CN116468269B - Flood high-risk area identification method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a flood high-risk area identification method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring grid data of the underlying surface in the research range; calculating the total amount of surface water in the research range based on the raster data; acquiring a ground water depth vector diagram based on the total ground water amount; generating a grid buffer area according to vector data of the underground space; and superposing the ground ponding depth vector diagram and the grid buffer area, and judging the area as a high-risk area when the superposed area meets the preset high-risk area condition. The embodiment of the invention can rapidly, scientifically and accurately identify the high-risk area of the flood, and fills up the blank of the identification method of the high-risk area of the large-scale flood.
Description
Technical Field
The invention relates to the technical field of urban inland inundation, in particular to a method, a device, equipment and a storage medium for identifying a high-risk area of inundation.
Background
In recent years, the frequent occurrence of flood disasters caused by extreme storm constitutes a great threat to the life and property safety of people, and the tragic training fully proves that a set of scientific and accurate flood high-risk area identification method is urgently needed in cities, so that scientific basis is provided for urban water safety management, major project site selection, pre-disaster early warning and post-disaster rescue.
At present, a method for identifying urban flood risk areas is mainly a hydrodynamic model method for simulating municipal drainage pipe networks or river channels and sluice pump stations, the accuracy requirement of the method on boundary conditions is extremely high, the method is generally applied to small-scale water safety management, a method for identifying urban large-scale flood high-risk areas is lacking, the influence of urban vertical conditions and ground hardening degree on surface rainwater runoffs and confluence is not fully considered in the existing small-scale technical method, particularly under the condition of extremely heavy storm, flood risks caused by urban underpass overpass, tunnels, subway station halls and other small-concave facilities due to the lack of natural rainwater overflow are not fully considered, and the places where casualties occur are basically located in tunnels and subways, so that the existing technical method can generate a certain degree of distortion.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for identifying a high-risk area of flood, which are used for solving the problem that a method for identifying the high-risk area of urban large-scale flood is not available in the prior art, and the problem that the identification of the high-risk area of flood is distorted to a certain extent by adopting a small-scale technical method.
In order to achieve the above object, an embodiment of the present invention provides a method for identifying a flood high risk area, including:
acquiring grid data of the underlying surface in the research range;
calculating the total amount of surface water in the research range based on the raster data;
acquiring a ground water depth vector diagram based on the total ground water amount;
generating a grid buffer area according to vector data of the underground space;
and superposing the ground ponding depth vector diagram and the grid buffer area, and judging the area as a high-risk area when the superposed area meets the preset high-risk area condition.
As an improvement of the above-mentioned scheme, the acquiring the grid data of the underlying surface in the research range includes:
acquiring a remote sensing image in a research range;
taking a preset underlying surface element in the remote sensing image as a training sample to generate a classification characteristic file;
and carrying out pixel statistics on the remote sensing image based on the classification characteristic file, and extracting a grid surface containing the underlying surface element in the remote sensing image.
As an improvement of the above-mentioned solution, the calculating the total amount of the surface water in the investigation range based on the raster data includes:
calculating the hour-by-hour rainfall in the research range;
calculating the comprehensive runoff coefficient by adopting a weighted average method according to the runoff coefficients of different underlying surface types and the underlying surface area;
multiplying the hour-by-hour rainfall by the comprehensive runoff coefficient to obtain an hour-by-hour net rainfall in the research range;
calculating the hourly drainage of the drainage facility according to the sluice width and the pump station flow in the research range;
calculating the total water storage capacity within the rainfall period according to the hour-by-hour net rainfall and the drainage;
calculating the water storage capacity of the river and the lake according to the area of the river and the lake water areas in the research range;
subtracting the water storage capacity of the river and the lake from the total water storage capacity to obtain the water quantity of the ground area in the research range.
As an improvement of the above-mentioned aspect, said calculating a total water storage amount over a designed rainfall duration from said hour-by-hour net rainfall and said drainage amount includes:
when the hour-by-hour net rainfall is larger than the drainage amount, subtracting the drainage amount from the hour-by-hour net rainfall to obtain an hour-by-hour water storage amount;
when the hour-by-hour net rainfall is less than or equal to the drainage, the hour-by-hour water storage is 0;
multiplying all the hour-by-hour water storage amounts in the designed rainfall duration with the area of the research range respectively, and adding the multiplied results to obtain the total water storage amount in the designed rainfall duration.
