Urban infrastructure-based flood simulation method and device thereof

Abstract

The application discloses a flood simulation method based on urban infrastructure and a device thereof, and the method comprises the following steps: collecting street view images of a city, wherein the street view images comprise street view data collected from different urban infrastructures at different angles and different times; classifying and processing the street view data according to the types of the infrastructures, and extracting urban infrastructure data; fusing the infrastructure data and terrain data of the city to obtain fused terrain data of the city; dividing catchment areas 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, so that the influence of urban street networks, fence systems, buildings and other infrastructures on urban flood processes is fully considered, the simulation accuracy of a city rainstorm flood model is improved, and urban flood disasters are reduced.

Description

Technical Field

[0001] This invention relates to the field of urban flood simulation technology, and in particular to a flood simulation method and apparatus based on urban infrastructure. Background Technology

[0002] Urban infrastructure significantly impacts urban flood processes by altering flood flow paths and speeds. Urban infrastructure directly influencing flood events includes street networks, perimeter fencing systems, and buildings. With increasing frequency of extreme weather events due to global climate change, urban flooding problems are becoming more severe, especially in densely populated urban areas. Urban flooding disasters are characterized by their sudden onset, high peak volume, and wide affected area. Numerous urban flooding disasters have occurred in recent years, causing severe losses. However, current urban flood simulations fail to consider the impact of urban street networks, perimeter fencing systems, and buildings on flood processes, resulting in unreasonable simulation results and negatively affecting subsequent urban flood risk assessments. Summary of the Invention

[0003] To address the aforementioned problems, the present invention aims to provide a flood simulation method, apparatus, and storage medium based on urban infrastructure, thereby improving the simulation accuracy of urban rainstorm and flood models and reducing urban flood disasters.

[0004] The technical solution adopted by this invention to solve its problem is:

[0005] In a first aspect, embodiments of this application provide a flood simulation method based on urban infrastructure. The method includes: acquiring street view images of the city, wherein the street view images include street view data acquired from different urban infrastructures at different angles and at different times; classifying the street view data according to the type of infrastructure to extract urban infrastructure data; fusing the infrastructure data and the city's topographic data to obtain fused urban topographic data; dividing the city's catchment areas based on the fused topographic data and extracting water system data; and establishing a flood simulation model based on urban infrastructure according to the water system data.

[0006] Secondly, embodiments of this application provide a flood simulation device based on urban infrastructure, comprising: a data acquisition module for acquiring street view images of the city, wherein the street view images include street view data acquired from different urban infrastructures at different angles and at different times; a classification module for classifying the street view data according to the type of infrastructure and extracting urban infrastructure data; a fusion module for fusing the infrastructure data and the topographic data of the city to obtain fused topographic data of the city; and a simulation module for dividing the city's catchment areas based on the fused topographic data and extracting water system data, and establishing a flood simulation model based on urban infrastructure according to the water system data.

[0007] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the flood simulation method based on urban infrastructure as described above.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the flood simulation method based on urban infrastructure as described above.

[0009] In this embodiment, street view images of the city are collected, including street view data collected from different urban infrastructures at different angles and time periods. The street view data is classified and processed according to the type of infrastructure to extract urban infrastructure data. The infrastructure data and urban topographic data are fused to obtain fused urban topographic data. Based on the fused topographic data, the city's catchment areas are divided and water system data is extracted. A flood simulation model based on urban infrastructure is established based on the water system data. This model fully considers the impact of urban street networks, wall systems, buildings, and other infrastructure on urban flood processes, improves the simulation accuracy of urban rainstorm flood models, and reduces urban flood disasters.

