Flood disaster digital twinborn processing method and electronic equipment

Abstract

The invention provides a flood disaster digital twinborn processing method and electronic equipment, and is applied to a flood disaster digital twinborn platform. Obtaining original data corresponding to the scene to be predicted; obtaining configuration information corresponding to the scene to be predicted, wherein the configuration information comprises at least one of the following items: a boundary condition and a Manning coefficient; according to the original data and the configuration information, using a flood simulation model to predict and obtain a flood prediction result of the to-be-predicted scene, the flood prediction result including urban inundation data and drainage pipe network flow data, and the urban inundation information being used for indicating the inundation height of each region in the city; and storing the flood prediction result. A user input interface and an operation method are provided, the transmission time is effectively shortened, the threshold for learning and using the flood disaster digital twinborn platform is lowered, and the use flexibility and adaptability are improved.

Description

Technical Field

[0001] The present application relates to the field of digital information technology, and in particular to a flood disaster digital twin processing method and electronic equipment. Background Art

[0002] In recent years, the concept and technology of digital twins have risen rapidly. It has built a bridge between the real world and the digital world in various fields and has been rapidly applied in all walks of life. Most urban flood simulations are mainly developed based on numerical simulation systems, mainly for professional technicians. They are highly professional but less intuitive, which is not conducive to auxiliary decision-making and visual display.

[0003] At present, digital twin technology still faces challenges in flood disaster process prediction, real-time data collection and integration, and visualization platform technology, and its application in the field of urban floods is still limited.

[0004] Therefore, there is an urgent need for a platform that can automatically predict flood disasters and display the prediction results. Summary of the invention

[0005] The purpose of this application is to provide a flood disaster digital twin processing method and electronic equipment to address the deficiencies in the above-mentioned prior art, so as to realize fully automatic prediction of flood disasters and display the prediction results.

[0006] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0007] In a first aspect, an embodiment of the present application provides a flood disaster digital twin processing method, which is applied to a flood disaster digital twin platform, and the method includes:

[0008] Acquire original data corresponding to the scene to be predicted from the target database, wherein the original data includes at least one of the following: urban terrain data, urban infrastructure data, and meteorological data;

[0009] Acquire configuration information corresponding to the scenario to be predicted, the configuration information including at least one of the following: boundary conditions and Manning coefficients, wherein the boundary conditions are used to adjust rainfall data and perform tide level simulation, and the Manning coefficient is used to indicate water flow resistance;

[0010] According to the original data and the configuration information, a flood simulation model is used to predict and obtain a flood prediction result of the scene to be predicted, wherein the flood prediction result includes: urban inundation data and drainage network flow data, and the urban inundation information is used to indicate the inundation height of each area in the city;

[0011] The flood prediction result is stored.

[0012] Optionally, the using a flood simulation model to predict and obtain a flood prediction result of the scene to be predicted according to the original data and the configuration information includes:

[0013] Determining non-floodable areas in the city based on the urban infrastructure data;

[0014] Creating a mask for the non-floodable area in the urban infrastructure data to obtain new original data;

[0015] The new raw data and the configuration information are input into the flood simulation model to predict the flood prediction result of the scene to be predicted.

[0016] Optionally, storing the flood prediction result includes:

[0017] Simplifying the flood prediction results to obtain a plurality of prediction result files, wherein the plurality of prediction result files include: a flood data file and at least one pipe network flow data file;

[0018] Storing the plurality of prediction result files in a front-end device;

[0019] Obtaining directory information of each prediction result file;

[0020] The directory information of each prediction result file is stored in the target database and bound to the identifier of the scene to be predicted.

[0021] Optionally, the urban inundation data is stored in the form of a raster file, and the drainage network flow data is stored in the form of comma separated values;

[0022] The flood prediction results are simplified to obtain multiple prediction result files, including:

[0023] Traversing each grid in the grid file, for the current grid traversed, if the value of the current grid is less than a preset threshold, modifying the value of the current grid to a preset invalid value;

[0024] After the traversal of each grid in the grid file is completed, the grid file at the end of the traversal is used as the flooding data file;

[0025] The drainage network flow data is sliced ​​to obtain a plurality of sliced ​​data, and each sliced ​​data is stored in a network flow data file, wherein one network flow data file is used to store flow data of one or more drainage networks.

[0026] Optionally, the method further comprises:

[0027] Obtaining the identifier of the scene to be displayed and the flood display instruction input by the user;

[0028] According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed;

[0029] According to the directory information of the flood data file in the prediction result file corresponding to the scene to be displayed, the urban flood data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

[0030] Optionally, the process of generating the flooding data file includes:

[0031] According to the area in the city to which each grid in the flood data file belongs, the area is divided into grids to obtain a grid set corresponding to each area in the city;

[0032] Determine the flooded area in each area, the flooded depth of each flooded area, and the flooded area according to the position, size, adjacent relationship, and value of each grid in the grid set corresponding to the area;

[0033] According to the flooding depth and the flooding area of ​​the flooded area, a flooding image is superimposed and displayed on the flooded area on the map, and the flooding image is used as a flooding data file.

[0034] Optionally, the displaying a flooded image on the flooded area on the map based on the flooded depth and flooded area of ​​the flooded area includes:

[0035] Determining a display style of the flooded image according to the flooded depth and the flooded area, the display style comprising: display color;

[0036] A flooded image is displayed superimposed on the flooded area on the map according to the display style, and the depth of the display color is positively correlated with the flooded depth.

[0037] Optionally, the method further comprises:

[0038] Obtaining the identifier of the scene to be displayed and the pipe network flow display instruction input by the user;

[0039] According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed;

[0040] According to the directory information of each of the pipe network flow data files in the prediction result file corresponding to the scene to be displayed, the drainage pipe network flow data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

[0041] Optionally, it also includes:

[0042] Obtaining urban inundation data in the flood prediction results corresponding to the scene to be compared and the measured water depth data of the scene to be compared;

[0043] The urban inundation data is compared with the measured water depth data, and the comparison result is displayed.

