Flood simulation method and device based on urban infrastructure

Abstract

The invention discloses a flood simulation method and device based on urban infrastructure, and the method comprises the following steps: collecting streetscape images of a city, the streetscape images comprising streetscape data collected from different urban infrastructures at different angles and in different time; performing classification processing on the streetscape data according to the types of the infrastructures, and extracting the infrastructure data of the city; fusing the infrastructure data and the topographic data of the city to obtain fused topographic data of the city; the method comprises the following steps: dividing water collecting areas of cities based on fused topographic data, extracting water system data, establishing a flood simulation model based on urban infrastructures according to the water system data, fully considering the influence of infrastructures such as urban street networks, wall systems and buildings on the urban flood process, and improving the simulation precision of the urban rainstorm flood model. And urban flood disasters are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of urban flood simulation, in particular to a flood simulation method and device based on urban infrastructure. Background Art

[0002] Urban infrastructure significantly influences urban flooding by altering flood flow paths and velocities. Urban infrastructure directly impacting flood processes includes street networks, wall systems, and buildings. With global climate change leading to more frequent extreme weather events, urban flooding is becoming increasingly serious, particularly in high-density urban areas. Urban flood disasters are characterized by sudden onset, high peak volumes, and widespread impact. In recent years, floods have occurred frequently in many cities, causing significant losses. However, current urban flood simulations fail to consider the impact of urban street networks, wall systems, and buildings on flood processes, resulting in irrational flood simulation results and adversely affecting subsequent urban flood risk assessments. Summary of the Invention

[0003] To solve the above problems, the purpose of the present invention is to provide a flood simulation method, device and storage medium based on urban infrastructure, so as to improve the simulation accuracy of urban rainstorm flood models and reduce urban flood disasters.

[0004] The technical solution adopted by the present invention to solve the problem is:

[0005] In a first aspect, an embodiment of the present application provides a flood simulation method based on urban infrastructure, the method comprising: collecting street view images of a city, wherein the street view images include street view data collected from infrastructure of different cities at different angles and at different times; classifying the street view data according to the type of the infrastructure to extract infrastructure data of the city; fusing the infrastructure data with the terrain data of the city to obtain fused terrain data of the city; dividing the watershed area of ​​the city based on the fused terrain data and extracting water system data, and establishing a flood simulation model based on urban infrastructure based on the water system data.

[0006] In a second aspect, an embodiment of the present application provides a flood simulation device based on urban infrastructure, including: an acquisition module for acquiring street view images of a city, wherein the street view images include street view data acquired from infrastructure of different cities at different angles and at different times; a classification module for classifying and processing the street view data according to the type of the infrastructure, and extracting the infrastructure data of the city; a fusion module for fusing the infrastructure data with the terrain data of the city to obtain fused terrain data of the city; a simulation module for dividing the watershed area of ​​the city based on the fused terrain data and extracting water system data, and establishing a flood simulation model based on urban infrastructure according to the water system data.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the urban infrastructure-based flood simulation method as described above is implemented.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the urban infrastructure-based flood simulation method as described above is implemented.

[0009] In an embodiment of the present application, street view images of a city are collected, wherein the street view images include street view data obtained from infrastructure of different cities at different angles and at different times; the street view data are classified and processed according to the type of infrastructure to extract the city's infrastructure data; the infrastructure data and the city's terrain data are integrated to obtain integrated terrain data of the city; the city's watershed is divided based on the integrated terrain data and water system data is extracted; a flood simulation model based on the urban infrastructure is established based on the water system data, fully considering the impact of infrastructure such as urban street networks, wall systems, and buildings on the urban flood process, thereby improving the simulation accuracy of the urban rainstorm flood model and reducing urban flood disasters.

