A flood risk warning method, device and medium

Abstract

The embodiment of the present application discloses a flood risk warning method, device and medium, which belongs to the field of flood warning technology and solves the problem that it is difficult to accurately warn of flood risks by relying on manual experience. It includes: constructing a two-dimensional hydraulic model based on the acquired geographic spatial data corresponding to the area to be tested and the regional building feature data corresponding to the area to be tested; acquiring rainwater data corresponding to the area to be tested, adjusting the boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generating a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested; dividing the area to be tested into multiple small areas based on the regional building features corresponding to the area to be tested, and determining the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram; determining different flood response information based on the flood risk level and the building features of each small area to generate a flood warning strategy.

Description

Technical Field

[0001] The present application relates to the field of flood warning technology, and in particular to a flood risk warning method, device and medium. Background Art

[0002] Against the backdrop of increasingly severe climate change, the frequent occurrence of flood disasters has become a global challenge, bringing unprecedented pressure to social and economic stability and ecological and environmental health.

[0003] Especially in complex river networks, dense urban drainage networks and vast plains, the simulation of water flow becomes particularly complex. The water flow in these areas is not only directly affected by the topography and landforms, but also has complex interactions with artificial facilities such as buildings and roads, making the propagation, diffusion and interaction of water flow extremely complex and difficult to predict.

[0004] Faced with this severe situation, traditional flood prediction and risk assessment methods that rely on human experience are unable to provide accurate early warnings when faced with complex and changing natural environments and urban drainage systems. Summary of the invention

[0005] The embodiments of the present application provide a flood risk warning method, device and medium for solving the following technical problems: Traditional flood prediction and risk assessment methods are difficult to accurately warn when facing complex and changeable natural environments and urban drainage systems, relying on manual experience.

[0006] The present application embodiment adopts the following technical solutions:

[0007] The embodiment of the present application provides a flood risk warning method. The method includes: constructing a two-dimensional hydraulic model based on the acquired geographic spatial data corresponding to the area to be tested and the regional building feature data corresponding to the area to be tested; acquiring rainwater data corresponding to the area to be tested, adjusting the boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generating a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested; dividing the area to be tested into multiple small areas based on the regional building features corresponding to the area to be tested, determining the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram; determining different flood response information based on the flood risk level and the building features of each small area to generate a flood warning strategy.

[0008] By constructing a two-dimensional hydraulic model, the embodiment of the present application can more accurately simulate the movement of water in two-dimensional space, and accurately adjust the boundary conditions of the two-dimensional hydraulic model through real-time or historical rain data to reflect the impact of actual rainfall on flood evolution. The generated spatiotemporal evolution diagram can intuitively display the distribution and changes of floods at different times and locations, and provide accurate reference information for flood warning and response, thereby improving the accuracy of flood warning. Secondly, the embodiment of the present application divides the area to be tested into multiple small areas, and considers the architectural characteristics of each small area to more finely evaluate the flood risks in different areas. Combined with the flood risk level and the architectural characteristics of each small area, flood response information for different areas is determined to improve the efficiency and effectiveness of flood response.

[0009] In one implementation of the present application, after determining the flood risk level corresponding to each small area, the method also includes: based on the flow of the flood, determining any two first small areas and second small areas in a superior-subordinate relationship as a warning area pair; determining a reference probability of a flood occurring in the second small area when a flood occurs in the first small area based on the terrain data between the warning area pair and the flood risk levels corresponding to the two small areas of the warning area pair; and calculating the impact value of each small area on the subordinate small area when a flood occurs based on the reference probability.

[0010] In one implementation of the present application, a two-dimensional hydraulic model is constructed based on the acquired geospatial data corresponding to the area to be measured and the regional building feature data corresponding to the area to be measured, specifically including: integrating the acquired geospatial data corresponding to the area to be measured; wherein the geospatial data includes at least one of satellite remote sensing images and digital elevation models; performing flood evolution impact analysis on the acquired building feature data, and determining a resistance coefficient based on the results of the flood evolution impact analysis; wherein the building feature data includes at least one of building layout, building height and building materials; based on the acquired drainage network map of the area to be measured and the surface cover type corresponding to the area to be measured, determining the drainage capacity value and surface infiltration value corresponding to the area to be measured; and constructing a two-dimensional hydraulic model based on the integrated geospatial data, resistance coefficient, drainage capacity value and surface infiltration value.