[0010] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0011] Figure 1 This is a flowchart of a flood simulation method based on urban infrastructure, as described in 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 A 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 this application. Detailed Implementation

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

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

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

[0023] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0024] The present invention relates to a flood simulation method, apparatus, and storage medium based on urban infrastructure. This method involves acquiring street view images of the city, including street view data collected from different angles and time periods of different urban infrastructures; classifying the street view data according to the type of infrastructure to extract urban infrastructure data; fusing the infrastructure data and urban topographic data to obtain fused urban topographic data; dividing the city's catchment areas based on the fused topographic data and extracting water system data; and establishing a flood simulation model based on urban infrastructure according to the water system data. This model fully considers the impact of urban street networks, wall systems, and buildings on urban flood processes, improving the simulation accuracy of urban stormwater and flood models and reducing urban flood disasters.

[0025] With the increase in extreme rainfall events caused by climate change, urban flooding disasters are characterized by their suddenness, high peak volume, and wide affected area. In recent years, numerous urban floods have occurred frequently, causing severe losses. Currently, urban flood simulation methods can be mainly categorized into hydrological methods, hydrodynamic methods, and coupled hydrological and hydrodynamic methods. Hydrological methods were the earliest adopted approach, calculating watershed runoff based on hydrological principles. They are simple in structure and relatively efficient, but cannot provide information on the impact of water level rise on urban structures and the flow patterns near drainage network inlets. Hydrodynamic methods are based on grid cells, using one-dimensional Saint-Venant equations and two-dimensional shallow water equations to calculate surface runoff, drainage network flow, and river flow. These methods offer high accuracy but are less efficient, especially for large-scale urban flood prediction, consuming excessive computation time.

[0026] However, current urban flood simulations do not take into account the impact of urban street networks, perimeter fencing systems, and buildings on flood events, resulting in unreasonable simulation results and adversely affecting subsequent urban flood risk assessments.

[0027] Based on the above, embodiments of the present invention provide a flood simulation method, apparatus, and storage medium based on urban infrastructure. This involves acquiring street view images of the city, including street view data collected from different urban infrastructures at different angles and time periods; classifying the street view data according to the type of infrastructure to extract urban infrastructure data; fusing the infrastructure data and urban topographic data to obtain fused urban topographic data; dividing the city's catchment areas based on the fused topographic data and extracting water system data; and establishing a flood simulation model based on urban infrastructure according to the water system data. This fully considers the impact of urban street networks, wall systems, and buildings on urban flood processes, improving the simulation accuracy of urban stormwater and flood models and reducing urban flood disasters.

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

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

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

[0031] Understandably, existing urban flood simulation methods are designed for managing data on urban drainage system infrastructure, meeting the data requirements for hydrological, hydraulic, and water quality simulations. They store relevant data in databases, supporting simulations of both separate and combined sewer systems, covering surface hydrology, pipe network hydraulics, and water quality processes. Furthermore, simulation models can also model the hydrological, hydraulic, and water quality changes associated with urban low-impact development measures, and achieve one-dimensional and two-dimensional coupled simulations of urban flooding, providing detailed simulation results such as inundation depth, flow direction, and velocity. Currently, urban flood simulation methods cannot comprehensively consider the impact of urban street networks, perimeter fencing systems, and buildings on flood processes, significantly affecting the accuracy and reliability of urban flood simulations. Therefore, to accurately obtain the specific parameters of urban infrastructure such as street networks, perimeter fencing systems, and buildings, and their impact on flood simulations, it is necessary to collect urban street view images to update and obtain the distribution and size of urban infrastructure in real time, enabling more accurate urban flood simulations.

[0032] Understandably, traditional maps are geographical query tools recorded using lines, points, and text symbols. More accurate maps rely on satellite imagery to represent geographical locations, but these are one-dimensional. A three-dimensional, realistic visual effect would be far more immersive. Currently, street view services on major platforms have achieved this effect, representing a true revolution in traditional map thinking. This innovative approach allows users to view streetscapes in 360-degree panoramas, giving the feeling of walking on the street itself. Therefore, street view maps are a type of real-world map service. They provide users with 360-degree panoramic images of cities, streets, or other environments, offering an immersive map browsing experience. Through street view images, users can realistically see high-definition street scenes on any device and obtain specific parameters of the city's internal structure and infrastructure.