[0010] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of a flood simulation method based on urban infrastructure according to an embodiment of the present invention;

[0012] Figure 2 for Figure 1 Flowchart of step S1000;

[0013] Figure 3 for Figure 1Flowchart of step S2000;

[0014] Figure 4 for Figure 1 Flowchart of another embodiment of step S2000;

[0015] Figure 5 for Figure 4 Flowchart of step S2500;

[0016] Figure 6 for Figure 1 Flowchart of step S3000;

[0017] Figure 7 for Figure 1 Flowchart of step S4000;

[0018] Figure 8 This is a structural diagram of a flood simulation device based on urban infrastructure according to an embodiment of the present invention;

[0019] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0021] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0022] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0023] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0024] The embodiments of the present invention involve a flood simulation method and device based on urban infrastructure, and a storage medium thereof. The method collects street view images of a city, wherein the street view images include street view data collected from infrastructure of different cities at different angles and at different times; classifies the street view data according to the type of infrastructure to extract the city's infrastructure data; fuses the infrastructure data with the city's terrain data to obtain fused terrain data of the city; divides the city's watershed based on the fused terrain data and extracts water system data; establishes a flood simulation model based on the urban infrastructure based on the water system data, fully considering the impact of infrastructure such as urban street networks, wall systems, and buildings on the urban flood process, improves the simulation accuracy of the urban rainstorm flood model, and reduces urban flood disasters.

[0025] With the increase in extreme rainfall events caused by climate change, urban flooding is characterized by sudden onset, high peak volumes, and widespread impact. In recent years, floods have occurred frequently in many cities, causing severe losses. Currently, urban flood simulation methods can be categorized into hydrological, hydrodynamic, and coupled hydrological and hydrodynamic methods. Hydrological methods, the earliest adopted, calculate runoff generation and confluence based on hydrological principles. While simple and efficient, they cannot account for the effects of urban buildings on water levels or flow patterns near drainage network inlets. Hydrodynamic methods, based on grid cells, use the one-dimensional Saint-Venant equations and the two-dimensional shallow water equations to calculate surface flow, confluence, and flow in drainage networks and rivers. While these methods offer high accuracy, they are inefficient and, particularly for large-scale urban flood prediction, consume excessive computational time.

[0026] However, in the current urban flood simulation process, the impact of the city's street network, wall system, buildings and other facilities on the flood process is not taken into account, which makes the results of flood simulation unreasonable and has an adverse impact on subsequent urban flood risk assessment.

[0027] Based on the above, embodiments of the present invention provide a flood simulation method and device based on urban infrastructure, and a storage medium thereof. The method collects street view images of a city, wherein the street view images include street view data collected from infrastructure of different cities at different angles and at different times; classifies the street view data according to the type of infrastructure to extract the city's infrastructure data; fuses the infrastructure data with the city's terrain data to obtain fused terrain data of the city; divides the city's watershed based on the fused terrain data and extracts water system data; establishes a flood simulation model based on the urban infrastructure based on the water system data, fully considering the impact of infrastructure such as urban street networks, wall systems, and buildings on the urban flood process, thereby improving the simulation accuracy of the urban rainstorm flood model and reducing urban flood disasters.

[0028] See Figure 1 , Figure 1 The flowchart of the flood simulation method based on urban infrastructure provided by an embodiment of the present invention is shown.

[0029] like Figure 1 As shown, the flood simulation method based on urban infrastructure according to an embodiment of the present invention includes the following steps:

[0030] Step S1000: Collect street view images of a city, wherein the street view images include street view data collected from infrastructure of different cities at different angles and at different times.

[0031] Understandably, existing urban flood simulation methods are designed for urban drainage infrastructure data management, meeting the data requirements for hydrological, hydraulic, and water quality simulations. By storing relevant data in a database, they support simulations of both separate and combined sewer systems, encompassing surface hydrology, pipe network hydraulics, and water quality processes. Furthermore, these models can simulate the hydrological, hydraulic, and water quality processes of urban low-impact development measures and implement coupled one- and two-dimensional simulations of urban waterlogging, providing detailed results such as inundation depth, flow direction, and velocity. Currently, urban flood simulation methods fail to comprehensively account for the impact of infrastructure such as urban street networks, wall systems, and buildings on flood processes, significantly impacting the accuracy and relevance of urban flood simulations. Therefore, to accurately capture the specific parameters of urban infrastructure, such as street networks, wall systems, and buildings, and their impact on flood simulations, it is necessary to collect street view imagery to update and capture the distribution and dimensions of urban infrastructure in real time, enabling more accurate urban flood simulations.