[0011] In one implementation of the present application, rainwater data corresponding to the area to be tested is obtained, boundary conditions of a two-dimensional hydraulic model are adjusted based on the rainwater data, and a spatiotemporal evolution diagram is generated, specifically including: splicing the rainwater data based on the timestamp of the rainwater data to obtain spliced ​​rainwater data; detecting abnormal data in the spliced ​​rainwater data, replacing the abnormal data with vacant data, performing vacancy interpolation processing on the vacant data, and obtaining spliced ​​rainwater data with vacancy interpolation; performing exponential smoothing processing on the spliced ​​rainwater data with vacancy interpolation to obtain reference rainwater data; inputting the reference rainwater data into the two-dimensional hydraulic model to adjust the boundary conditions of the two-dimensional hydraulic model; wherein the boundary conditions include at least one of a lateral inflow boundary and a precipitation infiltration boundary; determining flood prediction information corresponding to the area to be tested based on the simulation data of the two-dimensional hydraulic model; mapping the flood prediction data to the corresponding position of a preset map to generate a spatiotemporal evolution diagram.

[0012] In one implementation of the present application, the area to be tested is divided into multiple small areas based on the regional building features corresponding to the area to be tested, and the flood risk level corresponding to each small area is determined based on the building features and the spatiotemporal evolution diagram of each small area, specifically including: based on the geographic information corresponding to the area to be tested, the area to be tested is first divided to obtain multiple first areas; wherein the geographic information includes at least one of terrain information, landform information and water system distribution information; the building features corresponding to the multiple first areas are obtained, and based on the building features, the multiple first areas are secondly divided to obtain multiple small areas; wherein the building features include at least one of building density, building type, building height, building material and building age; based on the spatiotemporal evolution diagram, the exposure of each small area in a flood event is determined; based on the preset weights, exposure and building features, the flood risk level corresponding to each small area is determined.

[0013] In one implementation of the present application, based on preset weights, exposure and building characteristics, the flood risk level corresponding to each small area is determined, specifically including: subtracting the inundation water level elevation grid map corresponding to the area to be tested from the urban terrain elevation grid to determine the inundated part of the disaster-bearing body; based on the depth of the inundated part, the exposure level of each small area is divided; based on the damage to the buildings corresponding to each small area, a flood disaster loss curve corresponding to each small area is drawn, based on the curve, the total value of the disaster loss is determined, and the building feature level of each small area is divided based on the total value; based on the preset weights, the exposure level and the building feature level are weighted calculation to determine the flood risk level corresponding to each small area.

[0014] In one implementation of the present application, different flood response information is determined based on the flood risk level and the architectural features of each small area to generate a flood warning strategy, specifically including: querying in a flood response information database based on the architectural features of each small area to determine multiple response information corresponding to the architectural features; determining first reference response information corresponding to the flood risk level from multiple response information based on the flood risk level and the mapping relationship; inputting rainwater data and flood risk level corresponding to the area to be tested into a preset flood prediction model to obtain flood trend prediction information corresponding to the area to be tested; querying in a response information database based on the flood trend prediction information to determine multiple second reference response information; generating a flood warning strategy based on the first reference response information and the second reference response information.

[0015] In one implementation of the present application, based on flood trend prediction information, a query is performed in a response information database to determine multiple second reference response information, specifically including: determining the time of change of flood risk level based on flood trend prediction information; determining the flood risk level that changes with time, and based on the changed flood risk level, querying the corresponding second reference response information in the response information database; matching the time of change of flood risk level with the second reference response information corresponding to different times.

[0016] An embodiment of the present application provides a flood risk warning device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: construct a two-dimensional hydraulic model based on the acquired geographic spatial data corresponding to the area to be tested and the regional building feature data corresponding to the area to be tested; obtain rainwater data corresponding to the area to be tested, adjust the boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generate a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested; based on the regional building features corresponding to the area to be tested, divide the area to be tested into multiple small areas, and determine the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram; based on the flood risk level and the building features of each small area, determine different flood response information to generate a flood warning strategy.

[0017] A non-volatile computer storage medium provided in an embodiment of the present application stores computer executable instructions, wherein the computer executable instructions are configured to: construct a two-dimensional hydraulic model based on acquired geographic spatial data corresponding to a test area and regional building feature data corresponding to the test area; acquire rainwater data corresponding to the test area, adjust boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generate a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of floods on the test area; divide the test area into a plurality of small areas based on regional building features corresponding to the test area, and determine the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram; and determine different flood response information based on the flood risk level and the building features of each small area to generate a flood warning strategy.