[0044] In a second aspect, the embodiment of the present application further provides a flood disaster digital twin processing device, the device comprising:

[0045] An acquisition module, used to acquire original data corresponding to the scene to be predicted from a target database, wherein the original data includes at least one of the following: urban terrain data, urban infrastructure data, and meteorological data;

[0046] An acquisition module, used to acquire configuration information corresponding to the scene to be predicted, wherein the configuration information includes at least one of the following: a boundary condition and a Manning coefficient, wherein the boundary condition is used to adjust rainfall data and perform tide level simulation, and the Manning coefficient is used to indicate water flow resistance;

[0047] A prediction module, configured to use a flood simulation model to predict, based on the original data and the configuration information, a flood prediction result of the scene to be predicted, wherein the flood prediction result includes: urban inundation data and drainage network flow data, wherein the urban inundation information is used to indicate the inundation height of each area in the city;

[0048] A storage module is used to store the flood prediction results.

[0049] Optionally, the prediction module is specifically used for:

[0050] Determining non-floodable areas in the city based on the urban infrastructure data;

[0051] Creating a mask for the non-floodable area in the urban infrastructure data to obtain new original data;

[0052] The new raw data and the configuration information are input into the flood simulation model to predict the flood prediction result of the scene to be predicted.

[0053] Optionally, the storage module is specifically used for:

[0054] Simplifying the flood prediction results to obtain a plurality of prediction result files, wherein the plurality of prediction result files include: a flood data file and at least one pipe network flow data file;

[0055] Storing the plurality of prediction result files in a front-end device;

[0056] Obtaining directory information of each prediction result file;

[0057] The directory information of each prediction result file is stored in the target database and bound to the identifier of the scene to be predicted.

[0058] Optionally, the urban inundation data is stored in the form of a raster file, and the drainage network flow data is stored in the form of comma separated values;

[0059] The storage module is specifically used for:

[0060] Traversing each grid in the grid file, for the current grid traversed, if the value of the current grid is less than a preset threshold, modifying the value of the current grid to a preset invalid value;

[0061] After the traversal of each grid in the grid file is completed, the grid file at the end of the traversal is used as the flooding data file;

[0062] The drainage network flow data is sliced ​​to obtain a plurality of sliced ​​data, and each sliced ​​data is stored in a network flow data file, wherein one network flow data file is used to store flow data of one or more drainage networks.

[0063] Optionally, the display module is used to:

[0064] Obtaining the identifier of the scene to be displayed and the flood display instruction input by the user;

[0065] According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed;

[0066] According to the directory information of the flood data file in the prediction result file corresponding to the scene to be displayed, the urban flood data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

[0067] Optionally, the display module is specifically used for:

[0068] According to the area in the city to which each grid in the flood data file belongs, the area is divided into grids to obtain a grid set corresponding to each area in the city;

[0069] Determine the flooded area in each area, the flooded depth of each flooded area, and the flooded area according to the position, size, adjacent relationship, and value of each grid in the grid set corresponding to the area;

[0070] According to the flooding depth and the flooding area of ​​the flooded area, a flooding image is superimposed and displayed on the flooded area on the map, and the flooding image is used as a flooding data file.

[0071] Optionally, the display module is specifically used for:

[0072] Determining a display style of the flooded image according to the flooded depth and the flooded area, the display style comprising: display color;

[0073] A flooded image is displayed superimposed on the flooded area on the map according to the display style, and the depth of the display color is positively correlated with the flooded depth.

[0074] Optionally, the display module is specifically used for:

[0075] Obtaining the identifier of the scene to be displayed and the pipe network flow display instruction input by the user;

[0076] According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed;

[0077] According to the directory information of each of the pipe network flow data files in the prediction result file corresponding to the scene to be displayed, the drainage pipe network flow data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

[0078] Optionally, the display module is specifically used for:

[0079] Obtaining urban inundation data in the flood prediction results corresponding to the scene to be compared and the measured water depth data of the scene to be compared;

[0080] The urban inundation data is compared with the measured water depth data, and the comparison result is displayed.

[0081] In the third aspect, an embodiment of the present application also provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor, and when the application is running, the processor communicates with the storage medium through the bus, and the processor executes the program instructions to execute the steps of the flood disaster digital twin processing method described in the first aspect above.

[0082] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is read and executes the steps of the flood disaster digital twin processing method described in the first aspect above.

[0083] The beneficial effects of this application are:

[0084] The present application provides a flood disaster digital twin processing method and electronic device, which obtains the original data corresponding to the scene to be predicted and the configuration information corresponding to the scene to be predicted, and then uses the flood simulation model to predict the flood prediction results of the scene to be predicted based on the original data and the configuration information. And the flood prediction results are stored for the subsequent display of the flood prediction results. Simplify and optimize the process of building an urban flood disaster system. Flood prediction results are obtained by overlaying the flood simulation model, and the relevant information of the flood prediction results is stored in the target database, so that the flood test results can be displayed on the front-end interface later. A user input interface and calculation method are provided, which effectively reduces the transmission time, lowers the threshold for learning and using the flood disaster digital twin platform, improves the flexibility and adaptability of use, and adapts to the various needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0086] Figure 1 A schematic flow chart of a first flood disaster digital twin processing method provided in an embodiment of the present application;

[0087] Figure 2 A schematic flow chart of a second flood disaster digital twin processing method provided in an embodiment of the present application;

[0088] Figure 3 A schematic flow chart of a third flood disaster digital twin processing method provided in an embodiment of the present application;

[0089] Figure 4 A schematic flow chart of a fourth flood disaster digital twin processing method provided in an embodiment of the present application;

[0090] Figure 5 A schematic flow chart of a fifth flood disaster digital twin processing method provided in an embodiment of the present application;

