A multi-time series three-dimensional visualization method for flood evolution

Abstract

The present invention discloses a multi-time series three-dimensional visualization expression method for flood evolution, including: parsing NetCDF data of a two-dimensional hydrodynamic model, establishing a feature index library and a spatial library, storing indicators such as water level, water depth, flow velocity, and spatial information of irregular triangular grids. Generate the association relationship between grid points and feature indicators, and create a time series cache attribute table and a spatial data set. Read three-dimensional point spatial data and feature index data, construct a three-dimensional spatial grid, and use a two-dimensional surface data set for range clipping. Use thread pool technology to generate a time series service that conforms to the three-dimensional model tile data format. Load the three-dimensional service and terrain service on the browser side, perform color and height rendering based on feature values, and dynamically display the flood process. Set a timer to achieve three-dimensional visualization playback moment by moment. The advantages of the present invention are: effectively improve the dynamic display capability of flood evolution, and support efficient three-dimensional visualization rendering and real-time playback.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy engineering and geographic information system, and in particular to a multi-time series three-dimensional visualization expression method for flood evolution. Background Art

[0002] Digital twin water conservancy construction is one of the six implementation paths to promote high-quality development of water conservancy in the new stage. Its core goal is to achieve comprehensive perception, information sharing and simulation of water conservancy objects and data through digital twin technology. In recent years, with the frequent occurrence of flood disasters and the increase in extreme climate events, high-efficiency and high-fidelity simulation of flood situations has become an important task in digital twin water conservancy construction.

[0003] Currently, there are two main ways to render flood evolution simulation:

[0004] Single-moment rendering: By making thematic maps of water level, water depth and other elements at a single moment, raster images or raster tile services are published to render the flood status. This method has two major problems:

[0005] A new service or layer data needs to be loaded at each moment, resulting in the need to frequently switch between different services or data for the expression of multiple moments, which reduces the coherence and performance of multi-time series dynamic rendering.

[0006] The raster layer is expressed in the form of a two-dimensional map, which cannot intuitively display the changes in flood water surface height in a three-dimensional scene. It is also difficult to effectively integrate and display it with three-dimensional data such as terrain, oblique photography, and BIM, which limits the application effect of digital twin water conservancy.

[0007] Vector grid rendering: The hydrodynamic model results are rendered directly on the browser side in the form of vector grid data, and the indicator changes at different times are expressed by changing the color. However, due to the complex grid and huge data volume of the hydrodynamic model calculation results, which usually reaches hundreds of thousands or even millions, the browser side faces WebGL performance bottlenecks when loading and rendering, and the rendering efficiency is low, which makes it difficult to meet the needs of dynamic and efficient rendering in digital twin water conservancy. Summary of the invention

[0008] The present invention aims to solve the defects of the prior art that the rendering processing time is long and it cannot be displayed in superposition with three-dimensional data, and provides a multi-time series three-dimensional visualization expression method of flood evolution. Based on the three-dimensional water conservancy data base, the two-dimensional hydrodynamic model results are quickly and efficiently rendered in three dimensions, which improves the rendering performance and timeliness, and at the same time enhances the simulation and deduction capabilities of digital twin water conservancy.

[0009] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0010] A multi-time series three-dimensional visualization expression method for flood evolution includes the following steps:

[0011] Step 1: Parse the NetCDF data of the two-dimensional hydrodynamic model, establish a time-series cache element index library and a spatial library, the element index library is used to store element index information, and the spatial library is used to store the spatial information of irregular triangular grids; read the NetCDF data of the two-dimensional hydrodynamic model, parse and obtain the spatial coordinates of the grid points, the grid point index and the element index information, and establish the association relationship between the grid points and their corresponding element indicators; extract the element index values ​​of the grid points at all times, sort them in chronological order, and store them in the attribute table; store the spatial coordinates and grid point index of the irregular grid in the spatial library, and generate a two-dimensional surface data set with the maximum grid coverage through point group regionalization processing; establish an association view between the three-dimensional point spatial table and the multi-element index attribute table, and generate a multi-indicator time series service based on the view.

[0012] Step 2: Read the 3D point spatial data and feature index data from the associated view, construct the 3D spatial grid and time series model attribute information, use the 2D surface dataset as the outer boundary information, clip and constrain the model tile range, use the thread pool technology to process the data, generate the time series model tile for each feature, and generate a 3D service that conforms to the spatial 3D model tile data format.

[0013] Step 3: Load the single-indicator time-series cache 3D service and terrain service on the browser side, render the color and height based on the grid point element values, and dynamically display the changes in the flood process elements; set the timer to dynamically play moment by moment in chronological order to achieve a 3D visual expression of the flood evolution process.