[0018] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: the embodiments of the present application can more accurately simulate the movement law of water flow in two-dimensional space by constructing a two-dimensional hydraulic model, and accurately adjust the boundary conditions of the two-dimensional hydraulic model through real-time or historical rain data to reflect the impact of actual rainfall on flood evolution. The generated spatiotemporal evolution diagram can intuitively display the distribution and changes of floods at different times and locations, and provide accurate reference information for flood warning and response, thereby improving the accuracy of flood warning. Secondly, the embodiments of the present application divide the area to be tested into multiple small areas, and consider the architectural characteristics of each small area to more finely evaluate the flood risks in different areas. Combined with the flood risk level and the architectural characteristics of each small area, the flood response information for different areas is determined to improve the efficiency and effectiveness of flood response. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0020] In the figure:

[0021] Figure 1 A flow chart of a flood risk warning method provided in an embodiment of the present application;

[0022] Figure 2 A schematic structural diagram of a flood risk warning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] Embodiments of the present application provide a flood risk warning method, device, and medium.

[0024] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0025] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0026] Figure 1 A flow chart of a flood risk warning method provided in an embodiment of the present application is as follows: Figure 1 As shown in Figure 1, the flood risk early warning method includes the following steps:

[0027] S101 . Constructing a two-dimensional hydraulic model based on the acquired geographic space data corresponding to the area to be measured and the regional building feature data corresponding to the area to be measured.

[0028] In one embodiment of the present application, the acquired geospatial data corresponding to the area to be measured are integrated; wherein the geospatial data includes at least one of satellite remote sensing images and digital elevation models. The acquired building feature data are subjected to flood evolution impact analysis, and the resistance coefficient is determined based on the flood evolution impact analysis results; wherein the building feature data includes at least one of building layout, building height, and building materials. Based on the acquired drainage network map of the area to be measured and the surface coverage type corresponding to the area to be measured, the drainage capacity value and the surface infiltration value corresponding to the area to be measured are determined. Based on the integrated geospatial data, resistance coefficient, drainage capacity value, and surface infiltration value, a two-dimensional hydraulic model is constructed.

[0029] Specifically, geospatial data is the basis for flood simulation and risk assessment. It provides key information such as topography, landforms, and water systems, and integrates the acquired geospatial data of the area to be measured. These data may include satellite remote sensing images and digital elevation models. For example, high-resolution images of the area to be measured obtained through satellite remote sensing technology can be used to extract terrain information, water system distribution, surface cover type, etc., and digital elevation models can be used to obtain surface altitude information.

[0030] Furthermore, building characteristic data, such as building layout, building height and building materials, buildings will hinder the flow of floods and increase the resistance of water flow. The impact of building characteristic data on flood evolution is analyzed, and the resistance coefficient is determined based on the analysis results. For example, a dense building layout will reduce the space for flood flow and increase the resistance of water flow. By analyzing the building layout, the resistance distribution in different areas can be determined. Tall buildings will have a significant blocking effect on floods and increase the difficulty of floods passing through the area. According to the building height, the resistance coefficient of the building to flood flow can be estimated. Different materials have different permeability and resistance to water. For example, the resistance of concrete buildings is greater than that of wooden buildings. By considering building materials, the resistance coefficient can be estimated more accurately.

[0031] Furthermore, the drainage system plays an important role in flood control and discharge. At the same time, the surface cover type (such as vegetation, bare land, water body, etc.) will affect the surface infiltration capacity, thereby affecting the flood process. Based on the drainage network map and surface cover type data of the area to be tested, the drainage capacity value and surface infiltration value are determined. For example, by analyzing the drainage network map, the layout, diameter, flow rate and other information of the drainage pipes can be understood, and then the drainage capacity value of the entire area can be estimated. The surface cover type information of the area to be tested can be obtained through satellite remote sensing images. Different surface cover types have different infiltration capacities. For example, the surface infiltration capacity of vegetation-covered areas is usually higher than that of bare land or urban areas. Based on this information, the surface infiltration values ​​of different areas can be estimated.

[0032] Furthermore, after integrating geospatial data, determining resistance coefficients, drainage capacity values, and surface infiltration values, a two-dimensional hydraulic model can be constructed. This model will be used to simulate the evolution of floods in two-dimensional space, including the propagation, diffusion, and interaction with buildings of water flow. A two-dimensional hydraulic model can be constructed based on the above data using hydraulic simulation software. Boundary conditions (such as rainfall intensity, duration, etc.) are set in the model, and the evolution of floods in the area to be tested is simulated. By comparing the simulation results with the actual flood situation, the accuracy and reliability of the model are evaluated, and it is optimized and improved.

[0033] S102, obtaining rainwater data corresponding to the area to be tested, adjusting boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generating a spatiotemporal evolution diagram.