[0091] Figure 6 A schematic diagram of a flooding data display interface provided in an embodiment of the present application;

[0092] Figure 7 A flowchart of a sixth flood disaster digital twin processing method improved by the embodiment of the present application;

[0093] Figure 8A schematic diagram of a display interface for flooding data of each area provided in an embodiment of the present application;

[0094] Fig. 9 A schematic diagram of a display interface for pipe network flow data provided in an embodiment of the present application;

[0095] Fig.10 A schematic diagram of a data display interface of an observation station provided in an embodiment of the present application;

[0096] Fig.11 A flowchart of a seventh flood disaster digital twin processing method provided in an embodiment of the present application;

[0097] Fig.12 A schematic diagram of a device for a flood disaster digital twin processing method provided in an embodiment of the present application;

[0098] Fig.13 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0099] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0100] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0101] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0102] The flood disaster digital twin processing method provided in the embodiment of the present application is applied to the flood disaster digital twin platform. The flood disaster digital twin platform includes a three-layer system architecture, namely, a data acquisition and storage layer, a data modeling and integration layer, and a data analysis layer.

[0103] In addition, from the perspective of equipment, the flood disaster digital twin platform involves back-end equipment and front-end equipment. The back-end equipment is mainly used to realize the above-mentioned data simulation, fusion, and data analysis. The front-end equipment is mainly used to realize data visualization.

[0104] The specific implementation process of the flood disaster digital twin processing provided in the embodiment of the present application is explained in detail below.

[0105] Figure 1 The first embodiment of the present application provides a flowchart of a method for processing digital twins of flood disasters, wherein the execution subject of the method is the aforementioned electronic device. Figure 1 As shown, the method includes:

[0106] S101. Acquire original data corresponding to the scene to be predicted from a target database.

[0107] The original data may include at least one of the following: urban terrain data, urban infrastructure data, and meteorological data.

[0108] Alternatively, urban terrain data may refer to information on urban topography, elevation, location of rivers and water bodies, road networks, surface cover and permeability, etc. Such data helps to understand the geographical environment of the city, as well as the path and direction of water flow in the city.

[0109] Meteorological data includes information such as rainfall, temperature, humidity, wind speed, etc. measured by each meteorological station. It can also include information such as the station name, station location, and station type of each meteorological station. By analyzing meteorological data, we can understand the intensity and distribution of rainfall, and thus predict the possibility and scale of floods.

[0110] Urban infrastructure data can include information on roads, bridges, drainage systems, buildings, and underground data, which determine the path of water flow and the distribution of obstacles during floods. It can be used to assess the vulnerability and flood control capabilities of cities, so as to take appropriate measures to mitigate the impact of floods.

[0111] Among them, underground data can include information such as groundwater level, permeability, underground drainage network, etc., for example, it can include the location information (latitude and longitude), length, type and size of the pipeline, etc. By analyzing the underground pipeline network flow data, the urban flood process can be more accurately reflected and the accuracy of urban flood prediction can be improved.

[0112] Optionally, the target database may store raw data of a certain area in multiple different scenarios, and each scenario may store information of each scenario, including typhoon name, typhoon time, storm surge intensity, etc. For example, the raw data of the Typhoon Hato scenario, the raw data of the Typhoon Mangkhut scenario, the raw data of the QS scenario, etc. may be stored. It is worth noting that the raw data of each scenario is for the same area.

[0113] S102: Obtain configuration information corresponding to the scene to be predicted.

[0114] The configuration information may include at least one of the following: boundary conditions, Manning coefficient, etc. The boundary conditions may be used to adjust rainfall data and perform tide simulation, and the Manning coefficient is used to indicate water flow resistance.

[0115] Specifically, the user may input configuration information corresponding to the scenario to be predicted in advance in the front-end interface, and specifically, may input boundary conditions and Manning coefficients.

[0116] S103: According to the original data and the configuration information, a flood simulation model is used to predict and obtain a flood prediction result of the scene to be predicted.

[0117] The flood prediction results may include urban inundation data and drainage network flow data, wherein urban inundation data refers to the inundation height of each area in the city. For example, for the Shenzhen area, the inundation height of each area in the Shenzhen area can be predicted, such as the inundation height of each area in Bao'an District, the inundation height of each area in Futian District, the inundation height of each area in Longgang District, the inundation height of each area in Luohu District, the inundation height of each area in Nanshan District, the inundation height of each area in Yantian District, etc.

[0118] Optionally, in the flood simulation model, urban terrain data provides the ground elevation of the urban area. Boundary conditions can adjust rainfall data, which is a timeline data with rainfall and timeline. In addition, boundary conditions can also provide tide levels, which are used to measure and simulate tide levels during storm surge events. Consider underground drainage network information in flood scenarios, and pipeline parameters should be considered in flood simulation models. Involving issues such as the maximum drainage capacity of the pipeline and the highest pressure tolerance.

[0119] S104: Storing flood prediction results.

[0120] Specifically, the urban inundation data and drainage network flow data in the flood prediction results can be stored in the file directory of the scene to be predicted in the target database. Then, the target database not only stores the relevant information of the original data of the scene to be predicted but also stores the relevant information of the flood prediction results of the scene to be predicted. For example, the storage directory of the original data of the scene to be predicted and the directory of the flood prediction results of the scene to be predicted can be stored in the target database.

[0121] In this embodiment, by obtaining the original data corresponding to the scene to be predicted and obtaining the configuration information corresponding to the scene to be predicted, the flood prediction result of the scene to be predicted is obtained using the flood simulation model according to the original data and the configuration information. The flood prediction results are stored for the subsequent display of the flood prediction results. The process of building the urban flood disaster system is simplified and optimized. The flood prediction results are obtained by overlaying the flood simulation model, and the relevant information of the flood prediction results is stored in the target database, so that the flood test results can be displayed on the front-end interface later. A user input interface and calculation method are provided, which effectively reduces the transmission time, lowers the threshold for learning and using the flood disaster digital twin platform, improves the flexibility and adaptability of use, and adapts to the various needs of users.