[0032] Understandably, traditional maps are geographic query tools that record symbols such as lines, points, and text. More accurate maps rely on real-world satellite images to depict geographic locations, but these are only one-dimensional. A three-dimensional, real-world visual experience would be even more immersive. Currently, Street View on major platforms achieves this effect, a truly revolutionary change in traditional map thinking. It's incredibly innovative, offering a 360-degree panoramic view of streetscapes, giving the illusion of walking down the street. Therefore, Street View is a real-world mapping service. It provides users with 360-degree panoramic images of cities, streets, or other environments, enabling an immersive map browsing experience. Through Street View imagery, users can visualize the streets in high definition, directly through a terminal, and obtain detailed information about the city's internal structure and infrastructure.

[0033] See Figure 2 , Figure 2 FIG. 5 is a schematic diagram showing a specific implementation process of another embodiment of the above step S1000. Figure 2As shown, step S1000 at least includes the following steps:

[0034] Step S1100: Acquire coordinate information of a street view image in a city's coordinate picking platform.

[0035] It should be noted that in order to fully and accurately capture street view images of the current city, the coordinate information of the current city must first be obtained to quickly locate the current city on the coordinate picking platform and avoid measurement errors caused by position offset. Coordinates are an ordered array used to accurately describe the position of a point in space. Their core function is to transform abstract spatial locations into quantifiable and computable data through mathematical expression. In practical applications, the application of precise positioning is based on the coordinate system's ability to transform complex spatial relationships into actionable data. Its value lies not only in describing location, but also in providing a common language for quantitative analysis in various fields.

[0036] It is understood that mainstream coordinate extraction platforms, including Amap, Baidu Maps, Tencent Maps, and Google Maps, use different coordinate systems and have different functional focuses. In practical applications, extracting the coordinate information required in the embodiments of this application from street view images collected using Baidu Street View Maps in 3D street view mode is a prior art technique and will not be elaborated upon here.

[0037] Step S1200: input coordinate information, and set the collection interval, collection angle and collection interval of street view data to obtain the collection point matrix of street view data.

[0038] It's understandable that in order to more accurately and comprehensively acquire street view data for the city corresponding to the coordinate information, it's necessary to set collection intervals, angles, and intervals. This allows for the collection of street view data for cities at different time intervals, angles, and ranges, minimizing errors in urban flood simulations caused by street view data collection errors and blind spots. In practical applications, Python-based data collection methods are primarily used for street view data collection and processing. This is because Python provides efficient high-level data structures and allows for simple and effective object-oriented programming. Python's syntax, dynamic typing, and interpreted nature make it a popular programming language for scripting and rapid application development on most platforms. The coordinate information for the desired street view data range is obtained from the Baidu Coordinate Capture Platform. The data coordinates are then entered into the Python platform. The interval, angle, and interval for collecting street view points are then set. Data collection points are then arranged within the street view data collection range.

[0039] It should be noted that a collection dot matrix is ​​a graph with a specific and well-defined structure. An N×N dot matrix is ​​a two-dimensional grid with N edges on both the X and Y axes. In practical applications, regardless of whether the grid is folded or not, the grid corresponding to the dot matrix is ​​isomorphic, meaning that the elements on the dot matrix have a one-to-one correspondence. Therefore, what is important about a dot matrix is ​​not how it is presented on a two-dimensional plane, but rather how the elements within it are connected. In practical applications, dot matrix digital datasets offer a simple, rich, and widely applicable data representation method. These dot matrix digital datasets primarily represent numbers, characters, or graphics in a grid format, making them easy for computers to process and store. The appeal of dot matrices lies not only in their form but also in the infinite possibilities they hold. Specifically, dot matrix numbers are like tiny tiles; when arranged according to certain rules, they form complete information. A dot matrix digital dataset is simply an image composed of many pixels. Each pixel represents a portion of a number or character. Through the arrangement and combination of these pixels, computers can recognize various shapes, patterns, and even text. The core value of the point matrix digital dataset lies in its efficiency and universality. Many classic computer vision tasks, such as handwritten digit recognition, text recognition, and image classification, require training with the point matrix digital dataset. It is understandable that converting street view data into collected point matrices is an existing technology and will not be discussed in detail here.

[0040] Step S1300: link the collected point matrix to the corresponding folder and store the street view data.