[0033] Please see Figure 2 , Figure 2 A schematic diagram illustrating the specific implementation process of another embodiment of step S1000 described above is shown. For example... Figure 2As shown, step S1000 includes at least the following steps:

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

[0035] It's important to note that to obtain complete and accurate street view images of the current city, it's essential to first acquire the city's coordinate information. This allows for rapid location of the city on the coordinate picking platform, avoiding measurement errors caused by positional offsets. Coordinates are ordered arrays used to precisely describe the location 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, precise positioning relies on coordinate systems to convert complex spatial relationships into operable data. Their value lies not only in location description but also in providing a universal language for quantitative analysis across various fields.

[0036] It is understandable that mainstream coordinate acquisition platforms include Amap, Baidu Maps, Tencent Maps, and Google Maps, each using different coordinate systems and with different functional focuses. In practical applications, extracting the coordinate information of the street view images required in this application embodiment using street view images collected by Baidu Street View Maps in 3D street view mode is prior art and will not be elaborated here.

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

[0038] Understandably, to obtain more accurate and comprehensive street view data corresponding to city coordinates, it's necessary to set collection intervals, angles, and ranges. This allows for the collection of street view data across different time intervals, angles, and ranges, reducing errors in urban flood simulation caused by collection errors and blind spots. In practical applications, Python-based data acquisition methods are primarily used for street view data collection and processing. This is because Python provides efficient high-level data structures and enables 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 required street view data range's coordinate information is obtained from the Baidu coordinate acquisition platform. This coordinate information is then input into the Python platform, and the street view point collection intervals, angles, and ranges are set. Finally, data collection points are deployed within the street view data collection range.

[0039] It's important to note that a 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 along both the X and Y axes. In practical applications, regardless of whether the grid is folded, the corresponding grids of the dot matrix are isomorphic, meaning that the elements on the dot matrix can correspond one-to-one with each other. Therefore, what's important for a dot matrix is ​​not how it's presented on a two-dimensional plane, but how the elements within the dot matrix are connected. In practical applications, dot matrix data sets are a simple, rich, and widely used data representation method. These dot matrix data sets primarily represent numbers, characters, or graphics in a certain grid format, facilitating computer processing and storage. The charm of dot matrices lies not only in their form but also in the infinite possibilities they carry. Specifically, dot matrix numbers are like tiny bricks; when each brick is arranged according to certain rules, it can form complete information. A single dot matrix data set is simply an image composed of many pixels. Each pixel represents a part of a number or character. Through the arrangement and combination of these points, computers can recognize various shapes, patterns, and even text. The core value of raster digital datasets lies in their efficiency and universality. Many classic computer vision tasks, such as handwritten digit recognition, character recognition, and image classification, require training using raster digital datasets. Understandably, converting street view data into raster data is an existing technology, which will not be elaborated upon here.

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

[0041] Understandably, when acquiring street view data corresponding to the sampling points, it's necessary to link the street view data to corresponding folders and store it to facilitate computer learning, calculation, and correction. As the above steps show, street view data is acquired at different sampling intervals, from different angles, and within different sampling ranges. Therefore, to ensure the integrity and traceability of the data, it's necessary to store the street view data in different folders. In practical applications, it's necessary to link the sampling points to corresponding folders and store the street view data. In some embodiments, street view data can be acquired and stored separately according to street view images and image parameter data, facilitating the rapid and efficient reading of street view data required for flood simulation and improving the timeliness and accuracy of urban flood simulation.

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

[0043] Understandably, the infrastructure that affects urban flood simulation includes, but is not limited to, street networks, perimeter fencing 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 in order to extract the impact coefficients of different infrastructures on flood simulation in different dimensions and ranges, i.e., urban infrastructure data.

[0044] Please see Figure 3 , Figure 3 A schematic diagram illustrating the specific implementation process of another embodiment of step S2000 described above is shown. For example... 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 is understood that the street view images obtained through the above steps include urban infrastructure such as street networks, perimeter fencing systems, and buildings. Therefore, by automatically identifying and filtering street view images, the types of infrastructure can be determined. In some embodiments, infrastructure can be classified according to lines and surfaces. Specifically, street networks and perimeter fencing systems are generalized as line data, and buildings are extracted as surface data to accelerate the processing and analysis of street view images and improve the timeliness of urban flood simulation.