[0014] Furthermore, the specific sub-steps of step 1 are as follows:

[0015] 11) Establishing a temporal cache element index library and a spatial library, wherein the element index library is used to store specific element index information including water level, water depth and flow velocity, and the spatial library is used to store spatial coordinate information of irregular triangular grids;

[0016] 12) Read and parse the NetCDF data of the two-dimensional hydrodynamic model calculation results, and obtain the global attributes and all variables of the NetCDF data. According to the preset variable field names, extract the spatial coordinate information of the grid points, the grid point index information, and the information of each indicator element, and establish the association relationship between the grid points and the corresponding element indicator values ​​based on the grid point index and the element indicator index information;

[0017] 13) Extract the number of elements and related indicator information of the hydrodynamic model from the NetCDF data, and create a corresponding number of attribute tables in the time series cache element indicator library according to the number of extracted elements.

[0018] 14) For the first element index, process each grid point in a loop. Based on the established relationship between the grid point and the index value, extract the first element index value of the grid point at all time steps, sort them in chronological order, and use the binary byte stream method to store the single index data of a single grid point at all times as a row of records in the attribute table.

[0019] 15) According to step 4, all factor indicators are cyclically parsed to form multiple attribute tables containing each moment, which constitute the multi-moment attribute information of the time series model.

[0020] 16) Extract the spatial coordinates (x, y, z) and grid point index information of the irregular grid from the NetCDF data, create a spatial table in the time series cache spatial library, and store the grid point data in the form of a three-dimensional point data set;

[0021] 17) Perform point group regionalization processing on the three-dimensional point data set generated in step 16) to generate maximum grid coverage data, and create a spatial table of the two-dimensional surface data set in the time series cache space library to store the maximum grid coverage data.

[0022] 18) Based on the correlation between grid points and feature index values, create a correlation view between the three-dimensional point space table and multiple feature index attribute tables.

[0023] Furthermore, the specific sub-steps of step 2 are as follows:

[0024] 21) Through the associated view, read the three-dimensional point space table data, and build the three-dimensional space grid information of the time series model based on the grid point index information obtained by NetCDF analysis; read the element indicator data and establish the time series model attribute information of each indicator.

[0025] 22) The two-dimensional surface dataset is used as the outer boundary information of the time series cache to clip and constrain the range of the time series model tiles.

[0026] 23) The three-dimensional spatial grid information, multi-time attribute information and outer boundary information of the time series model are taken as input, and the thread pool technology is used to process multiple indicators in parallel to generate the time series model tiles for each element respectively.

[0027] 24) After each single-indicator time series model tile is generated, it is automatically published as a three-dimensional service that complies with the spatial three-dimensional model tile data format standard according to the preset configuration information until all element services are published.

[0028] Furthermore, the specific sub-steps of step 3 are as follows:

[0029] 31) Load the generated 3D service and 3D terrain service on the browser.

[0030] 32) The client presets color segmentation information and establishes a correspondence between the indicator value and the rendering color based on the maximum and minimum values ​​of a single indicator. The color of the grid surface is interpolated according to the indicator value of the grid point at each moment, and the changes in the element indicators during the flood process are displayed through color rendering.

[0031] 33) Based on the value of the element index of each grid point, control the height change of the time series cache model vertex at different time steps. Through height rendering combined with terrain service, show the height change during the flood process.

[0032] 34) Set a timer to dynamically play the changes of each single element moment by moment in chronological order, so as to realize a three-dimensional visualization of the water level, water depth and flow rate in the whole process of flood evolution.

[0033] Compared with the prior art, the advantages of the present invention are:

[0034] 1. Improve flood prediction and management capabilities: Through multi-time series 3D visualization, the evolution of floods can be clearly displayed, helping to predict the scope and intensity of flood impacts, and supporting scientific decision-making and effective management.

[0035] 2. Enhance disaster emergency response: Provide intuitive 3D views to help emergency responders understand flood dynamics and optimize resource allocation and emergency measures.

[0036] 3. Improve public risk awareness: By visualizing the evolution of floods, the public's awareness of flood risks can be raised, which will help enhance disaster prevention and mitigation awareness and actions.

[0037] 4. Improve data processing efficiency: Use advanced data analysis and processing technologies to optimize the storage and access of large-scale hydrodynamic model data and improve the efficiency of data processing and visualization.

[0038] 5. Achieve high-precision and high-fidelity rendering: Through three-dimensional visualization and dynamic rendering technology, it provides high-precision flood status display, which can more realistically simulate the changes in water level, water depth, flow rate and other factors.

[0039] 6. Support comprehensive decision-making analysis: Effectively integrate with three-dimensional terrain, remote sensing data and other environmental data to provide a comprehensive flood analysis view, support comprehensive decision-making and optimize management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a multi-time series three-dimensional visualization expression method of flood evolution according to an embodiment of the present invention.

