the disaster objects include: occurrence time, occurrence place, influence range, sliding speed, sliding time, sliding square quantity, track energy, river direction, beidou monitoring and meteorological monitoring; the enhanced expression method corresponding to the occurrence time comprises the following steps: the landslide occurrence time is represented by adopting character marks; the enhanced expression method corresponding to the occurrence place comprises the following steps: a character mark is adopted to represent the landslide occurrence place; the enhancement expression method corresponding to the influence range comprises the following steps: the sliding range is represented by adopting a coverage surface, and simultaneously, single-color display is matched; the enhanced expression method corresponding to the sliding speed comprises the following steps: the sliding speed is expressed by adopting text marks; the enhanced expression method corresponding to the sliding time comprises the following steps: a progress bar is adopted to control the landslide process, and the sliding time is represented by a character mark; the enhanced expression method corresponding to the sliding amount comprises the following steps: the sliding earthwork quantity is expressed by adopting character marks; the enhanced expression method corresponding to the track energy comprises the following steps: the landslide track energy is represented by adopting one-to-one corresponding colors; the method for enhancing expression corresponding to the river direction comprises the following steps: the river direction is indicated by an arrow; the Beidou monitoring corresponding enhanced expression method comprises the following steps: the symbol marks are adopted to represent the positions of Beidou monitoring stations at the bridge, and the displacement monitoring information is represented by a graph; the enhanced expression method corresponding to the meteorological monitoring comprises the following steps: the position of a weather monitoring station at the bridge is represented by symbol marks, and weather monitoring information is represented by a chart;

The scene elements corresponding to the disaster objects comprise: damaged roads and damaged buildings; the enhanced expression method corresponding to the damaged road comprises the following steps: the construction passageway range affected by landslide is represented by a single color line; the method for enhancing expression corresponding to the damaged building comprises the following steps: according to the severity of damage, the block model is adopted to match three single colors for expression, the highlighting and flickering effects are increased, and meanwhile, the self-explanatory symbol is adopted to represent the building type.

10. A method for adaptively visualizing multi-modal temporal information of a railway, which is realized by applying the multi-modal temporal information of a railway adaptive visualization engine according to any one of claims 1 to 9, and comprises the following steps:

The service layer receives a railway service scene currently appointed by a user and transmitted by the user terminal forwarded by the display layer, and transmits the railway service scene currently appointed by the user to the calculation layer;

The calculation layer adopts cloud-edge cooperative calculation architecture aiming at the railway service scene, invokes the railway space-time data stored by the data layer to provide calculation force support for the service layer so as to obtain space-time analysis calculation results aiming at the railway service scene, and transmits the space-time analysis calculation results to the service layer;

the service layer correspondingly generates railway space-time service data aiming at the railway service scene according to a space-time analysis calculation result aiming at the railway service scene based on a preset micro-service architecture, and distributes the railway space-time service data aiming at the railway service scene to the display layer;

The display layer carries out railway three-dimensional visual scene construction and semantic enhancement processing according to railway space-time service data which are called from the service layer and aim at the railway service scene so as to obtain railway three-dimensional visual scene data which aim at the railway service scene, and display enhancement visual effect data corresponding to the railway service scene in various types of user terminals based on the railway three-dimensional visual scene data.