[0047] Step S2200: Determine the data type of the infrastructure based on its type.

[0048] Understandably, different types of infrastructure require different data types. In the embodiments described above, street networks and perimeter fencing systems are generalized as line data, while buildings are extracted as surface data. This allows for a precise and rapid reflection of the impact of different types of infrastructure on flood simulations, translating this data into specific parameters. This is because the impact of different types of infrastructure, such as perimeter fencing versus residential buildings, differs significantly in watershed hydrological analysis of urban environments. Therefore, it is necessary to determine the data type of infrastructure based on its type to conduct urban flood simulations more scientifically and accurately.

[0049] Step S2300: Perform generalization processing on the street view data according to the data type to obtain the elevation data of the infrastructure.

[0050] Understandably, after acquiring the data type, detailed settings can be made for the specific parameters of the infrastructure. Specifically, in the embodiment described above, street networks and wall systems are generalized into line data, and buildings are extracted into area data. That is, when the infrastructure is a street network or wall system, extracting the height and length of the infrastructure allows for accurate acquisition of its surface elevation and length information; when the infrastructure is a residential building, shopping mall, or other building, extracting its height, width, and length allows for accurate acquisition of its surface elevation and occupied area information, thus achieving accurate acquisition of elevation data for different types of infrastructure. Among these, elevation data is the core geographic data describing surface elevation information and is widely used in geology, meteorology, urban planning, and other fields. Currently, global or regional elevation datasets can be obtained through various free channels, each with its own characteristics in terms of resolution, accuracy, and processing methods. By generalizing street view data according to data type, accurate acquisition of infrastructure elevation data facilitates a more scientific digital analysis of the impact of infrastructure on urban flooding, improving the accuracy and reference value of urban flood simulation methods.

[0051] It should be noted that a Digital Elevation Model (DEM) is a digital simulation of ground topography achieved through limited terrain elevation data; that is, a digital representation of the surface morphology of the terrain. A DEM is a physical ground model that represents ground elevation using an ordered array of numerical values. Understandably, to allow street view data to be directly imported into a DEM, the street view data needs to be generalized to improve the accuracy of the DEM.

[0052] Please see Figure 4 , Figure 4 A schematic diagram illustrating the specific implementation process of another embodiment of step S2000 described above is shown. For example... Figure 4 As shown, step S2000 includes at least the following steps:

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

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

[0055] Step S2500: Determine the calculated value of the infrastructure based on the feature value and the preset calculation threshold.

[0056] Understandably, given the large number of infrastructure projects, it's necessary to set calculation thresholds for these infrastructure projects to screen them and avoid data redundancy and processing pressure caused by numerous insignificant feature values. This ensures efficient computation of urban infrastructure data and conserves computing resources. In practical applications, the calculation thresholds for infrastructure projects included in the calculation are adjusted based on 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 road width and level; when the infrastructure is a wall, the proportion of walls participating in the urban flood simulation can be set according to length; and when the infrastructure is a building, the area threshold for participating in the urban flood simulation can be determined based on the building's floor area.

[0057] Please see Figure 5 , Figure 5 A schematic diagram illustrating the specific implementation process of another embodiment of step S2500 described above is shown. For example... Figure 5 As shown, step S2500 includes at least the following steps:

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

[0059] Understandably, by setting a calculation threshold and comparing the feature value with the corresponding threshold, the feature value can be filtered based on the comparison result. Specifically, when the feature value is within the calculation threshold range, it is set as the calculated value for the infrastructure. In some embodiments, if the calculation threshold for a wall is set to 2 meters, then when the actual length of the infrastructure is 3 meters, the actual length of the wall is greater than the calculation threshold, and the actual length of the wall is configured as the calculated value for the infrastructure. Of course, in other embodiments, multiple threshold intervals or threshold ranges can be set. When the feature value of the infrastructure falls into different threshold intervals or ranges, it is calculated with the corresponding weights to obtain the final calculated value for the infrastructure.