[0122] Figure 2 A flow chart of a second flood disaster digital twin processing method provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, in the above S103, based on the original data and the configuration information, the flood simulation model is used to predict the flood prediction result of the scene to be predicted, which may include:

[0123] S201. Determine non-floodable areas in the city based on urban infrastructure data.

[0124] Specifically, since urban infrastructure data include data such as roads, bridges, drainage systems, buildings and underground data, in real flood scenarios, buildings are usually not submerged, and floods generally occur in open spaces such as streets, roads and low-lying areas. Therefore, in order to more accurately simulate flood scenarios and eliminate the impact of buildings, areas with buildings in urban infrastructure data can be treated as non-floodable areas.

[0125] S202: Create a mask for the non-floodable area in the urban infrastructure data to obtain new original data.

[0126] Specifically, a masking technique or a masking technique may be used to mask the building, and the height of the building will not exist in the new original data.

[0127] S203: Input the new original data and configuration information into the flood simulation model to obtain the flood prediction result of the scene to be predicted.

[0128] The flood simulation model performs flood prediction based on urban infrastructure data, urban terrain data, meteorological data and user-entered configuration information without building data, thereby predicting urban inundation data and drainage network flow data in the scenario to be predicted.

[0129] Figure 3 A schematic diagram of the flow of a third flood disaster digital twin processing method provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the storage of flood prediction results in S104 may include:

[0130] S301. Simplify flood prediction results to obtain multiple prediction result files.

[0131] The multiple prediction result files include: a flooding data file and at least one pipe network flow data file.

[0132] Among them, the urban inundation data in the flood prediction results output by the flood simulation model is in the form of an ASC file, and the drainage network flow data is in the form of a CSV file. This type of file data occupies a large amount of memory, so it is necessary to first simplify the flood prediction results output by the flood simulation model to obtain multiple prediction result files, and the obtained multiple prediction result files are static resource files.

[0133] The flooding data file contains the flooding height of each area in the city at different times under the scenario to be predicted. Each pipe network flow data file contains the flow data of each pipe network at different times. Each pipe network flow data file can be an Excel table, which can take each time as a row and the identifier of each pipe network as a column to obtain the flow data of each pipe network at different times.

[0134] S302: Store multiple prediction result files in a front-end device.

[0135] Specifically, the flooding data file and the pipe network flow data file can be stored in the front-end device respectively, that is, each static resource file is stored in the front-end device, and directory information of each prediction result file is established in the front-end device.

[0136] S303: Obtain directory information of each prediction result file.

[0137] Specifically, the directory information of each prediction result file stored in the front-end device is obtained through MongoDB. Since each prediction result file in static form is sorted and combined at time intervals of hours, that is, a group of prediction result files will be obtained every hour, the flooding data file and the pipe network flow data file obtained for a scene to be predicted are very large. Therefore, in this embodiment, it is sufficient to read the directory information of each prediction result file.

[0138] S304: Store the directory information of each prediction result file in the target database and bind it with the identifier of the scene to be predicted.

[0139] Specifically, MongoDB can be used to create a file directory for each prediction result file in the target database. After MongoDB creates the JSON file, the directory information and file name of each prediction result file can be written into the created JSON file, and the obtained JSON file is bound to the identifier of the scene to be predicted to obtain the association relationship between the JSON file and the identifier of the scene to be predicted, so as to store the obtained JSON file in the target database. This makes it easy for the subsequent front end to call each prediction result file under the scene to be predicted.

[0140] MongoDB has the following advantages: MongoDB is a document database that uses a flexible JSON-like document format to store data, and does not require a predefined fixed table structure. Generally, all flood data is stored and loaded in JSON format files on the web front end. This makes it easy to adjust and modify the data model when storing data to adapt to changing needs. Since MongoDB has advantages in flexibility, scalability, performance, etc., this embodiment uses MongoDB to process large-scale data and complex application scenarios.

[0141] In this embodiment, the flood prediction results are converted into prediction result files, specifically, the geographic format files are converted into front-end JSON data, so that the front-end can quickly call and read the prediction result files under the scenarios to be predicted, thereby displaying the prediction results to be predicted.

[0142] Optionally, urban inundation data are stored in the form of raster files (ASC) and drainage network flow data are stored in the form of comma separated values ​​(CVS).

[0143] Figure 4 A flowchart of a fourth flood disaster digital twin processing method provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, in the above S301, the flood prediction results are simplified to obtain multiple prediction result files, which may include:

[0144] S401, traversing each grid in the grid file, and for the current grid that has been traversed, if the value of the current grid is less than a preset threshold, modifying the value of the current grid to a preset invalid value.

[0145] Each grid in the grid file refers to each area in the city, the preset threshold is, for example, 0.1 m, and the preset invalid value may be, for example, 0. The value of the current grid refers to the flooding data of the current grid.

[0146] For the current grid traversed, if the flooded data of the current grid is less than the preset threshold, the value of the current grid is modified to the preset invalid value. Among them, if the value of the current grid is less than the preset threshold, it means that the flooding degree of the current grid is relatively slight, that is, the current grid belongs to the slightly flooded area or the slightly waterlogged area, then the value of the current grid is set to empty, that is, 0, indicating that the current area belongs to the non-waterlogged area. For example, if the flooded data of the current grid is less than 0.1m, the flooded data of the current grid is modified to 0.

[0147] S402: After the traversal of each grid in the grid file is completed, the grid file after the traversal is completed is used as a flooded data file.