[0041] It will be appreciated that, when acquiring the corresponding acquisition point matrix for street view data, the street view data needs to be linked to and stored in corresponding folders to facilitate computer learning, calculation, and correction. As can be seen from the above steps, street view data is acquired at different acquisition intervals, acquisition angles, and acquisition intervals. Therefore, to ensure the integrity and traceability of data preservation, the street view data needs to be stored in different folders. In practical applications, the acquisition point matrix needs to be linked to corresponding folders and the street view data needs to be stored. In some embodiments, street view data can be collected and stored separately according to street view images and image parameter data, allowing for rapid and efficient access to the street view data required for flood simulation, thereby improving the timeliness and accuracy of urban flood simulation.

[0042] Step S2000: Classify the street view data according to the type of infrastructure and extract the city's infrastructure data.

[0043] It is understandable that the infrastructure that affects urban flood simulation includes but is not limited to street networks, wall systems, and buildings. In order to scientifically and accurately analyze the impact of urban infrastructure on flood simulation, it is necessary to classify and process street view data according to the type of infrastructure to extract the impact coefficients of different infrastructure on flood simulation in different dimensions and ranges, that is, the city's infrastructure data.

[0044] See Figure 3 , Figure 3 FIG. 5 is a schematic diagram showing a specific implementation process of another embodiment of the above step S2000. Figure 3 As shown, step S2000 includes at least the following steps:

[0045] Step S2100: Determine the type of infrastructure based on the street view image.

[0046] It will be appreciated that the street view images acquired through the above steps include urban infrastructure such as street networks, wall systems, and buildings. Therefore, the types of infrastructure can be determined by automatically identifying and filtering the street view images. In some embodiments, infrastructure can be categorized by lines and surfaces. Specifically, street networks and wall systems can be generalized as line data, while buildings can be extracted as surface data. This accelerates the processing and analysis of street view images and improves the timeliness of urban flood simulation.

[0047] Step S2200: Determine the data type of the infrastructure according to the type of the infrastructure.

[0048] It is understandable that the data type of the infrastructure needs to be determined for different types of infrastructure. In the embodiment described above, the street network and wall system are generalized as line data, and the buildings are extracted as surface data to accurately and quickly reflect the influence of different types of infrastructure in flood simulation and convert them into specific data parameters. This is because the impact of different types of infrastructure, such as walls and residential buildings, varies greatly in the watershed hydrological analysis of urban environments. Therefore, it is necessary to determine the data type of infrastructure based on the type of infrastructure in order to conduct urban flood simulation more scientifically and accurately.

[0049] Step S2300: Generalize the street view data according to the data type to obtain the elevation data of the infrastructure.

[0050] It will be appreciated that after obtaining the data type, detailed settings can be made for the specific parameters of the infrastructure. Specifically, in the above-described embodiment, the street network and wall system are generalized as line data, and buildings are extracted as surface data. Specifically, when the infrastructure is a street network or wall system, the height and length of the infrastructure are extracted to accurately obtain the infrastructure's surface elevation and length information. When the infrastructure is a building such as a residence or shopping mall, the height, width, and length of the infrastructure are extracted to accurately obtain the infrastructure's surface elevation information and occupied area information, thereby accurately obtaining elevation data for different types of infrastructure. Elevation data is the core geographic data that describes surface elevation information and is widely used in fields such as geology, meteorology, and urban planning. Currently, global or regional elevation datasets are available free of charge through various channels, each with its own unique resolution, accuracy, and processing methods. By generalizing street view data based on data type, accurate infrastructure elevation data can be obtained, facilitating more scientific digital analysis of the impact of infrastructure on urban flooding and improving the accuracy and reference value of urban flooding simulation methods.

[0051] It should be noted that a Digital Elevation Model (DEM) is a digital simulation of ground topography using limited terrain elevation data, i.e., a digital representation of the terrain's surface morphology. A DEM is a physical ground model that represents ground elevations in the form of an ordered array of numerical values. It is understood that in order to directly import street view data into a DEM, it must be generalized to improve the accuracy of the DEM.