[0060] Step S2520: When the feature value is less than the calculated threshold, delete the feature value.

[0061] Understandably, by setting a calculation threshold and comparing the feature value with the corresponding threshold, the feature value can be filtered based on the comparison result. Specifically, when a feature value is outside the range of the calculation threshold, the feature value is set to zero, or the feature value of the infrastructure is deleted. In some embodiments, if the calculation threshold for a 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 also be set, and when the feature value of the infrastructure is outside each threshold interval or range, the feature value of the infrastructure can be deleted, that is, the impact of the infrastructure in the urban flood simulation can be ignored.

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

[0063] Understandably, after obtaining the calculated values ​​of the infrastructure in the above steps, the calculated values ​​of the infrastructure are summarized 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 and improve the timeliness of urban flood simulation.

[0064] Step S3000: Merge and process infrastructure data and urban terrain data to obtain fused urban terrain data.

[0065] Understandably, after obtaining infrastructure data in the above steps, it is necessary to overlay it with existing urban topographic data to improve and optimize the urban topographic data, so that the impact of urban infrastructure in the urban flood simulation process can be fully and accurately reflected in the fused urban topographic data.

[0066] Please see Figure 6 , Figure 6 A schematic diagram illustrating the specific implementation process of another embodiment of step S3000 described above is shown. For example... Figure 6 As shown, step S3000 includes at least the following steps:

[0067] Step S3100: Obtain the city's terrain data.

[0068] It is understandable that urban topographic data can be directly obtained through a DEM. There are various methods for creating a DEM, depending on the data source and acquisition method, including: direct ground measurement, involving instruments such as horizontal guide rails, probes, probe holders, and relative elevation measuring boards, or high-end instruments such as GPS, total stations, and field surveying equipment; acquisition based on aerial or space imagery through photogrammetry, such as stereo coordinate instrument observation and aerial triangulation, analytical mapping, digital photogrammetry, etc.; and acquisition from existing topographic maps, such as grid reading, manual digitizer tracking, and semi-automatic scanner acquisition, followed by interpolation to generate the DEM. There are many DEM interpolation methods, mainly three types: global interpolation, block interpolation, and point-by-point interpolation. Global interpolation uses a fitting model established from 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 at the point to be interpolated to fit the surrounding data points. The range of data points changes with the interpolation location, hence it is also called moving fit. In practical applications, directly reading urban terrain data from an existing DEM is a current technology and will not be elaborated upon here.

[0069] Step S3200: According to the type of infrastructure, overlay the infrastructure data onto the terrain data to obtain fused terrain data.

[0070] Understandably, in practical applications, to ensure that infrastructure data can be accurately and quickly integrated into urban topographic data, it is necessary to overlay infrastructure data onto topographic data according to the type of infrastructure. Specifically, when the infrastructure is a wall, it is embedded into the DEM as a linear water barrier; when the infrastructure is a road network, it is integrated into the terrain using cut-and-fill methods based on its calculated values; and when the infrastructure is a building, it is embedded into the terrain as a generalized areal water barrier. By overlaying different infrastructure data onto topographic data, a new urban topography that considers urban infrastructure is formed, ensuring that the integrated topographic data accurately reflects the impact of infrastructure in urban flood simulation and improving the reference value of urban flood simulation.

[0071] Step S4000: Divide the city’s water catchment area based on the fused terrain data and extract water system data, and establish a flood simulation model based on urban infrastructure based on the water system data.

[0072] Understandably, after acquiring fused topographic data through steps S1000-S3000, a flood simulation model based on urban infrastructure can be established. A Digital Elevation Model (DEM) is a digital elevation model describing the topographic surface, providing crucial raw data for feature identification and watershed topography, containing rich geomorphological, topographic, and hydrological information. In hydrological information, the DEM primarily describes watershed topographic information, including sub-watershed division and boundary determination, river network extraction and identification, aspect, and slope determination, thus providing underlying surface parameters for distributed hydrological simulation. With the continuous development and improvement of DEM-based watershed feature extraction technology, the accuracy of extracting watershed hydrological features using DEM, especially in areas with significant topographic relief, will undoubtedly improve.