[0148] Optionally, after traversing each grid and the value of each grid in the raster file, a raster file is obtained in which all the values ​​of each grid less than a preset threshold are set to a preset invalid value, and the raster file at the end of the traversal is used as a flooded data file. That is, the obtained flooded data file is a file with valid flooded data. By deleting the values ​​less than the preset threshold in the raster file, that is, deleting unnecessary values ​​in the raster file, the raster file is simplified, the size of the raster file is reduced, and the memory occupied by storing the raster file in the front-end device is reduced.

[0149] Optionally, the obtained flooding data file is a tif file. As mentioned above, the flooding data file is generated every certain period of time, and the flooding data file includes the flooding data of each grid at each moment, and the flooding data of each grid at each moment is stored in the form of a tif image file.

[0150] Among them, TIFF (Tag Image File Format) image file is one of the commonly used formats in graphic image processing. Its image format is very complex, but it is widely used because it can store image information flexibly and variably, supports many color systems, and is independent of the operating system. In various geographic information systems, photogrammetry and remote sensing applications, images are required to have geographic coding information, such as the coordinate system where the image is located, scale, coordinates of points on the image, longitude and latitude, length units and angle units, etc. Therefore, tif image files can be used to display flood inundation areas.

[0151] S403, slicing the drainage network flow data to obtain a plurality of sliced ​​data, and storing each sliced ​​data in a network flow data file.

[0152] A pipe network flow data file stores flow data of one or more drainage pipe networks.

[0153] Optionally, when a city flood occurs, not only the street pavement will be under great flood pressure, but the city's underground drainage system will also be under drainage pressure. The numerical simulation program merged into the backend can be used to calculate the water flow of each pipe network. For example, in the scenario to be predicted, the dynamic flow rate of pipe A is the dynamic flow rate of pipe B. The dynamic flow rate and dynamic flow rate of each pipe will be integrated in a data table to obtain the drainage pipe network flow data in csv format.

[0154] Optionally, the drainage network flow data can be sliced ​​according to the name of each pipeline, and the flow and velocity data of each pipeline will be segmented out. The flow and velocity of each pipeline obtained after segmentation are stored in the network flow data file.

[0155] At the same time, the segmented data is used to create a network script command with swagger as the data interface. Swagger specifies the API and JSON format requirements for writing data. In this way, batch modification and writing of data can be completed quickly and automatically, greatly improving the efficiency of writing data required for the early warning platform.

[0156] Among them, Swagger is a functional framework for online automatic document generation and functional testing of RESTFUL interfaces. Swagger is a standardized and complete framework for generating, describing, calling and visualizing RESTful-style services. It allows the client and the file system to be updated at the same speed as the server. For back-end development users: the framework has low code intrusion, adopts a full annotation method, and is simple to develop. In the process of test development, the modification of back-end system method parameter names, addition, and reduction of parameters can take effect directly without manual maintenance. For front-end development users: the back-end only needs to define the interface, and the document will be automatically generated, and the interface functions and parameters are clear at a glance. The front-end and back-end joint debugging is convenient. If there is a problem, the parameters can be checked in real time through the test interface, and the test return value can be obtained, so that the problem can be quickly located whether it is a problem in the front-end or the back-end. For test users: for those without a front-end UI, Swagger can be used to test the interface. The operation is simple and can be operated without understanding the specific code.

[0157] Figure 5A schematic diagram of the fifth flood disaster digital twin processing method provided in the embodiment of the present application is shown in FIG. Figure 5 As shown, the method may also include:

[0158] S501: Acquire the identifier of the scene to be displayed and the flooding display instruction input by the user.

[0159] Among them, the flooding display instruction is used to instruct the display of flooding data on the front-end interface.

[0160] Optionally, the user can input a scene to be displayed on the front-end interface, and the scene to be displayed can be any scene pre-stored in the target database. Figure 6 As shown, users can Figure 6 Enter the identifier of the scene to be displayed in the input box on the right, such as Typhoon Type - Mangkhut Scene.

[0161] S502: Query the target database for directory information of the prediction result file corresponding to the scene to be displayed according to the identifier of the scene to be displayed.

[0162] Specifically, as can be seen from the foregoing, after obtaining each prediction result file, the directory information storage of each prediction result file is bound together with the identifier of the scene to be predicted and stored in the target database. Then, the directory information of the prediction result file corresponding to the scene to be displayed can be queried from the target database according to the identifier of the scene to be displayed.

[0163] S503 . Read and display the urban inundation data in the flood prediction result of the scene to be displayed from the front-end device according to the directory information of the inundation data file in the prediction result file corresponding to the scene to be displayed.

[0164] Optionally, it can be known from the above that the flooding data file is stored in the front-end device, that is, after obtaining the directory information of the flooding data file in the prediction result file, the flooding data file can be read from the front-end device. Specifically, the flooding data file can be read in chronological order, and the read flooding data files at each time are displayed in the front-end interface, such as Figure 6 As shown, Figure 6 It is the flooding data of each area of ​​the city under the scenario to be displayed at a certain moment.

[0165] In this embodiment, after the user selects a scene to be displayed, the directory information of the prediction result file corresponding to the scene to be displayed can be automatically found from the target database according to the identifier of the selected scene to be displayed, so that the urban inundation data under the scene to be displayed can be obtained according to the found directory information, that is, the urban inundation data can be obtained according to the identifier of the scene to be displayed, which can improve the convenience and efficiency of data acquisition, thereby improving the efficiency of data display.

[0166] Figure 7 This is a flowchart of the sixth flood disaster digital twin processing method improved by the embodiment of the present application, such as Figure 7 As shown, the process of generating the flood data file may include:

[0167] S601. According to the area in the city to which each grid in the flood data file belongs, the area is divided into grids to obtain a grid set corresponding to each area in the city.

[0168] Optionally, for example, the Shenzhen area includes multiple areas, such as Baoan District, Futian District, Longgang District, etc. Each area can be divided into multiple grids, and after each area is grid-divided, a grid set corresponding to each area is obtained. For example, grid-dividing Baoan District obtains grid set 1 of Baoan District, grid-dividing Futian District obtains grid set 2, and grid-dividing Longgang District obtains grid set 3.