[0034] In one embodiment of the present application, rainwater data is spliced ​​based on the timestamp of rainwater data to obtain spliced ​​rainwater data. Abnormal data in the spliced ​​rainwater data is detected, and the abnormal data is replaced with vacant data, and the vacant data is interpolated to obtain spliced ​​rainwater data with vacant interpolation. The spliced ​​rainwater data with vacant interpolation is subjected to exponential smoothing to obtain reference rainwater data. The reference rainwater data is input into a two-dimensional hydraulic model to adjust the boundary conditions of the two-dimensional hydraulic model; wherein the boundary conditions include at least one of a lateral inflow boundary and a precipitation infiltration boundary. Based on the simulation data of the two-dimensional hydraulic model, the flood prediction information corresponding to the area to be tested is determined. The flood prediction data is mapped to the corresponding position of the preset map to generate a spatiotemporal evolution diagram. wherein the spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested.

[0035] Specifically, the timestamps of the rain data are determined, and based on the order of the timestamps, different rain data are spliced ​​to form a complete rainfall time series. In the spliced ​​data, there may be outliers, such as extreme values ​​caused by equipment failure, data transmission errors, etc. These outliers need to be detected and replaced with vacant values. After replacing the abnormal data, vacant values ​​will appear in the data set. In order to maintain the continuity of the data, these vacant values ​​are interpolated. Common interpolation methods include linear interpolation, polynomial interpolation, and neighboring value interpolation. For example, the linear interpolation method is selected. For vacant values, the linear relationship between the two non-vacant values ​​before and after is used to estimate the vacant value. The exponential smoothing algorithm is used to process the rain data after vacant interpolation to obtain smoothed reference rain data.

[0036] Furthermore, the reference rainwater data is used as the input of the precipitation infiltration boundary, and the precipitation infiltration of the model is adjusted according to the rainfall in the data. After adjusting the boundary conditions, the two-dimensional hydraulic model is run for simulation, and the flood prediction information corresponding to the test area is determined according to the simulation results, such as flood arrival time, flood peak, flood duration, etc. The flood prediction data is mapped to the preset map, and a spatiotemporal evolution diagram is generated to show the evolution of the flood at different times and locations. For example, the flood prediction data can be mapped to the map using geographic information system software, and an animated spatiotemporal evolution diagram can be generated to show the changes of the flood in one day.

[0037] S103, dividing the area to be tested into multiple small areas based on the regional building characteristics corresponding to the area to be tested, and determining the flood risk level corresponding to each small area based on the building characteristics and the spatiotemporal evolution diagram of each small area.

[0038] In one embodiment of the present application, based on the geographic information corresponding to the area to be measured, the area to be measured is divided into a first area to obtain multiple first areas; wherein the geographic information includes at least one of terrain information, landform information and water system distribution information. The building features corresponding to the multiple first areas are obtained, and based on the building features, the multiple first areas are divided into a second area to obtain multiple small areas; wherein the building features include at least one of building density, building type, building height, building material and building age. Based on the spatiotemporal evolution diagram, the exposure of each small area in a flood event is determined. Based on the preset weights, exposure and building features, the flood risk level corresponding to each small area is determined.

[0039] Specifically, the area to be tested is divided into a plurality of first areas according to the geographical information of the area to be tested, such as terrain information, landform information, and water system distribution information. For example, assuming that the area to be tested is a city, the city can be divided into a plurality of first areas, such as mountain areas, plains, river valleys, and water systems such as rivers, lakes, and reservoirs, according to terrain such as mountains, plains, and river valleys, and water systems such as rivers, lakes, and reservoirs. Then, the building features corresponding to each first area are obtained, such as building density, building type, building height, building materials, and building age, and the first area is further divided according to these features to obtain a plurality of smaller areas. For example, in the first area of ​​the plain area, it is further divided according to the building features. For example, a commercial area with high building density and high building height is divided into a small area, and a residential area with low building density and low building height is divided into another small area.

[0040] Furthermore, the spatiotemporal evolution diagram is used to determine the exposure of each small area in a flood event, that is, the degree to which a certain area is affected by the flood in a specific flood event. By analyzing historical flood event data and simulation data, it is determined which small area, such as the commercial area along the river, is more likely to be flooded in a flood event and thus has a higher exposure. Finally, based on the preset weights, exposure and building characteristics, the flood risk level is determined for each small area. The preset weights can be set according to the degree of influence of different building characteristics and exposure on the flood risk. For example, it is assumed that a commercial area with high building density and high building height has a higher risk weight in a flood event. At the same time, combined with the exposure of the area in a flood event, such as frequent flooding, the flood risk level of the small area is determined to be high risk. On the contrary, residential areas with low building density and low building height may have a lower flood risk level.