[0052] See Figure 4 , Figure 4 FIG. 5 is a schematic diagram showing a specific implementation process of another embodiment of the above step S2000. Figure 4 As shown, step S2000 includes at least the following steps:

[0053] Step S2400: Obtain characteristic values ​​of infrastructure in elevation data.

[0054] It is understood that after obtaining infrastructure elevation data, it is necessary to analyze and process the infrastructure's characteristic values ​​in different dimensions to specifically reflect the infrastructure's impact on urban flooding. In some embodiments, the infrastructure's characteristic values ​​in the elevation data are obtained by obtaining technical parameters such as the length, width, and height of the infrastructure that reflect its impact on urban flooding. Of course, in some embodiments, other characteristic values ​​of the infrastructure can also be extracted, such as building shape and material, which are not limited here.

[0055] Step S2500: Determine the calculated value of the infrastructure according to the characteristic value and the preset calculation threshold.

[0056] It is understandable that due to the large number of infrastructures, in order to avoid data redundancy and data processing pressure caused by a large number of eigenvalues ​​with little impact, it is necessary to set a calculation threshold for the eigenvalues ​​to screen the infrastructure, so as to ensure the computational efficiency of the city's infrastructure data and save computing resources. In practical applications, the calculation threshold of the infrastructure included in the calculation is adjusted according to the computing power and accuracy requirements. Specifically, when the infrastructure is a street network, the proportion of roads participating in the urban flood simulation can be set according to the width and level of the road; when the infrastructure is a wall, the proportion of walls participating in the urban flood simulation can be set according to the length; when the infrastructure is a building, the area threshold for participating in the urban flood simulation can be determined according to the building's floor area.

[0057] See Figure 5 , Figure 5 FIG. 5 shows a schematic diagram of a specific implementation process of another embodiment of the above step S2500. Figure 5 As shown, step S2500 at least includes the following steps:

[0058] Step S2510: When the characteristic value is greater than or equal to the calculation threshold, the characteristic value is set to the calculated value of the infrastructure.

[0059] It is understandable that by setting a calculation threshold and comparing the characteristic value with the corresponding calculation threshold, the characteristic value can be screened according to the comparison result. Specifically, when the characteristic value is within the range of the calculation threshold, the characteristic value is set as the calculated value of the infrastructure. In some embodiments, the calculation threshold of the fence is set to 2 meters. Then, when the actual length of the infrastructure is 3 meters, the actual length of the fence is greater than the calculation threshold, and the actual length of the fence is configured as the calculated value of the infrastructure. Of course, in other embodiments, multiple threshold intervals or threshold ranges can be set. When the characteristic value of the infrastructure falls into different threshold intervals and threshold ranges, the final calculated value of the infrastructure is obtained by performing calculations with the corresponding weights.

[0060] Step S2520: When the eigenvalue is less than the calculation threshold, delete the eigenvalue.

[0061] It is understandable that by setting a calculation threshold and comparing the eigenvalue with the corresponding calculation threshold, the eigenvalue can be screened based on the comparison result. Specifically, when the eigenvalue is not within the range of the calculation threshold, the eigenvalue is set to zero, or the eigenvalue of the infrastructure is deleted. In some embodiments, the calculation threshold of the wall is set to 2 meters. Then, when the actual length of the infrastructure is 1 meter, the actual length of the wall is less than the calculation threshold, and the actual length of the wall is configured to zero. Of course, in other embodiments, multiple threshold intervals or threshold ranges can be set. When the eigenvalue of the infrastructure is not within each threshold interval and threshold range, the eigenvalue of the infrastructure is deleted, that is, the impact of the infrastructure in the urban flood simulation is ignored.

[0062] Step S2600: Summarize the calculated values ​​to obtain the city's infrastructure data.

[0063] It can be understood that after obtaining the calculated values ​​of the infrastructure in the above steps, the calculated values ​​of the infrastructure are summarized and processed to obtain the infrastructure data of the city, so as to facilitate the processing and analysis of the calculated values ​​of each infrastructure in subsequent steps, thereby improving the timeliness of urban flood simulation.

[0064] Step S3000: Fusing the infrastructure data and the city's terrain data to obtain fused terrain data of the city.