As an improvement of the above solution, the obtaining a surface water depth vector map based on the total surface water amount includes:
constructing a surface flooding three-dimensional digital model in the research range;
under the condition that the total amount of the surface water is the total amount, based on a surface confluence model and the surface flooding three-dimensional digital model, simulating and calculating the depth of the surface water in the research range;
and carrying out rasterization treatment on the surface water depth to generate a surface water depth vector diagram.
As an improvement of the above solution, the generating a grid buffer according to vector data of the underground space includes:
acquiring vector data of an underground space in the research range;
and converting the coordinates of the vector data into coordinates under a preset coordinate system, and generating a grid buffer area.
As an improvement of the above-described scheme, the flooding high-risk area condition includes:
the ground ponding depth is larger than the first preset depth, and comprises an area of any facility of a tunnel, a culvert and a bridge tunnel;
the ground ponding depth is greater than or equal to a second preset depth, and comprises the area of the subway station;
the ground water accumulation depth is greater than or equal to the area with the third preset depth;
the first preset depth is smaller than the second preset depth, and the second preset depth is smaller than the third preset depth.
The embodiment of the invention also provides a device for identifying the flood high-risk area, which comprises the following steps:
the grid data acquisition module is used for acquiring grid data of the underlying surface in the research range;
the total surface water calculation module is used for calculating the total surface water in the research range based on the grid data;
the ground water depth vector diagram acquisition module is used for acquiring a ground water depth vector diagram based on the total ground water amount;
the grid buffer zone generating module is used for generating a grid buffer zone according to vector data of the underground space;
and the flood high risk area identification module is used for superposing the ground ponding depth vector diagram and the grid buffer area, and judging that the area is a flood high risk area when the superposed area meets the preset condition of the flood high risk area.
The embodiment of the invention also provides electronic equipment, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor realizes the flood high-risk area identification method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium including a stored computer program; wherein the computer program, when running, controls the device in which the computer readable storage medium is located to perform the flood high risk zone identification method as described above.
Compared with the prior art, the method, the device, the equipment and the storage medium for identifying the high-risk area of the flood provided by the embodiment of the invention acquire the depth vector diagram of the ground water through the calculated total amount of the ground water, then generate the grid buffer area according to the vector data of the underground space, finally stack the depth vector diagram of the ground water and the grid buffer area, and judge the high-risk area of the flood after stacking. Therefore, the embodiment of the invention can rapidly, scientifically and accurately identify the high-risk area of the flood, makes up the blank of the identification method of the high-risk area of the large-scale flood, and provides scientific basis for urban water safety control, major project site selection, pre-disaster early warning and post-disaster rescue. In addition, the embodiment of the invention calculates the ground ponding depth through simulation, is more in line with the disaster formation process of extra heavy storm, and enables the identification of the flood high-risk area to be more accurate.
Drawings
Fig. 1 is a flowchart of a method for identifying a flood high risk area according to an embodiment of the present invention;
fig. 2 is a structural block diagram of a flood high risk area identification device provided by an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for identifying a high risk area of flooding, where the method for identifying a high risk area of flooding includes:
s1, acquiring raster data of an underlying surface in a research range;
s2, calculating the total amount of the surface water in the research range based on the grid data;
s3, acquiring a ground water depth vector diagram based on the total ground water amount;
s4, generating a grid buffer area according to vector data of the underground space;
s5, superposing the ground ponding depth vector diagram and the grid buffer area;
and S6, when the overlapped area meets the preset condition of the high-risk area, judging that the area is the high-risk area.
In an alternative embodiment, the step S1 of acquiring the grid data of the underlying surface in the study range includes:
acquiring a remote sensing image in a research range;
taking a preset underlying surface element in the remote sensing image as a training sample to generate a classification characteristic file;
and carrying out pixel statistics on the remote sensing image based on the classification characteristic file, and extracting a grid surface containing the underlying surface element in the remote sensing image.
It will be appreciated that the underlying surface element comprises at least: green land, road, bare land, building surface and water system.
The method comprises the steps of obtaining TIF data of a high-precision satellite remote sensing image in a research range, and taking underlying surface elements such as green land, road, bare land, building surface, water system and the like in the remote sensing image as training samples to form a classification characteristic file; and (3) extracting a classification characteristic file by adopting a GIS maximum likelihood classification method, performing pixel statistics on the remote sensing image, and extracting a grid surface containing the underlying surface elements in the remote sensing image.