[0041] In one embodiment of the present application, the flooded water level elevation grid map corresponding to the area to be tested is subtracted from the urban terrain elevation grid to determine the flooded part of the disaster-prone body. Based on the depth of the flooded part, each small area is divided into exposure levels. Based on the damage to the buildings corresponding to each small area, a flood disaster loss curve corresponding to each small area is drawn, and based on the curve, the total value of the disaster loss is determined, and the building feature level of each small area is divided based on the total value. Based on the preset weights, the exposure level and the building feature level are weighted to determine the flood risk level corresponding to each small area.

[0042] Specifically, obtain the inundation water level elevation raster map and the urban terrain elevation raster corresponding to the area to be tested. Both raster maps are represented by the same geographic coordinate system, such as longitude and latitude, and the same resolution. Subtract the inundation water level elevation raster map from the terrain elevation raster map to obtain a new raster map, in which the value of each grid cell represents the inundation depth of the location in the flood event. For example, assuming that the terrain elevation of a grid location is 10 meters, and the flood inundation water level elevation is 15 meters, then the inundation depth of the location is 5 meters. Based on the inundation depth, each small area is divided into exposure levels. The exposure level can be defined according to different ranges of inundation depth, for example, 0-1 meters is low exposure, 1-3 meters is medium exposure, and more than 3 meters is high exposure.

[0043] Furthermore, the damage to the buildings in each small area is evaluated based on factors such as the type, structure, height, age, and inundation depth of the buildings. A flood damage loss curve is drawn for each small area, in which the horizontal axis represents the inundation depth and the vertical axis represents the damage loss. For example, for a small area, when the inundation depth reaches 2 meters, it is expected that 10% of the buildings will be damaged, with a loss value of 1 million yuan; when the inundation depth reaches 4 meters, it is expected that 50% of the buildings will be damaged, with a loss value of 5 million yuan. Based on these data, a flood damage loss curve for the small area is drawn. The total damage loss value of each small area is determined based on the flood damage loss curve. Then, based on this total value and other building characteristics (such as building density, building type, etc.), the building characteristics level of each small area is divided. For example, if the total damage loss value of a small area is very high and the building density of the area is also very large, the building characteristics level of the area can be divided into a high risk level.

[0044] Furthermore, the exposure level and the building feature level are weighted according to the preset weights to determine the flood risk level of each small area. These weights can be set in advance according to specific research objectives and regional characteristics. For example, the weight of the exposure level is set to 0.6, and the weight of the building feature level is set to 0.4. For a small area, its exposure level is medium exposure (score is 2), and the building feature level is high risk level (score is 3). Then, the flood risk level score of the small area is 2×0.6+3×0.4=2.4. According to the set risk level threshold, the flood risk level of the small area is determined.

[0045] In one embodiment of the present application, based on the flood flow, any two first small areas and second small areas in a superior-subordinate relationship are determined as an early warning area pair. Based on the terrain data between the early warning area pair and the flood risk levels corresponding to the two small areas of the early warning area pair, the reference probability of flooding in the second small area when the first small area is flooded is determined. The impact value of each small area on the subordinate small area when a flood occurs is calculated based on the reference probability.

[0046] Specifically, first, determine the path through which the flood flows and the areas with superior-subordinate relationships. Usually, if the flood in a flood area (the first small area) is likely to flow to another area (the second small area), and the two areas have an obvious upstream and downstream relationship in the terrain, then they can be determined as a pair of early warning areas. Suppose there are two adjacent small areas A and B, A is located upstream, B is located downstream, and the flood in A is likely to flow to B. Then, A and B can be determined as a pair of early warning areas. Assume that the flood risk level of area A is high, and the terrain slope between A and B is large, and the elevation difference is also large. These factors all indicate that when A floods, B is more likely to flood, and it is concluded that when A floods, B is more likely to flood. Through the corresponding terrain slope, elevation difference, and distance, etc., a query is performed in the probability database to determine similar pairs of early warning areas, and the corresponding probability of the early warning area pair is used as a reference probability. Among them, the larger the reference probability, the greater the corresponding impact value.

[0047] S104. Based on the flood risk level and the building characteristics of each small area, different flood response information is determined to generate a flood warning strategy.

[0048] In one embodiment of the present application, a query is performed in a flood response information database based on the architectural features of each small area to determine multiple response information corresponding to the architectural features. Based on the flood risk level and the mapping relationship, a first reference response information corresponding to the flood risk level is determined from multiple response information. The rainwater data and the flood risk level corresponding to the area to be tested are input into a preset flood prediction model to obtain flood trend prediction information corresponding to the area to be tested. Based on the flood trend prediction information, a query is performed in the response information database to determine multiple second reference response information. Based on the first reference response information and the second reference response information, a flood warning strategy is generated.