[0065] It is understandable that after obtaining the infrastructure data in the above steps, it needs to be overlaid with the existing urban terrain data to improve and optimize the urban terrain data so that the influence of the city’s infrastructure in the urban flood simulation process can be fully and accurately reflected in the city’s integrated terrain data.

[0066] See Figure 6 , Figure 6 FIG. 5 is a schematic diagram showing a specific implementation process of another embodiment of the above step S3000. Figure 6 As shown, step S3000 includes at least the following steps:

[0067] Step S3100: Acquire the city's topographic data.

[0068] It is understandable that a city's topographic data can be directly obtained through a DEM. There are various methods for establishing a DEM, depending on the data source and acquisition method. These methods include: direct ground measurement, involving instruments such as horizontal guide rails, styluses, stylus holders, and relative elevation measurement plates. High-end instruments such as GPS, total stations, and field surveying can also be used; acquisition through photogrammetry based on aerial or space imagery, such as stereo coordinate instrument observation and aerial triangulation, analytical mapping, and digital photogrammetry; and acquisition from existing topographic maps, such as grid point reading, handheld 60-degree tracking with a digitizer, and semi-automatic acquisition using a scanner, followed by interpolation to generate a DEM. There are many DEM interpolation methods, primarily global interpolation, block interpolation, and point-by-point interpolation. The global interpolation fitting model is established using the observations of all sampling points within the study area. Block interpolation divides the reference space into several equal-sized blocks, applying different functions to each block. Point-by-point interpolation uses a local function centered on the point to be interpolated to fit the surrounding data points. The range of data points changes as the interpolated location changes, hence the name "moving fit." In practice, directly reading a city's topographic data from an existing DEM is a well-known technique and will not be discussed here.

[0069] Step S3200: superimpose the infrastructure data onto the terrain data according to the type of infrastructure to obtain fused terrain data.

[0070] It's understandable that in practical applications, to ensure accurate and rapid integration of infrastructure data into a city's topographic data, it's necessary to overlay infrastructure data onto the topographic data based on the type of infrastructure. Specifically, if the infrastructure is a wall, it's embedded into the DEM as a linear water barrier; if the infrastructure is a road network, it's integrated into the topography using a cut-and-fill method based on its calculated values; and if the infrastructure is a building, it's embedded into the topography based on its generalization as a planar water barrier. By overlaying different infrastructure data onto the topographic data, a new urban topography is formed that takes urban infrastructure into account. This ensures that the fused topographic data accurately reflects the impact of infrastructure on urban flood simulations, improving the reference value of urban flood simulations.

[0071] Step S4000: Divide the watershed area of ​​the city based on the fused terrain data and extract water system data, and establish a flood simulation model based on the urban infrastructure according to the water system data.

[0072] It can be understood that after obtaining the fused terrain data through the above steps S1000-S3000, a flood simulation model based on urban infrastructure can be established based on the fused terrain data. DEM is a digital elevation model that describes the terrain surface. It can provide important original data for land feature identification and watershed terrain, and contains rich landform, topography and hydrological information. In hydrological information, DEM mainly describes the terrain information of the watershed, including the division of sub-watersheds and the determination of boundaries, the extraction and identification of river networks, the determination of slope direction and slope, etc., thereby providing underlying surface parameters for distributed hydrological simulation. With the continuous development and improvement of DEM watershed feature extraction technology, the accuracy of extracting watershed hydrological characteristics using DEM, especially the extraction accuracy in areas with large terrain undulations, will definitely be improved.

[0073] See Figure 7 , Figure 7 FIG. 4 shows a schematic diagram of a specific implementation process of another embodiment of the above step S4000. Figure 7 As shown, step S4000 at least further includes the following steps:

[0074] Step S4100: Determine the water catchment area of ​​the city based on the fused terrain data.

[0075] It is understandable that in the prior art, the catchment area of ​​a river basin is calculated by analyzing and processing the basin's topographic hydrological index and characteristic parameters. A river basin is a complete, relatively independent natural system on the Earth's surface, serving as the catchment area for rivers and lakes. Water serves as the link, connecting the left and right banks, and upstream and downstream, into an indivisible whole. Similarly, in urban flood simulations, the city's catchment area can be determined by integrating topographic data. Specifically, the data is calculated using hydrological analysis tools. After DEM depression filling, flow direction, and runoff accumulation calculations, repeated trials are performed to determine the catchment area threshold to form the city's catchment area. The catchment area (or basin) refers to the mountain range that separates one river basin from another, or the watershed, and includes the entire area or region that supplies water to a river or lake. However, due to the influence of geology, groundwater, and other factors, it cannot be determined simply by area. Furthermore, it is a complex entity that varies from time to time due to human intervention.