In an alternative embodiment, the calculating the total amount of surface water in the study area based on the raster data in step S2 includes:
calculating the hour-by-hour rainfall in the research range;
calculating the comprehensive runoff coefficient by adopting a weighted average method according to the runoff coefficients of different underlying surface types and the underlying surface area;
multiplying the hour-by-hour rainfall by the comprehensive runoff coefficient to obtain an hour-by-hour net rainfall in the research range;
calculating the hourly drainage of the drainage facility according to the sluice width and the pump station flow in the research range;
calculating the total water storage capacity within the rainfall period according to the hour-by-hour net rainfall and the drainage;
calculating the water storage capacity of the river and the lake according to the area of the river and the lake water areas in the research range;
subtracting the water storage capacity of the river and the lake from the total water storage capacity to obtain the water quantity of the ground area in the research range.
It will be appreciated that the underlying surface area is interpreted based on the remote sensing image, i.e., the raster data includes the underlying surface area, which includes at least the area of the river or lake water.
Exemplary, the hour-by-hour rainfall H within the research scope under the design criteria is calculated using the Chicago rain type or the same frequency amplification method i (rainfall in mm for the ith hour);
according to the runoff coefficients of different underlying surface types, combining different underlying surface areas of remote sensing interpretation, and calculating a comprehensive runoff coefficient psi by adopting a weighted average method; the runoff coefficients of different underlying surface types can be referred to the outdoor drainage design standard (GB 50014-2021);
calculating the net rainfall H of the research range hour by hour Clean i (Unit: m 3 ) I.e. net rainfall H at the ith hour Clean i ＝H i ×ψ；
According to the acquired sluice width and pump station flow in the research range, converting the drainage quantity H of the drainage facility per hour Row of rows (unit: mm/h);
calculating the total water storage quantity Q in the rainfall period Total storage (Unit: m 3 )；
According to the area of the river and lake water area interpreted by remote sensing, calculating the water storage quantity Q of the river and the lake River and lake reservoir (Unit: m 3 )；
Calculating ground ponding under design standardQuantity Q Total (S) ＝Q Total storage -Q River and lake reservoir 。
In an alternative embodiment, said calculating a total water storage volume over a designed rainfall duration based on said hour-by-hour net rainfall and said drainage volume comprises:
when the hour-by-hour net rainfall is larger than the drainage amount, subtracting the drainage amount from the hour-by-hour net rainfall to obtain an hour-by-hour water storage amount;
when the hour-by-hour net rainfall is less than or equal to the drainage, the hour-by-hour water storage is 0;
multiplying all the hour-by-hour water storage amounts in the designed rainfall duration with the area of the research range respectively, and adding the multiplied results to obtain the total water storage amount in the designed rainfall duration.
Exemplary, calculating the total Water storage quantity Q over the design rainfall period Total storage (Unit: m 3 ) Comprising the following steps:
when H is Clean i ＞H Row of rows ，H Total i of storage (i hour water storage) =h Clean i -H Row of rows ；
When H is Clean i ＜H Row of rows ，H Total i of storage ＝0；
Then the first time period of the first time period,
s is the area of the research range, and h is the duration of rainfall.
In an optional embodiment, the step S3 of obtaining a surface water depth vector map based on the total surface water amount includes:
constructing a surface flooding three-dimensional digital model in the research range;
under the condition that the total amount of the surface water is the total amount, based on a surface confluence model and the surface flooding three-dimensional digital model, simulating and calculating the depth of the surface water in the research range;
and carrying out rasterization treatment on the surface water depth to generate a surface water depth vector diagram.
Illustratively, on the basis of a high-resolution digital elevation model DEM, a remote sensing interpreted house building is defined as a water-blocking structure, and finally, gridding treatment is carried out on DEM data to obtain a three-dimensional digital model for constructing the earth surface flood;
total amount of accumulated water Q on the ground Total (S) Under the condition, based on a three-dimensional digital model of surface flooding and a SWMM surface confluence model, the ground water depth H in the research range is simulated and calculated Ponding water (unit: m);
ground ponding depth H by GIS Ponding water Gridding to treat the surface water depth H Ponding water And endowing the raster data with field attributes, and finally forming a ground water depth vector diagram.