[0073] Please see Figure 7 , Figure 7 A schematic diagram illustrating the specific implementation process of another embodiment of step S4000 described above is shown. For example... Figure 7 As shown, step S4000 further includes at least the following steps:

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

[0075] Understandably, in existing technologies, the catchment area of ​​a watershed is calculated by analyzing and processing its topographic and hydrological indices and characteristic parameters. A watershed is a complete and relatively independent natural system on the Earth's surface, serving as the catchment area for rivers and lakes, connecting the left and right banks, and upstream and downstream areas into an inseparable whole through water. Similarly, in urban flood simulation, the city's catchment area can be determined by integrating topographic data. Specifically, hydrological analysis tools are used to calculate data, including DEM (Digital Elevation Model) depression filling, water flow direction, and runoff accumulation calculations, and repeated experiments are conducted to obtain a catchment area threshold to form the city's catchment area. A catchment area (or watershed) refers to the mountain range separating one watershed from another, encompassing the entire area or region supplying water to a river or lake. However, due to the influence of geology, groundwater, and other factors, it cannot be determined solely by simple area; furthermore, human intervention and time-varying changes make it a complex entity.

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

[0077] Understandably, after obtaining the city's catchment area through the above steps, the city's water system can be extracted by combining it with the city's rainfall information. Rainfall refers to the total amount of rain or snowfall within 12 or 24 hours. It can also refer to the depth of liquid or solid (after melting) water that falls from the sky to the ground and accumulates on a horizontal surface without evaporation, infiltration, or runoff. Rainfall is measured in mm, with one decimal place used in meteorological observations to visually represent the amount of rainfall.

[0078] Understandably, water system extraction is a method used to extract water network structures 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 importance in geographical research and environmental protection; therefore, the research and application of water system extraction algorithms are becoming increasingly important. The core idea of ​​water system extraction is to extract the connections between water bodies in geospatial data through a series of calculations and analyses based on the characteristics and topological relationships of the data, forming a complete water network. Commonly used geographic data in water system extraction algorithms include DEM data and water body vector data. By determining rainfall information in catchment areas and cities, and simulating surface runoff flow, water systems are formed to determine the city's water system data.

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

[0080] Understandably, the above steps involve overlaying urban topographic and infrastructure data to obtain fused topographic data, generating urban water system data based on the DEM, and completing the processing of spatial data related to the simulation model. Therefore, establishing an urban flood simulation model based on fused topographic and water system data fully considers the impact of urban street networks, wall systems, and buildings on urban flood processes, improving the simulation accuracy of urban stormwater and flood models, and reducing urban flood disasters. The method of establishing a flood simulation model based on fused topographic and urban water system data is existing technology and will not be elaborated upon here.

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

[0082] The acquisition module 510 is used to acquire street view images of the city, which include street view data acquired from different urban infrastructures at different angles and at different times.

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

[0084] The fusion module 530 is used to fuse infrastructure data and urban terrain data to obtain fused urban terrain data.

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

[0086] It should be noted that the information interaction and execution process between the modules of the above-mentioned device are based on the same concept as the method embodiment of this application. For details on their specific functions and technical effects, please refer to the method embodiment section, and they will not be repeated here.

[0087] Figure 9 An electronic device 600 according to an embodiment of this application is shown. The electronic device 600 includes, but is not limited to:

[0088] Memory 601 is used to store programs;

[0089] The processor 602 is used 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 used to execute the above-mentioned flood simulation method based on urban infrastructure.

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

[0091] The 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 flood simulation method based on urban infrastructure described in any embodiment of this application. The processor 602 implements the above-described flood simulation method based on urban infrastructure by running the non-transitory software program and instructions stored in the memory 601.