[0076] Step S4200: Determine the city's water system data based on the catchment area and the city's rainfall information.

[0077] It is understood that after obtaining the city's catchment area in the above steps, the city's water system can be extracted by combining it with the city's rainfall information. Precipitation refers to the total amount of rainfall or snowfall within a 12 or 24-hour period. It can also refer to the depth of liquid or solid (melted) water that falls from the sky and accumulates on the horizontal surface without evaporation, infiltration, or loss. Precipitation is measured in millimeters, and meteorological observations are rounded to one decimal place to intuitively indicate the amount of rainfall.

[0078] It can be understood that water system extraction is a method used to extract water system networks from geospatial data. A water system refers to a collection of interconnected water bodies such as rivers, lakes, and reservoirs. Water systems are of great significance in geographical research and environmental protection, so the research and application of water system extraction algorithms are becoming increasingly important. The core idea of ​​water system extraction is to extract the connection between water bodies in geospatial data through a series of calculations and analyses based on the characteristics and topological relationships of geospatial data to form a complete water system network. The geographic data commonly used in water system extraction algorithms include DEM data and water body vector data. By determining the rainfall information of the catchment area and the city, the water system is formed by simulating the flow of surface runoff to determine the water system data of the city.

[0079] Step S4300: Establish a flood simulation model based on urban infrastructure according to the fused terrain data and water system data.

[0080] It can be understood that through the above steps, the city's terrain data and infrastructure data are superimposed to obtain fused terrain data, and the city's water system data is generated based on the DEM, completing the processing of the relevant spatial data for the simulation model. Therefore, establishing an urban flood simulation model based on the fusion of terrain and water system data fully considers the impact of infrastructure such as urban street networks, wall systems, and buildings on urban flood processes, improves the simulation accuracy of urban stormwater models, and reduces urban flood disasters. The establishment of a flood simulation model based on the fusion of terrain data and urban water system data is a prior art and will not be elaborated on here.

[0081] See also Figure 8 , Figure 8 This is a structural diagram of a flood simulation device 500 based on urban infrastructure provided in an embodiment of the present application. The entire process of the flood simulation method based on urban infrastructure provided in an embodiment of the present application involves the following modules in the flood simulation device based on urban infrastructure: an acquisition module 510, a classification module 520, a fusion module 530 and a simulation module 540.

[0082] The acquisition module 510 is used to acquire street view images of a city, wherein the street view images include street view data acquired from infrastructure of different cities at different angles and at different times;

[0083] A classification module 520 is used to classify the street view data according to the type of infrastructure and extract the city's infrastructure data;

[0084] A fusion module 530 is used to fuse the infrastructure data and the city's terrain data to obtain fused terrain data of the city;

[0085] The simulation module 540 is used to divide the water catchment area of ​​the city based on the fused terrain data and extract water system data, and establish a flood simulation model based on the urban infrastructure according to the water system data.

[0086] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned device are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0087] Figure 9 The electronic device 600 provided in an embodiment of the present application is shown. The electronic device 600 includes but is not limited to:

[0088] Memory 601, used for storing programs;

[0089] The processor 602 is configured to execute the program stored in the memory 601 . When the processor 602 executes the program stored in the memory 601 , the processor 602 is configured to execute the above-mentioned flood simulation method based on urban infrastructure.

[0090] The processor 602 and the memory 601 may be connected via a bus or other means.

[0091] Memory 601, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs, such as the urban infrastructure-based flood simulation method described in any embodiment of this application. Processor 602 implements the aforementioned urban infrastructure-based flood simulation method by executing the non-transitory software program and instructions stored in memory 601.