In an alternative embodiment, the generating the grid buffer according to the vector data of the underground space in step S4 includes:
acquiring vector data of an underground space in the research range;
and converting the coordinates of the vector data into coordinates under a preset coordinate system, and generating a grid buffer area.
The preset coordinate system is an urban coordinate system in which the research scope is located; firstly, vector data of underground space POIs such as tunnels, culverts, bridge tunnels, subway stations and the like are obtained by using Python, then coordinate deviation correction is converted into a coordinate system of a city where a research range is located, and finally a grid buffer area is generated.
In an alternative embodiment, the flooding high-risk area condition in step S5 includes:
the ground ponding depth is larger than the first preset depth, and comprises an area of any facility of a tunnel, a culvert and a bridge tunnel;
the ground ponding depth is greater than or equal to a second preset depth, and comprises the area of the subway station;
the ground water accumulation depth is greater than or equal to the area with the third preset depth;
the first preset depth is smaller than the second preset depth, and the second preset depth is smaller than the third preset depth.
Preferably, the first preset depth is 0.15m, the second preset depth is 0.4m, and the second preset depth is 0.6m.
It can be appreciated that the vector data in the grid buffer and the ground water depth vector map are superimposed, and the area meeting the condition of the high risk area is defined as a high risk area.
In an alternative embodiment, after the surface water depth vector diagram and the grid buffer area are overlapped, generating a flood high-risk area vector diagram spot based on the condition of the flood high-risk area;
it will be appreciated that the pattern is determined to be a flood high risk area.
Referring to fig. 2, fig. 2 is a block diagram of a flooding high-risk area identifying apparatus 10 according to an embodiment of the present invention, where the flooding high-risk area identifying apparatus 10 includes:
a raster data acquisition module 11, configured to acquire raster data of an underlying surface in a research range;
a total surface water calculation module 12, configured to calculate a total surface water in the research area based on the raster data;
the ground water depth vector diagram acquisition module 13 is used for acquiring a ground water depth vector diagram based on the total ground water amount;
a grid buffer generation module 14 for generating a grid buffer from vector data of the underground space;
and the flood high risk area identification module 15 is used for superposing the ground ponding depth vector diagram and the grid buffer area and judging that the area is a flood high risk area when the superposed area meets the preset condition of the flood high risk area.
Preferably, the acquiring the grid data of the underlying surface in the research range includes:
acquiring a remote sensing image in a research range;
taking a preset underlying surface element in the remote sensing image as a training sample to generate a classification characteristic file;
and carrying out pixel statistics on the remote sensing image based on the classification characteristic file, and extracting a grid surface containing the underlying surface element in the remote sensing image.
Preferably, said calculating the total amount of surface water in said investigation region based on said raster data comprises:
calculating the hour-by-hour rainfall in the research range;
calculating the comprehensive runoff coefficient by adopting a weighted average method according to the runoff coefficients of different underlying surface types and the underlying surface area;
multiplying the hour-by-hour rainfall by the comprehensive runoff coefficient to obtain an hour-by-hour net rainfall in the research range;
calculating the hourly drainage of the drainage facility according to the sluice width and the pump station flow in the research range;
calculating the total water storage capacity within the rainfall period according to the hour-by-hour net rainfall and the drainage;
calculating the water storage capacity of the river and the lake according to the area of the river and the lake water areas in the research range;
subtracting the water storage capacity of the river and the lake from the total water storage capacity to obtain the water quantity of the ground area in the research range.
Preferably, said calculating a total water storage volume over a designed rainfall duration from said hour-by-hour net rainfall and said drainage volume comprises:
when the hour-by-hour net rainfall is larger than the drainage amount, subtracting the drainage amount from the hour-by-hour net rainfall to obtain an hour-by-hour water storage amount;
when the hour-by-hour net rainfall is less than or equal to the drainage, the hour-by-hour water storage is 0;
multiplying all the hour-by-hour water storage amounts in the designed rainfall duration with the area of the research range respectively, and adding the multiplied results to obtain the total water storage amount in the designed rainfall duration.
Preferably, the acquiring the surface water depth vector map based on the total surface water amount includes:
constructing a surface flooding three-dimensional digital model in the research range;
under the condition that the total amount of the surface water is the total amount, based on a surface confluence model and the surface flooding three-dimensional digital model, simulating and calculating the depth of the surface water in the research range;
and carrying out rasterization treatment on the surface water depth to generate a surface water depth vector diagram.