[0169] S602: Determine the flooded area in each region, the flooded depth and the flooded area of ​​each flooded area according to the position, size, adjacent relationship and value of each grid in the grid set corresponding to the region.

[0170] For example, the flooded area of ​​Baoan District, the flooded depth and the flooded area of ​​each flooded area can be determined based on the value of each grid in Baoan District's grid set 1, the position, size and adjacent relationship of each grid. The flooded area of ​​Futian District, the flooded depth and the flooded area of ​​each flooded area can be determined based on the value of each grid in Futian District's grid set 2, the position, size and adjacent relationship of each grid. The flooded area of ​​Longgang District, the flooded depth and the flooded area of ​​each flooded area can be determined based on the value of each grid in Longgang District's grid set 3, the position, size and adjacent relationship of each grid. Figure 8 As shown, the map images of Baoan District, Futian District, Longgang District, etc. can be obtained. Figure 8 A schematic diagram of a display interface for flooding data of various regions provided in an embodiment of the present application.

[0171] Specifically, for each grid in a region, the region where each grid is located corresponds to a sub-region of the region. If the value of the grid is not zero, it means that the sub-region where the grid is located is a flooded region; if the value of the grid is zero, it means that the sub-region where the grid is located is not a flooded region.

[0172] S603: According to the flooding depth and flooding area of ​​the flooded area, a flooding image is superimposed and displayed on the flooded area on the map, and the flooding image is used as a flooding data file.

[0173] The obtained flood data file is an image file that has been converted into a tif format. In order to compress the image file size and facilitate fast reading by the front-end interface, the flood data file needs to be further compressed and converted. The flood data file can be converted into a binary file. Specifically, the triangulation positioning method can be used to determine the position of each grid.

[0174] Optionally, the above S603, displaying a flooded image overlaid on the flooded area on the map according to the flooded depth and flooded area of ​​the flooded area, may include:

[0175] Specifically, the display style of the flooded image can be determined according to the flooded depth and the flooded area. The display style includes the display color. The flooded image is superimposed on the flooded area on the map according to the display style, wherein the depth of the display color is positively correlated with the flooded depth. The values ​​of all the grids in the area can be collected, and the values ​​of all the grids can be sorted according to size to obtain the sorting result, and the colors are assigned according to the sorting result. The larger the value of the grid, that is, the deeper the water in the grid, the darker the color assigned to the grid. For example, the values ​​of each grid in the raster set of Futian District are sorted to obtain the sorting result of Futian District, and the colors are assigned according to the sorting result, as shown below. Figure 8 Flooded image of Zhongfutian District.

[0176] Optionally, in the Web front end, React or other suitable frameworks can be used to create a user interface, and a timeline component can be set in the front end interface, which allows the user to control the time progression of the flood data file display, that is, to control the display of the flood data file of the city at different times in the front end interface. Figure 8 As shown in the image of Luohu District in the figure, as time goes by on the timeline component of Luohu District, the color of Futian District gradually changes from light purple to dark blue. This means that the water in the Futian area is getting deeper and deeper, and then slowly weakening. The flooding data of the area at different times can be displayed on the front-end interface through the timeline component. Such a dynamic visualization process is achieved by continuously reading the flooding data file on the web front-end. By setting the timeline, the front-end interface calls the static resource file through the framework to which react belongs, that is, the binary file converted before. According to the timeline, the flooding data file is read into the cache in sequence, and the images in the flooding data file will be displayed in chronological order on the front-end interface. A corresponding image of the area can be read at one time, thus realizing a simple dynamic simulation.

[0177] Optionally, the method may further include:

[0178] Optionally, an identifier of a scene to be displayed and a pipe network flow display instruction input by a user are obtained.

[0179] The pipe network flow display instruction is used to instruct the front-end interface to display the pipe network flow data of the city in the scene to be displayed. The user can enter the scene to be displayed on the front-end interface, and the scene to be displayed can be any scene pre-stored in the target database. The user can enter the scene to be displayed in the front-end interface. Fig. 9 Enter the identifier of the scene to be displayed in the input box on the right, such as Typhoon Type - Hato scene.

[0180] Optionally, the directory information of the prediction result file corresponding to the scene to be displayed can be queried from the target database according to the identifier of the scene to be displayed. And according to the directory information of the submerged data file in the prediction result file corresponding to the scene to be displayed, the drainage network flow data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed. The drainage network flow data of the scene to be displayed is as follows: Fig. 9 shown.

[0181] like Fig. 9 As shown in the figure, the structure of the pipeline network is based on points. The starting point and end point of each pipeline have a geographic coordinate. The starting point and end point of each pipeline can be obtained through the geographic coordinates of the starting point and the geographic coordinates of the end point of each pipeline. The line obtained by connecting the starting point and the end point of the pipeline on the display interface is used as the pipeline.

[0182] It is worth noting that Fig. 9 The perspective of the entire map is presented from an underground perspective. When connecting the start point and the end point, you need to pay attention to the parameters of the pipeline. Pipes can include different types of pipelines, such as drainage pipes, rainwater pipes, and culverts. Different colors and shapes can be used to display different types of pipelines, such as Fig. 9 The rainwater pipes in the map can be represented by green pipes, the drainage pipes can be represented by white pipes, and the culverts can be represented by red pipes. At the same time, the size of the pipes can be scaled proportionally on the map according to the parameters of each pipe. Through the above process, a city's underground pipe network can be clearly built.

[0183] Optionally, each section of the pipeline will have parameter information for each pipeline. The user can select any pipeline in any area and click to view the parameter information of the selected pipeline, such as Fig. 9 The pipeline parameters & information interface in the pipeline includes the selected pipeline's representation, size, start point, end point, section type and other parameters. It can also display the pipeline's flow data at different times in the form of a bar graph. As time goes by, the water depth will change with time and the flood process. At the same time, the flow and flow rate data in the pipeline will be displayed simultaneously in the form of a dynamic chart.