[0092] The memory 601 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store and execute the above-mentioned urban infrastructure-based flood simulation method. In addition, the memory 601 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 601 may optionally include a memory remotely arranged relative to the processor 602, and these remote memories may be connected to the processor 602 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0093] The non-transient software programs and instructions required to implement the above-mentioned urban infrastructure-based flood simulation method are stored in the memory 601. When executed by one or more processors 602, the urban infrastructure-based flood simulation method provided by any embodiment of the present application is executed.

[0094] An embodiment of the present application further provides a storage medium storing computer-executable instructions, which are used to execute the above-mentioned flood simulation method based on urban infrastructure.

[0095] In one embodiment, the storage medium stores computer-executable instructions, which are executed by one or more control processors 602, for example, by a processor 602 in the above-mentioned electronic device 600, so that the above-mentioned one or more processors 602 can execute the flood simulation method based on urban infrastructure provided in any embodiment of the present application.

[0096] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0097] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A flood simulation method based on urban infrastructure, characterized by: The following steps are involved: Collecting street view images of cities, wherein the street view images include street view data collected from infrastructure in different cities at different angles and at different times; Classifying the street view data according to the types of the infrastructure to extract infrastructure data of the city; fusing the infrastructure data with the terrain data of the city to obtain fused terrain data of the city; The water catchment area of ​​the city is divided based on the fused terrain data and water system data is extracted, and a flood simulation model based on urban infrastructure is established according to the water system data.

2. The flood simulation method based on urban infrastructure according to claim 1, characterized in that: The collecting of street view images of the city includes: Acquiring coordinate information of the street view image in a coordinate picking platform of the city; Input the coordinate information, and set the collection interval, collection angle and collection interval of the street view data to obtain the collection point matrix of the street view data; The collected point matrix is ​​linked to a corresponding folder and the street view data is stored.

3. The flood simulation method based on urban infrastructure according to claim 1, characterized in that: The classifying and processing the street view data according to the type of the infrastructure includes: determining the type of the infrastructure according to the street view image; Determining the data type of the infrastructure according to the type of the infrastructure; The street view data is generalized according to the data type to obtain elevation data of the infrastructure.

4. The flood simulation method based on urban infrastructure according to claim 3, characterized in that: The extracted infrastructure data of the city includes: Obtaining characteristic values ​​of the infrastructure in the elevation data; Determining a calculated value of the infrastructure according to the characteristic value and a preset calculation threshold; The calculated values ​​are aggregated to obtain infrastructure data of the city.

5. The flood simulation method based on urban infrastructure according to claim 4, characterized in that: Determining the calculated value of the infrastructure according to the characteristic value and a preset calculation threshold includes: When the characteristic value is greater than or equal to the calculation threshold, setting the characteristic value to the calculated value of the infrastructure; When the eigenvalue is less than the calculation threshold, the eigenvalue is deleted.

6. The flood simulation method based on urban infrastructure according to claim 3, characterized in that: The fusing of the infrastructure data and the terrain data of the city to obtain fused terrain data of the city includes: Obtaining terrain data of the city; According to the type of the infrastructure, the infrastructure data is superimposed on the terrain data to obtain the fused terrain data.

7. The flood simulation method based on urban infrastructure according to claim 6, characterized in that: The method of dividing the water catchment area of ​​the city based on the fused terrain data and extracting water system data, and establishing a flood simulation model based on urban infrastructure according to the water system data, further includes: determining a water catchment area of ​​the city based on the fused terrain data; Determining water system data of the city based on the catchment area and rainfall information of the city; A flood simulation model based on urban infrastructure is established according to the fused terrain data and the water system data.

8. A flood simulation device based on urban infrastructure, characterized in that: include: An acquisition module, configured to acquire street view images of a city, wherein the street view images include street view data acquired from infrastructure of different cities at different angles and at different times; a classification module, configured to classify the street view data according to the type of the infrastructure and extract the infrastructure data of the city; a fusion module, configured to fuse the infrastructure data with the terrain data of the city to obtain fused terrain data of the city; A simulation module is used to divide the water catchment area of ​​the city based on the fused terrain data and extract water system data, and establish a flood simulation model based on urban infrastructure according to the water system data.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for simulating flooding based on urban infrastructure according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the flood simulation method based on urban infrastructure according to any one of claims 1 to 7 is implemented.

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