Preferably, the generating the grid buffer according to the vector data of the underground space includes:
acquiring vector data of an underground space in the research range;
and converting the coordinates of the vector data into coordinates under a preset coordinate system, and generating a grid buffer area.
Preferably, the flood high-risk zone conditions include:
the ground ponding depth is larger than the first preset depth, and comprises an area of any facility of a tunnel, a culvert and a bridge tunnel;
the ground ponding depth is greater than or equal to a second preset depth, and comprises the area of the subway station;
the ground water accumulation depth is greater than or equal to the area with the third preset depth;
the first preset depth is smaller than the second preset depth, and the second preset depth is smaller than the third preset depth.
It should be noted that, the working process of each module in the flood high-risk area identifying apparatus 10 according to the embodiment of the present invention may refer to the working process of the flood high-risk area identifying method according to the foregoing embodiment, which is not described herein.
Embodiments of the present invention also provide a computer-readable storage medium including a stored computer program; wherein the computer program, when running, controls a device in which the computer readable storage medium is located to perform the flood high risk zone identification method according to any one of the embodiments described above.
Referring to fig. 3, fig. 3 is a block diagram of an electronic device 20 according to an embodiment of the present invention, where the electronic device 20 includes: a processor 21, a memory 22 and a computer program stored in said memory 22 and executable on said processor 21. The processor 21, when executing the computer program, implements the steps in the above-described embodiments of the flood high-risk zone identification method. Alternatively, the processor 21 may implement the functions of the modules/units in the above-described device embodiments when executing the computer program.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used to describe the execution of the computer program in the electronic device 20.
The electronic device 20 may be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The electronic device 20 may include, but is not limited to, a processor 21, a memory 22. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the electronic device 20 and is not meant to be limiting of the electronic device 20, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device 20 may also include input-output devices, network access devices, buses, etc.
The processor 21 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 21 is a control center of the electronic device 20, and connects various parts of the entire electronic device 20 using various interfaces and lines.
The memory 22 may be used to store the computer program and/or module, and the processor 21 may implement various functions of the electronic device 20 by executing or executing the computer program and/or module stored in the memory 22, and invoking data stored in the memory 22. The memory 22 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the integrated modules/units of the electronic device 20 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the method embodiments described above when executed by the processor 21. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Compared with the prior art, the method, the device, the equipment and the storage medium for identifying the high-risk area of the flood provided by the embodiment of the invention acquire the depth vector diagram of the ground water through the calculated total amount of the ground water, then generate the grid buffer area according to the vector data of the underground space, finally stack the depth vector diagram of the ground water and the grid buffer area, and judge the high-risk area of the flood after stacking. Therefore, the embodiment of the invention can rapidly, scientifically and accurately identify the high-risk area of the flood, makes up the blank of the identification method of the high-risk area of the large-scale flood, and provides scientific basis for urban water safety control, major project site selection, pre-disaster early warning and post-disaster rescue. In addition, the embodiment of the invention calculates the ground ponding depth through simulation, is more in line with the disaster formation process of extra heavy storm, and enables the identification of the flood high-risk area to be more accurate.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (8)
1. A method for identifying a flood high risk zone, comprising:
acquiring grid data of the underlying surface in the research range;
calculating the total amount of surface water in the research range based on the raster data;
acquiring a ground water depth vector diagram based on the total ground water amount;
generating a grid buffer area according to vector data of the underground space;
overlapping the ground ponding depth vector diagram and the grid buffer area, and judging the area as a high-risk area when the overlapped area meets the preset high-risk area condition;
wherein, based on the raster data, the calculating the total amount of the surface water in the research range includes:
calculating the hour-by-hour rainfall in the research range;
calculating the comprehensive runoff coefficient by adopting a weighted average method according to the runoff coefficients of different underlying surface types and the underlying surface area;
multiplying the hour-by-hour rainfall by the comprehensive runoff coefficient to obtain an hour-by-hour net rainfall in the research range;
calculating the hourly drainage of the drainage facility according to the sluice width and the pump station flow in the research range;
calculating the total water storage capacity within the rainfall period according to the hour-by-hour net rainfall and the drainage;
calculating the water storage capacity of the river and the lake according to the area of the river and the lake water areas in the research range;
subtracting the water storage capacity of the river and the lake from the total water storage capacity to obtain the water capacity of the ground area in the research range;
wherein, based on the total amount of the surface water, obtain the surface water depth vector diagram, include:
constructing a surface flooding three-dimensional digital model in the research range;
under the condition that the total amount of the surface water is the total amount, based on a surface confluence model and the surface flooding three-dimensional digital model, simulating and calculating the depth of the surface water in the research range;
and carrying out rasterization treatment on the surface water depth to generate a surface water depth vector diagram.