[0184] Optionally, the user can also Figure 6In the interface for displaying flood data files, select an observation station in the area of ​​the city to display the observation data of the selected observation station. For example, a dynamic bar chart can be used for rainfall measurement stations such as Fig.10 The data from the rainwater stations in the wind speed measurement station can be used with dynamic curves such as Fig.10 The data of the wind speed station in the figure shows that the atmospheric pressure station and the tide station can also use dynamic curve graphs such as Fig.10 The data of the pressure station and the data of the tide station are displayed.

[0185] Fig.11 A flow chart of the seventh flood disaster digital twin processing method provided in the embodiment of the present application is as follows: Fig.11 As shown, the method may also include:

[0186] S701: Obtain urban inundation data in the flood prediction results corresponding to the scene to be compared and the measured water depth data of the scene to be compared.

[0187] The scene to be compared refers to a scene with measured water depth data and urban flooding data predicted by a flood simulation model. The front-end device stores both the measured water depth data of the water depth station in the scene to be compared and the predicted urban flooding data in the scene to be compared.

[0188] S702: Compare the urban inundation data with the measured water depth data, and display the comparison result.

[0189] Specifically, the measured water depth data obtained by the water depth station at each time can be compared with the predicted urban flooding data at each time to obtain a comparison result. The comparison result is shown as Fig.10 The display interface of the water Shenzhen (with comparison) in Fig.10 The blue curve in the display interface (with comparison) is the predicted urban flooding data, and the red line is the measured water depth data.

[0190] Optionally, Fig.10 The display interface of the water depth station is a comparison of urban inundation data without prediction, that is, only the actual water depth data measured by the water depth station.

[0191] The two different curves can provide a clear comparison, thereby further verifying the accuracy of the flood simulation model predictions.

[0192] Optionally, when the front-end device stores the flood prediction result file, all data can be saved in memory through the Redis database component, and a special design is used to provide low-latency read and write performance. As an in-memory database, Redis can provide low-latency, high-throughput cache services for data reading and calculation, and provide a stable framework and data services. Redis can support different types of abstract data structures such as strings, lists, mappings, sets, ordered sets, bitmaps, data streams, and spatial indexes. Therefore, using Redis as the core database and real-time computing engine can meet various complex data processing requirements.

[0193] Fig.12 A schematic diagram of a flood disaster digital twin processing method provided in an embodiment of the present application is shown in FIG. Fig.12 As shown, the device comprises:

[0194] The acquisition module 801 is used to acquire original data corresponding to the scene to be predicted from the target database, wherein the original data includes at least one of the following: urban terrain data, urban infrastructure data, and meteorological data;

[0195] The acquisition module 801 is used to acquire configuration information corresponding to the scene to be predicted, and the configuration information includes at least one of the following: boundary conditions and Manning coefficients, wherein the boundary conditions are used to adjust rainfall data and perform tide level simulation, and the Manning coefficient is used to indicate water flow resistance;

[0196] A prediction module 802 is used to predict the flood prediction result of the scene to be predicted using a flood simulation model according to the original data and the configuration information, wherein the flood prediction result includes: urban inundation data and drainage network flow data, and the urban inundation information is used to indicate the inundation height of each area in the city;

[0197] The storage module 803 is used to store the flood prediction results.

[0198] Optionally, the prediction module 802 is specifically used for:

[0199] Determining non-floodable areas in the city based on the urban infrastructure data;

[0200] Creating a mask for the non-floodable area in the urban infrastructure data to obtain new original data;

[0201] The new raw data and the configuration information are input into the flood simulation model to predict the flood prediction result of the scene to be predicted.

[0202] Optionally, the storage module 803 is specifically used for:

[0203] Simplifying the flood prediction results to obtain a plurality of prediction result files, wherein the plurality of prediction result files include: a flood data file and at least one pipe network flow data file;

[0204] Storing the plurality of prediction result files in a front-end device;

[0205] Obtaining directory information of each prediction result file;

[0206] The directory information of each prediction result file is stored in the target database and bound to the identifier of the scene to be predicted.

[0207] Optionally, the urban inundation data is stored in the form of a raster file, and the drainage network flow data is stored in the form of comma separated values;

[0208] The storage module 803 is specifically used for:

[0209] Traversing each grid in the grid file, for the current grid traversed, if the value of the current grid is less than a preset threshold, modifying the value of the current grid to a preset invalid value;

[0210] After the traversal of each grid in the grid file is completed, the grid file at the end of the traversal is used as the flooding data file;

[0211] The drainage network flow data is sliced ​​to obtain a plurality of sliced ​​data, and each sliced ​​data is stored in a network flow data file, wherein one network flow data file is used to store flow data of one or more drainage networks.

[0212] Optionally, the display module 804 is used to:

[0213] Obtaining the identifier of the scene to be displayed and the flood display instruction input by the user;

[0214] According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed;

[0215] According to the directory information of the flood data file in the prediction result file corresponding to the scene to be displayed, the urban flood data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

[0216] Optionally, the display module 804 is specifically used for:

[0217] According to the area in the city to which each grid in the flood data file belongs, the area is divided into grids to obtain a grid set corresponding to each area in the city;

[0218] Determine the flooded area in each area, the flooded depth of each flooded area, and the flooded area according to the position, size, adjacent relationship, and value of each grid in the grid set corresponding to the area;

[0219] According to the flooding depth and the flooding area of ​​the flooded area, a flooding image is superimposed and displayed on the flooded area on the map, and the flooding image is used as a flooding data file.

[0220] Optionally, the display module 804 is specifically used for:

[0221] Determining a display style of the flooded image according to the flooded depth and the flooded area, the display style comprising: display color;

[0222] A flooded image is displayed superimposed on the flooded area on the map according to the display style, and the depth of the display color is positively correlated with the flooded depth.