[0041] Figure 2Schematic diagram A of a three-dimensional rendering expression result according to an embodiment of the present invention;

[0042] Figure 3 Schematic diagram B of the three-dimensional rendering expression result of an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0044] like Figure 1 As shown, a multi-time series three-dimensional visualization expression method for flood evolution includes the following steps:

[0045] Step 1: Parse the 2D hydrodynamic model NetCDF data

[0046] 11): Establish a time-series cache element index library and a space library, where the element index library is used to store the water level, water depth, flow velocity and other element index information of the two-dimensional hydrodynamic model calculation results, and the space library is used to store the irregular triangular grid space information of the two-dimensional hydrodynamic model.

[0047] 12): Read the NetCDF data of the two-dimensional hydrodynamic model calculation results, parse and obtain the global attributes and all variables of the NetCDF data. According to the preset variable field names, extract the grid point spatial coordinate information, grid point index information, each indicator element information and index information, and establish the association relationship between a single grid point and its corresponding element indicator value based on the grid point index and element indicator index information.

[0048] 13): Extract the number of elements and indicator information of the hydrodynamic model from the NetCDF data variable, and create an equal number of attribute tables in the time series cache element indicator library according to the number of elements.

[0049] 14): For the first element index parsed, loop through each grid point, extract the first element index value of the grid point at all times according to the association between the grid point and the index value established in step 12), and sort them in chronological order. Use binary byte stream to store the single index data of a single grid point at all times as a row of records in the attribute table.

[0050] 15): According to step 14), complete the cyclic parsing process of all element indicators to form multiple attribute tables.

[0051] 16): Extract the spatial coordinates (x, y, z) of the grid points of the irregular grid and the grid point index information that make up the grid from the NetCDF data variable. Create a spatial table in the time series cache spatial library and store the grid point data in the form of a three-dimensional point data set.

[0052] 17): Using point group regionalization processing, the three-dimensional point data set generated in step 16) is processed into the maximum grid coverage area, that is, irregular grid boundary data. A new spatial table is created in the time series cache spatial library, and the maximum grid coverage area data is stored in the library in the form of a two-dimensional surface data set.

[0053] 18): Based on the association relationship between the grid points and the index values ​​established in step 12), an association view is established between the three-dimensional point space table and the attribute tables corresponding to the multiple element indicators.

[0054] Step 2: Generate a multi-indicator time series service

[0055] 21): Through the associated view generated in step 18), read the three-dimensional point space table data, and construct the three-dimensional points into the three-dimensional space grid information of the time series model based on the grid point index information obtained by NetCDF analysis; read the element indicator data and establish the multi-time attribute information of each indicator time series model.

[0056] 22): The two-dimensional surface dataset generated in step 17) is used as the outer boundary information of the time series cache to constrain and clip the range of the time series model tile.

[0057] 23): Take the three-dimensional spatial grid information of the time series model established in step 21), the multi-time attribute information of the time series model and the outer boundary information of the time series cache established in step 22) as input data, use thread pool technology to process multiple indicators in parallel, and generate time series model tiles for each element respectively.

[0058] 24): After the single indicator time series model tile is generated, it is automatically published as a three-dimensional service that conforms to the spatial three-dimensional model tile data format standard according to the preset configuration information until all indicator element services are published.

[0059] Step 3: Dynamically render 3D timing service

[0060] 31): The browser loads the single-indicator time-series cache 3D service address and 3D terrain service generated in step 24).

[0061] 32): The client presets color segmentation information, establishes a corresponding relationship from the indicator value to the rendering color based on the maximum and minimum values ​​of a single indicator, and interpolates the grid surface color at the current moment according to the indicator value of the grid point at each moment. The changes in flood process element indicators are displayed through color rendering.

[0062] 33): Based on the index of each grid point, the height of the time series cache model vertex is controlled at different times. Through height rendering and overlay comparison with terrain services, the height change of the flood process is displayed (for example, the height change of the water depth element value is used to express the flood inundation effect).

[0063] 34): Set a timer to play a single indicator dynamically at each moment, and realize the multi-time series 3D rendering expression of water level, water depth, flow rate and other elements in the whole process of flood evolution ( Figure 2 and Figure 3 ).

[0064] The method according to the present invention described above can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the multi-time series three-dimensional visualization expression method of flood evolution described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the processing shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the processing shown herein.