[0092] The memory 601 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store the aforementioned flood simulation method based on urban infrastructure. Furthermore, the memory 601 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 601 may optionally include memory remotely located relative to the processor 602, and these remote memories may be connected to the processor 602 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

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

[0094] This application also provides a storage medium storing computer-executable instructions for executing the above-described flood simulation method based on urban infrastructure.

[0095] In one embodiment, the storage medium stores computer-executable instructions that are executed by one or more control processors 602, such as one of the processors 602 in the aforementioned electronic device 600, which enable the one or more processors 602 to perform the flood simulation method based on urban infrastructure provided in any embodiment of this application.

[0096] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0097] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A flood simulation method based on urban infrastructure, characterized in that, Includes the following steps: Collect street view images of the city, wherein the street view images include street view data collected from different urban infrastructures at different angles and at different times; The street view data is classified according to the type of infrastructure to obtain the elevation data of the infrastructure, and the infrastructure data of the city is extracted, including: obtaining the feature values ​​of the infrastructure in the elevation data; determining the calculated value of the infrastructure based on the feature values ​​and a preset calculation threshold; and summarizing the calculated values ​​to obtain the infrastructure data of the city. By fusing the infrastructure data and the city's terrain data, we obtain the city's fused terrain data; Based on the fused terrain data, the city's catchment areas are divided and water system data is extracted. A flood simulation model based on urban infrastructure is then established using the water system data.

2. The flood simulation method based on urban infrastructure according to claim 1, characterized in that, The street view images of the city being collected include: The coordinate information of the street view image is obtained from the coordinate acquisition platform of the city; Input the coordinate information, and set the acquisition interval, acquisition angle and acquisition range of the street view data to obtain the acquisition point matrix of the street view data; The collected point matrix is ​​linked to the 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 process of classifying the street view data according to the type of infrastructure includes: The type of infrastructure is determined based on the street view image; Determine the data type of the infrastructure based on its type; The street view data is generalized according to the data type to obtain the elevation data of the infrastructure.

4. The flood simulation method based on urban infrastructure according to claim 1, characterized in that, The step of determining the calculated value of the infrastructure based on the feature value and a preset calculation threshold includes: When the feature value is greater than or equal to the calculation threshold, the feature value is set to the calculated value of the infrastructure; If the feature value is less than the calculated threshold, the feature value is deleted.

5. The flood simulation method based on urban infrastructure according to claim 3, characterized in that, The fusion of the infrastructure data and the city's terrain data yields fused terrain data for the city, including: Obtain the terrain data of the city; According to the type of infrastructure, the infrastructure data is overlaid on the terrain data to obtain the fused terrain data.

6. The flood simulation method based on urban infrastructure according to claim 5, characterized in that, The process of dividing the city's catchment areas 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: The city's water catchment area is determined based on the fused terrain data; Based on the rainfall information of the catchment area and the city, the city's water system data is determined; A flood simulation model based on urban infrastructure is established based on the fused terrain data and the water system data.

7. A flood simulation device based on urban infrastructure, characterized in that, include: The acquisition module is used to acquire street view images of the city, wherein the street view images include street view data acquired from different urban infrastructures at different angles and at different times; A classification module is used to classify the street view data according to the type of infrastructure, obtain the elevation data of the infrastructure, and extract the infrastructure data of the city, including: obtaining the feature values ​​of the infrastructure in the elevation data; determining the calculated value of the infrastructure according to the feature values ​​and a preset calculation threshold; and summarizing the calculated values ​​to obtain the infrastructure data of the city. The fusion module is used to fuse the infrastructure data and the city's terrain data to obtain fused terrain data of the city. The simulation module is used to divide the city's catchment area based on the fused terrain data and extract water system data, and to establish a flood simulation model based on the urban infrastructure according to the water system data.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the flood simulation method based on urban infrastructure as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The system contains a computer program that, when executed by a processor, implements the flood simulation method based on urban infrastructure as described in any one of claims 1 to 6.

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