[0223] Optionally, the display module 804 is specifically used for:

[0224] Obtaining the identifier of the scene to be displayed and the pipe network flow display instruction input by the user;

[0225] According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed;

[0226] According to the directory information of each of the pipe network flow data files in the prediction result file corresponding to the scene to be displayed, the drainage pipe network flow data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

[0227] Optionally, the display module 804 is specifically used for:

[0228] Obtaining urban inundation data in the flood prediction results corresponding to the scene to be compared and the measured water depth data of the scene to be compared;

[0229] The urban inundation data is compared with the measured water depth data, and the comparison result is displayed.

[0230] Fig.13 FIG. 9 is a structural block diagram of an electronic device 900 provided in an embodiment of the present application. Fig.13 As shown, the electronic device may include: a processor 901 and a memory 902 .

[0231] Optionally, a bus 903 may also be included, wherein the memory 902 is used to store machine-readable instructions executable by the processor 901. When the electronic device 900 is running, the processor 901 communicates with the memory 902 through the bus 903. When the machine-readable instructions are executed by the processor 901, the method steps in the above method embodiment are performed.

[0232] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method steps in the above-mentioned flood disaster digital twin processing method embodiment are executed.

[0233] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0234] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program code.

[0235] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A flood disaster digital twin processing method, characterized in that: Applied to a flood disaster digital twin platform, the method includes: Acquire original data corresponding to the scene to be predicted from the target database, wherein the original data includes at least one of the following: urban terrain data, urban infrastructure data, and meteorological data; Acquire configuration information corresponding to the scenario to be predicted, the configuration information including at least one of the following: boundary conditions and Manning coefficients, wherein the boundary conditions are used to adjust rainfall data and perform tide level simulation, and the Manning coefficient is used to indicate water flow resistance; According to the original data and the configuration information, a flood simulation model is used to predict and obtain a flood prediction result of the scene to be predicted, wherein the flood prediction result includes: urban inundation data and drainage network flow data, and the urban inundation information is used to indicate the inundation height of each area in the city; The flood prediction result is stored.

2. The flood disaster digital twin processing method according to claim 1 is characterized in that: The method of using a flood simulation model to predict, based on the original data and the configuration information, obtaining a flood prediction result of the scene to be predicted includes: Determining non-floodable areas in the city based on the urban infrastructure data; Creating a mask for the non-floodable area in the urban infrastructure data to obtain new original data; The new raw data and the configuration information are input into the flood simulation model to predict the flood prediction result of the scene to be predicted.

3. The flood disaster digital twin processing method according to claim 1 is characterized in that: The storing of the flood prediction result comprises: Simplifying the flood prediction results to obtain a plurality of prediction result files, wherein the plurality of prediction result files include: a flood data file and at least one pipe network flow data file; Storing the plurality of prediction result files in a front-end device; Obtaining directory information of each prediction result file; The directory information of each prediction result file is stored in the target database and bound to the identifier of the scene to be predicted.

4. The flood disaster digital twin processing method according to claim 3 is characterized in that: The urban flooding data is stored in the form of a raster file, and the drainage network flow data is stored in the form of a comma separated value; The flood prediction results are simplified to obtain multiple prediction result files, including: Traversing each grid in the grid file, for the current grid traversed, if the value of the current grid is less than a preset threshold, modifying the value of the current grid to a preset invalid value; After the traversal of each grid in the grid file is completed, the grid file at the end of the traversal is used as the flooding data file; The drainage network flow data is sliced ​​to obtain a plurality of sliced ​​data, and each sliced ​​data is stored in a network flow data file, wherein one network flow data file is used to store flow data of one or more drainage networks.

5. The flood disaster digital twin processing method according to claim 3 is characterized in that: The method further comprises: Obtaining the identifier of the scene to be displayed and the flood display instruction input by the user; According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed; According to the directory information of the flood data file in the prediction result file corresponding to the scene to be displayed, the urban flood data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

6. The flood disaster digital twin processing method according to claim 5 is characterized in that: The process of generating the flooding data file includes: According to the area in the city to which each grid in the flood data file belongs, the area is divided into grids to obtain a grid set corresponding to each area in the city; Determine the flooded area in each region, the flooded depth of each flooded area, and the flooded area according to the position, size, adjacent relationship, and value of each grid in the grid set corresponding to the region; According to the flooding depth and flooding area of ​​the flooding area, a flooding image is superimposed and displayed on the flooding area on the map, and the flooding image is used as a flooding data file.

7. The flood disaster digital twin processing method according to claim 6 is characterized in that: The method of displaying a flood image overlaid on the flooded area on the map according to the flooded depth and the flooded area of ​​the flooded area includes: Determining a display style of the flooded image according to the flooded depth and the flooded area, the display style comprising: display color; A flooded image is displayed superimposed on the flooded area on the map according to the display style, and the depth of the display color is positively correlated with the flooded depth.

8. The flood disaster digital twin processing method according to claim 3 is characterized in that: The method further comprises: Obtaining the identifier of the scene to be displayed and the pipe network flow display instruction input by the user; According to the identifier of the scene to be displayed, querying the target database for directory information of the prediction result file corresponding to the scene to be displayed; According to the directory information of each of the pipe network flow data files in the prediction result file corresponding to the scene to be displayed, the drainage pipe network flow data in the flood prediction result of the scene to be displayed is read from the front-end device and displayed.

9. The flood disaster digital twin processing method according to any one of claims 1 to 8, characterized in that: Also includes: Obtaining urban inundation data in the flood prediction results corresponding to the scene to be compared and the measured water depth data of the scene to be compared; The urban inundation data is compared with the measured water depth data, and the comparison result is displayed.

10. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program executable by the processor, and the processor implements the steps of the flood disaster digital twin processing method described in any one of claims 1 to 9 when executing the computer program.

Images