[0065] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A multi-time series three-dimensional visualization expression method for flood evolution, characterized in that: The following steps are involved: Step 1: Parse the NetCDF data of the two-dimensional hydrodynamic model, establish a temporal cache element index library and a spatial library, the element index library is used to store element index information, the element index information includes element index data of water level, water depth and flow velocity, and the spatial library is used to store the spatial information of irregular triangular grids; read the NetCDF data of the two-dimensional hydrodynamic model, parse and obtain the spatial coordinates of the grid points, the grid point index and the element index information, and establish the association relationship between the grid points and their corresponding element indicators; extract the element index values ​​of the grid points at all times, sort them in chronological order, and store them in the attribute table; The spatial coordinates and grid point indexes of the irregular grid are stored in the spatial library, and a two-dimensional surface data set with the maximum grid coverage is generated through point group regionalization processing; an associated view between the three-dimensional point spatial table and the multi-factor indicator attribute table is established, and a multi-indicator time series service is generated based on the view; Step 2: Read the 3D point spatial data and feature index data from the associated view, construct the 3D spatial grid and time series model attribute information, use the 2D surface dataset as the outer boundary information, clip and constrain the model tile range, use the thread pool technology to process the data, generate the time series model tile of each feature, and generate a 3D service that conforms to the spatial 3D model tile data format; Step 3: The browser loads the single indicator time series cache 3D service and terrain service in the element indicator information, performs color and height rendering based on the element indicator values ​​corresponding to the grid points, and dynamically displays the changes in the flood process elements; Set a timer and play the video dynamically moment by moment in chronological order to achieve a three-dimensional visual expression of the flood evolution process.

2. The multi-time series three-dimensional visualization expression method of flood evolution according to claim 1 is characterized by: The specific sub-steps of step 1 are as follows: 11) Establish a temporal cache element index database and a spatial database; 12) Read and parse the NetCDF data of the two-dimensional hydrodynamic model calculation results, obtain the global attributes and all variables of the NetCDF data; extract the spatial coordinate information of the grid points, the grid point index information, and the information of each indicator element according to the preset variable field name, and establish the association relationship between the grid points and the corresponding element indicator values ​​according to the grid point index and the element indicator index information; 13) Extract the number of elements and related indicator information of the hydrodynamic model from the NetCDF data, and create a corresponding number of attribute tables in the time series cache element indicator library according to the number of extracted elements; 14) For the first element index, process each grid point in a loop; based on the established association between the grid point and the index value, extract the first element index value of the grid point at all time steps, sort them in chronological order, and use the binary byte stream method to store the single index data of a single grid point at all times as a row of records in the attribute table; 15) According to step 4, all factor indicators are cyclically parsed to form multiple attribute tables containing each moment, which constitute the multi-moment attribute information of the time series model; 16) Extract the spatial coordinates (x, y, z) and grid point index information of the irregular grid from the NetCDF data, create a spatial table in the time series cache spatial library, and store the grid point data in the form of a three-dimensional point data set; 17) performing point group regionalization processing on the three-dimensional point data set generated in step 16) to generate maximum grid coverage data, and creating a spatial table of a two-dimensional surface data set in the time series cache space library to store the maximum grid coverage data; 18) Based on the correlation between grid points and feature index values, create a correlation view between the three-dimensional point space table and multiple feature index attribute tables.

3. The multi-time series three-dimensional visualization expression method of flood evolution according to claim 2 is characterized by: The specific sub-steps of step 2 are as follows: 21) Read the three-dimensional point space table data through the associated view, and build the three-dimensional space grid information of the time series model based on the grid point index information obtained by NetCDF analysis; Read the factor indicator data and establish the time series model attribute information of each indicator; 22) Using the two-dimensional surface dataset as the outer boundary information of the time series cache to clip and constrain the range of the time series model tiles; 23) Taking the three-dimensional spatial grid information, multi-time attribute information and outer boundary information of the time series model as input, the thread pool technology is used to process multiple indicators in parallel to generate the time series model tiles of each element respectively; 24) After each single-indicator time series model tile is generated, it is automatically published as a three-dimensional service that complies with the spatial three-dimensional model tile data format standard according to the preset configuration information until all element services are published.

4. The multi-time series three-dimensional visualization expression method of flood evolution according to claim 3 is characterized by: The specific sub-steps of step 3 are as follows: 31) Loading the generated 3D service and 3D terrain service on the browser side; 32) The client presets color segmentation information, and establishes a correspondence between the indicator value and the rendering color based on the maximum and minimum values ​​of a single indicator; the color of the grid surface is interpolated according to the indicator value of the grid point at each moment, and the changes of the element indicators during the flood process are displayed through color rendering; 33) Based on the value of the element index of each grid point, control the height change of the time series cache model vertex at different time steps; through height rendering combined with terrain services, show the height change during the flood process; 34) Set a timer to dynamically play the changes of each single element moment by moment in chronological order, so as to realize a three-dimensional visualization of the water level, water depth and flow rate in the whole process of flood evolution.

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