[0049] Specifically, the embodiment of the present application is provided with a database containing architectural features of each small area, such as building type, height, drainage facilities, etc. Then, based on these architectural features, the corresponding response information is queried in the flood response information database. These response information may include emergency evacuation routes, flood prevention measures, drainage system recommendations, etc. Suppose a small area is mainly a low-rise residential area and a school, and the drainage system is old. After querying the database, the following response information may be obtained: start the emergency drainage system, evacuate residents to the school high ground, prepare sandbags and water pumps, etc.

[0050] Furthermore, the flood risk level is usually assessed based on historical data, geographic information and real-time data (such as rainfall, water level, etc.). Through the correspondence between the flood risk level and the response information, the first reference response information that best matches the current risk level is selected from multiple response information according to the assessed flood risk level. For example, if the assessed flood risk level is "medium", the mapping relationship indicates that the response information corresponding to the medium risk level includes strengthening inspections, preparing emergency supplies, etc. These are the first reference response information.

[0051] Furthermore, by inputting the rainfall data and flood risk level of the area to be tested, the model can predict the flood trend of the area, such as the possibility of flood occurrence, peak flow, arrival time, etc. According to the flood trend prediction information, the corresponding response information is queried again in the response information database, and the second reference response information is obtained after querying the database according to the predicted flood trend. Combining the first reference response information and the second reference response information, a comprehensive and specific flood warning strategy is formulated.

[0052] In one embodiment of the present application, the time of change of the flood risk level is determined based on the flood trend prediction information. The flood risk level that changes with time is determined, and based on the changed flood risk level, the corresponding second reference response information is queried in the response information database. The time of change of the flood risk level is matched with the second reference response information corresponding to different times.

[0053] Specifically, the flood risk level change time in the embodiment of the present application refers to the specific time point or time period when the flood risk level transitions from one level to another. Based on the flood trend prediction information, the flood risk level corresponding to each time point or time period can be determined. For each flood risk level, the corresponding second reference response information can be queried in the response information database. For example, when the flood risk level is "low", the second reference response information may include strengthening inspections, preparing basic emergency supplies, etc.; when the flood risk level rises to "medium", the response information may include starting the drainage system, evacuating residents in low-lying areas, etc.; when the flood risk level further rises to "high", the response information may include closing important facilities, transferring important materials, setting up temporary resettlement points, etc. The purpose is to match the flood risk level change time with the second reference response information corresponding to different times to ensure that corresponding response measures can be taken in time when the flood risk level changes.

[0054] Figure 2 This is a schematic diagram of the structure of a flood risk warning device provided in an embodiment of the present application. Figure 2 As shown, it includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: construct a two-dimensional hydraulic model based on the acquired geographic spatial data corresponding to the area to be tested and the regional building feature data corresponding to the area to be tested; obtain rainwater data corresponding to the area to be tested, adjust the boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generate a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested; based on the regional building features corresponding to the area to be tested, the area to be tested is divided into multiple small areas, and based on the building features of each small area and the spatiotemporal evolution diagram, the flood risk level corresponding to each small area is determined; based on the flood risk level and the building features of each small area, different flood response information is determined to generate a flood warning strategy.

[0055] A non-volatile computer storage medium provided in an embodiment of the present application stores computer executable instructions, wherein the computer executable instructions are configured to: construct a two-dimensional hydraulic model based on acquired geographic spatial data corresponding to a test area and regional building feature data corresponding to the test area; acquire rainwater data corresponding to the test area, adjust boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generate a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of floods on the test area; divide the test area into a plurality of small areas based on regional building features corresponding to the test area, and determine the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram; and determine different flood response information based on the flood risk level and the building features of each small area to generate a flood warning strategy.