2. The flood high risk area identification method of claim 1, wherein the acquiring the grid data of the underlying surface within the research scope comprises:
acquiring a remote sensing image in a research range;
taking a preset underlying surface element in the remote sensing image as a training sample to generate a classification characteristic file;
and carrying out pixel statistics on the remote sensing image based on the classification characteristic file, and extracting a grid surface containing the underlying surface element in the remote sensing image.
3. The method of identifying a high risk area for flooding of claim 1, wherein said calculating a total water storage volume over a designed rainfall duration from said hour-by-hour net rainfall and said drainage volume comprises:
when the hour-by-hour net rainfall is larger than the drainage amount, subtracting the drainage amount from the hour-by-hour net rainfall to obtain an hour-by-hour water storage amount;
when the hour-by-hour net rainfall is less than or equal to the drainage, the hour-by-hour water storage is 0;
multiplying all the hour-by-hour water storage amounts in the designed rainfall duration with the area of the research range respectively, and adding the multiplied results to obtain the total water storage amount in the designed rainfall duration.
4. The flood high risk zone identification method of claim 1, wherein generating a grid buffer zone from vector data of the subsurface space comprises:
acquiring vector data of an underground space in the research range;
and converting the coordinates of the vector data into coordinates under a preset coordinate system, and generating a grid buffer area.
5. The flood high risk zone identification method of claim 1, wherein the flood high risk zone conditions comprise:
the ground ponding depth is larger than the first preset depth, and comprises an area of any facility of a tunnel, a culvert and a bridge tunnel;
the ground ponding depth is greater than or equal to a second preset depth, and comprises the area of the subway station;
the ground water accumulation depth is greater than or equal to the area with the third preset depth;
the first preset depth is smaller than the second preset depth, and the second preset depth is smaller than the third preset depth.
6. A flood high risk zone identification device, comprising:
the grid data acquisition module is used for acquiring grid data of the underlying surface in the research range;
the total surface water calculation module is used for calculating the total surface water in the research range based on the grid data;
the ground water depth vector diagram acquisition module is used for acquiring a ground water depth vector diagram based on the total ground water amount;
the grid buffer zone generating module is used for generating a grid buffer zone according to vector data of the underground space;
the flood high risk area identification module is used for superposing the ground ponding depth vector diagram and the grid buffer area, and judging that the area is a flood high risk area when the superposed area meets the preset condition of the flood high risk area;
wherein, based on the raster data, the calculating the total amount of the surface water in the research range includes:
calculating the hour-by-hour rainfall in the research range;
calculating the comprehensive runoff coefficient by adopting a weighted average method according to the runoff coefficients of different underlying surface types and the underlying surface area;
multiplying the hour-by-hour rainfall by the comprehensive runoff coefficient to obtain an hour-by-hour net rainfall in the research range;
calculating the hourly drainage of the drainage facility according to the sluice width and the pump station flow in the research range;
calculating the total water storage capacity within the rainfall period according to the hour-by-hour net rainfall and the drainage;
calculating the water storage capacity of the river and the lake according to the area of the river and the lake water areas in the research range;
subtracting the water storage capacity of the river and the lake from the total water storage capacity to obtain the water capacity of the ground area in the research range;
wherein, based on the total amount of the surface water, obtain the surface water depth vector diagram, include:
constructing a surface flooding three-dimensional digital model in the research range;
under the condition that the total amount of the surface water is the total amount, based on a surface confluence model and the surface flooding three-dimensional digital model, simulating and calculating the depth of the surface water in the research range;
and carrying out rasterization treatment on the surface water depth to generate a surface water depth vector diagram.
7. An electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the flood high-risk zone identification method of any one of claims 1-5 when the computer program is executed.
8. A computer readable storage medium, wherein the computer readable storage medium comprises a stored computer program; wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the flood high risk zone identification method according to any one of claims 1 to 5.
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