[0056] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0057] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various changes and variations. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A flood risk early warning method, characterized in that: The method comprises: Based on the acquired geographic spatial data corresponding to the area to be measured and the regional building feature data corresponding to the area to be measured, a two-dimensional hydraulic model is constructed; Acquire rainwater data corresponding to the area to be tested, adjust the boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generate a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested; Based on the regional building features corresponding to the area to be tested, the area to be tested is divided into a plurality of small areas, and based on the building features of each of the small areas and the spatiotemporal evolution diagram, the flood risk level corresponding to each of the small areas is determined; Based on the flood risk level and the building characteristics of each of the small areas, different flood response information is determined to generate a flood warning strategy; The method of dividing the area to be tested into a plurality of small areas based on the regional building features corresponding to the area to be tested, and determining the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram, specifically includes: Based on the geographic information corresponding to the area to be measured, the area to be measured is first divided into a plurality of first areas; wherein the geographic information includes at least one of terrain information, landform information and water system distribution information; Acquire building features corresponding to the plurality of first areas respectively, and perform a second division on the plurality of first areas based on the building features to obtain a plurality of small areas; wherein the building features include at least one of building density, building type, building height, building material, and building age; Based on the spatiotemporal evolution diagram, determining the exposure of each of the small areas to the flood event; Determining the flood risk level corresponding to each of the small areas based on the preset weights, the exposure and the building characteristics; The determining of the flood risk level corresponding to each of the small areas based on the preset weight, the exposure and the building features specifically includes: Subtracting the flooded water level elevation grid map corresponding to the area to be tested from the urban terrain elevation grid to determine the flooded part of the disaster-bearing body; Based on the depth of the submerged portion, each of the small areas is classified into an exposure level; Based on the damage to the buildings corresponding to each of the small areas, a flood disaster loss curve corresponding to each of the small areas is drawn, based on the curve, a total value of the disaster loss is determined, and based on the total value, the building feature level of each of the small areas is classified; Based on the preset weights, the exposure level and the building feature level are weightedly calculated to determine the flood risk level corresponding to each of the small areas.

2. A flood risk early warning method according to claim 1, characterized in that: After determining the flood risk level corresponding to each of the small areas, the method further includes: Based on the flood flow path, any two first small areas and second small areas in a superior-subordinate relationship are determined as a warning area pair; Determining, based on the terrain data between the pair of warning areas and the flood risk levels respectively corresponding to the two small areas in the pair of warning areas, a reference probability of flooding in the second small area when flooding occurs in the first small area; The impact value on the subordinate small areas when flood occurs in each of the small areas is calculated based on the reference probability.

3. A flood risk early warning method according to claim 1, characterized in that: The step of constructing a two-dimensional hydraulic model based on the acquired geographic spatial data corresponding to the area to be measured and the regional building feature data corresponding to the area to be measured specifically includes: Integrate the acquired geospatial data corresponding to the area to be measured; wherein the geospatial data includes at least one of a satellite remote sensing image and a digital elevation model; Performing a flood evolution impact analysis on the acquired building characteristic data, and determining a resistance coefficient based on the flood evolution impact analysis result; wherein the building characteristic data includes at least one of building layout, building height and building material; Based on the acquired drainage network map of the area to be measured and the surface coverage type corresponding to the area to be measured, determining the drainage capacity value and the surface permeability value corresponding to the area to be measured; The two-dimensional hydraulic model is constructed based on the integrated geospatial data, the resistance coefficient, the drainage capacity value and the surface permeability value.

4. A flood risk early warning method according to claim 1, characterized in that: The step of obtaining rainwater data corresponding to the area to be tested, adjusting boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generating a spatiotemporal evolution diagram specifically includes: splicing the rain data based on the timestamps of the rain data to obtain spliced ​​rain data; Detecting abnormal data in the spliced ​​rainwater data, replacing the abnormal data with vacant data, and performing vacant interpolation processing on the vacant data to obtain spliced ​​rainwater data with vacant interpolation; Performing exponential smoothing on the missing interpolated spliced ​​rainwater data to obtain reference rainwater data; Inputting the reference rainwater data into the two-dimensional hydraulic model to adjust the boundary conditions of the two-dimensional hydraulic model; wherein the boundary conditions include at least one of a lateral inflow boundary and a precipitation infiltration boundary; Determining flood prediction information corresponding to the area to be tested based on the simulation data of the two-dimensional hydraulic model; The flood prediction information is mapped to corresponding positions of a preset map to generate the spatiotemporal evolution diagram.

5. A flood risk early warning method according to claim 1, characterized in that: Determining different flood response information based on the flood risk level and the building characteristics of each of the small areas to generate a flood warning strategy specifically includes: Based on the building features of each of the small areas, a query is performed in a flood response information database to determine a plurality of response information corresponding to the building features; Based on the flood risk level and the mapping relationship, determining first reference response information corresponding to the flood risk level from the plurality of response information; Inputting the rainwater data corresponding to the area to be tested and the flood risk level into a preset flood prediction model to obtain flood trend prediction information corresponding to the area to be tested; Based on the flood trend prediction information, querying the response information database to determine a plurality of second reference response information; A flood warning strategy is generated based on the first reference response information and the second reference response information.

6. A flood risk early warning method according to claim 5, characterized in that: The querying in the response information database based on the flood trend prediction information to determine a plurality of second reference response information specifically includes: Based on the flood trend prediction information, determining the time of flood risk level change; Determine the flood risk level that changes with time, and based on the changed flood risk level, query the corresponding second reference response information in the response information database; The flood risk level change time is matched with the second reference response information corresponding to different times.

7. A flood risk warning device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Based on the acquired geographic spatial data corresponding to the area to be measured and the regional building feature data corresponding to the area to be measured, a two-dimensional hydraulic model is constructed; Acquire rainwater data corresponding to the area to be tested, adjust the boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generate a spatiotemporal evolution diagram; wherein the spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested; Based on the regional building features corresponding to the area to be tested, the area to be tested is divided into a plurality of small areas, and based on the building features of each of the small areas and the spatiotemporal evolution diagram, the flood risk level corresponding to each of the small areas is determined; Based on the flood risk level and the building characteristics of each of the small areas, different flood response information is determined to generate a flood warning strategy; The method of dividing the area to be tested into a plurality of small areas based on the regional building features corresponding to the area to be tested, and determining the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram, specifically includes: Based on the geographic information corresponding to the area to be measured, the area to be measured is first divided into a plurality of first areas; wherein the geographic information includes at least one of terrain information, landform information and water system distribution information; Acquire building features corresponding to the plurality of first areas respectively, and perform a second division on the plurality of first areas based on the building features to obtain a plurality of small areas; wherein the building features include at least one of building density, building type, building height, building material, and building age; Based on the spatiotemporal evolution diagram, determining the exposure of each of the small areas to the flood event; Determining the flood risk level corresponding to each of the small areas based on the preset weights, the exposure and the building characteristics; The determining of the flood risk level corresponding to each of the small areas based on the preset weight, the exposure and the building features specifically includes: Subtracting the flooded water level elevation grid map corresponding to the area to be tested from the urban terrain elevation grid to determine the flooded part of the disaster-bearing body; Based on the depth of the submerged portion, each of the small areas is classified into an exposure level; Based on the damage to the buildings corresponding to each of the small areas, a flood disaster loss curve corresponding to each of the small areas is drawn, based on the curve, a total value of the disaster loss is determined, and based on the total value, the building feature level of each of the small areas is classified; Based on the preset weights, the exposure level and the building feature level are weightedly calculated to determine the flood risk level corresponding to each of the small areas.

8. A non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: Based on the acquired geographic spatial data corresponding to the area to be measured and the regional building feature data corresponding to the area to be measured, a two-dimensional hydraulic model is constructed; Acquire rainwater data corresponding to the area to be tested, adjust the boundary conditions of the two-dimensional hydraulic model based on the rainwater data, and generate a spatiotemporal evolution diagram; wherein, The spatiotemporal evolution diagram is related to the impact of the flood on the area to be tested; Based on the regional building features corresponding to the area to be tested, the area to be tested is divided into a plurality of small areas, and based on the building features of each of the small areas and the spatiotemporal evolution diagram, the flood risk level corresponding to each of the small areas is determined; Based on the flood risk level and the building characteristics of each of the small areas, different flood response information is determined to generate a flood warning strategy; The method of dividing the area to be tested into a plurality of small areas based on the regional building features corresponding to the area to be tested, and determining the flood risk level corresponding to each small area based on the building features of each small area and the spatiotemporal evolution diagram, specifically includes: Based on the geographic information corresponding to the area to be measured, the area to be measured is first divided into a plurality of first areas; wherein the geographic information includes at least one of terrain information, landform information and water system distribution information; Acquire building features corresponding to the plurality of first areas respectively, and perform a second division on the plurality of first areas based on the building features to obtain a plurality of small areas; wherein the building features include at least one of building density, building type, building height, building material, and building age; Based on the spatiotemporal evolution diagram, determining the exposure of each of the small areas to the flood event; Determining the flood risk level corresponding to each of the small areas based on the preset weights, the exposure and the building characteristics; The determining of the flood risk level corresponding to each of the small areas based on the preset weight, the exposure and the building features specifically includes: Subtracting the flooded water level elevation grid map corresponding to the area to be tested from the urban terrain elevation grid to determine the flooded part of the disaster-bearing body; Based on the depth of the submerged portion, each of the small areas is classified into an exposure level; Based on the damage to the buildings corresponding to each of the small areas, a flood disaster loss curve corresponding to each of the small areas is drawn, based on the curve, a total value of the disaster loss is determined, and based on the total value, the building feature level of each of the small areas is classified; Based on the preset weights, the exposure level and the building feature level are weightedly calculated to determine the flood risk level corresponding to each of the small areas.

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