CN115358650B - Flood disaster emergency risk avoiding and material real-time allocation method - Google Patents
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Abstract
The invention relates to the technical field of intelligent decision, in particular to an emergency risk avoiding and material real-time allocation method for flood disasters. The method comprises the following steps: acquiring original sampling data; constructing a flood simulation model; generating dynamic simulation process data of flood according to the flood simulation model; generating a maximum flood submerging water depth grid map according to the flood dynamic simulation process data; constructing a rapid and intelligent flood inundation prediction model according to the maximum flood inundation water depth grid map, and generating simulation process data of flood inundation; generating disaster-affected data according to the flooding simulation process data; generating danger-avoiding transfer real-time data and scheduling decision data according to the disaster-suffered data; constructing a material allocation model according to risk avoidance transfer real-time analysis and scheduling decision analysis, and generating material allocation real-time data and scheduling decision data according to the material allocation model; the method takes flood simulation, flood real-time prediction, risk avoiding transfer analysis and material allocation analysis as core technical processes to support flood emergency flood control decisions.
Description
Technical Field
The invention relates to the technical field of intelligent decision, in particular to an emergency risk avoiding and material real-time allocation method for flood disasters.
Background
Flood disasters are one of the most common natural disasters in China. The comprehensive improvement of the disaster prevention, reduction and relief capacity of flood disasters is a key construction task for water safety guarantee. Emergency danger avoiding and emergency material allocation of personnel in flood affected areas are important non-engineering measures for guaranteeing life and property safety of people. Most of the traditional risk avoiding transfer or material allocation is static, and a risk avoiding arrangement plan, a material arrangement plan and a material allocation plan are planned and compiled in advance according to the disaster influence envelope range.
In practical application, the static plan has limited guidance benefit and poor application effect due to lack of disaster dynamic development process and consideration of supply and demand factors. The method and the system combine a flood forecasting model based on hydrodynamic force, a disaster area risk avoiding transfer model and a material demand forecasting and allocating model, comprehensively realize real-time simulation and research and judgment of flood disaster situation, and realize real-time and dynamic analysis of risk avoiding transfer and material allocation under the forecast disaster situation. The technical achievement provides powerful technical support for various flood disaster emergency decision scenes and has wide practical significance.
Disclosure of Invention
The invention provides a flood disaster emergency risk avoiding and real-time material allocation method for solving the technical problems;
in one embodiment of the invention, the method comprises the following steps:
step S1: acquiring original sampling data, and constructing a basic model data set according to the original sampling data, wherein the basic model data set comprises a one-dimensional river surge model, a two-dimensional earth surface model and an underground pipe network drainage model;
step S2: constructing a flood simulation model according to the basic model data set;
and step S3: generating flood dynamic simulation process data according to the flood simulation model;
and step S4: generating a maximum flood submerging water depth grid map according to the flood dynamic simulation process data;
step S5: constructing a flood submerging rapid intelligent prediction model according to the maximum flood submerging water depth grid map, and generating flood submerging simulation process data according to the flood submerging rapid intelligent prediction model;
step S6: generating disaster-affected data according to the flooding simulation process data;
step S7: generating danger avoiding transfer real-time data and scheduling decision data according to the disaster-suffered data;
step S8: and constructing a material allocation model according to risk avoidance transfer real-time analysis and scheduling decision analysis, and generating material allocation real-time data and scheduling decision data according to the material allocation model to execute material allocation.
The method comprises the steps of acquiring original sampling data and analyzing and constructing a basic model data set according to the original sampling data; constructing flood dynamic simulation process data according to the basic model data set; generating flood dynamic simulation process data according to the flood simulation model; generating a maximum flood submerging water depth grid map according to the flood dynamic simulation process data; constructing a flood inundation rapid intelligent prediction model according to the maximum flood inundation water depth grid map, and generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model; generating disaster-stricken data according to the flooding simulation process data; generating real-time analysis and scheduling decisions of risk avoidance transfer according to the disaster-suffered data; the material allocation model is constructed according to real-time risk transfer analysis and scheduling decision analysis, and the real-time material allocation analysis and scheduling decision are generated according to the material allocation model, so that technical support is provided for flood disaster emergency risk avoiding scheduling decision and material allocation when flood disasters occur, and efficient and convenient disaster area rescue is provided.
In an embodiment of the present invention, step S1 specifically includes:
acquiring original sampling data, wherein the original sampling data comprises position information, basic geographic information data, hydraulic structure basic data and scheduling scheme data, hydrological and historical disaster data and urban rainwater pipe network data, the basic geographic information data comprises river water system data, river terrain data, surface terrain data and land utilization data, the land utilization data comprises house data, road data and railway data, the hydraulic structure basic data and scheduling scheme data comprises sluice data, pump station data, embankment data, culvert data and water conservancy junction data, the hydrological and historical disaster data comprises historically measured rainfall data, historical flood flooding water level data and historical flood flooding range data, and the urban rainwater pipe network data comprises pipe network data and water outlet data;
analyzing according to the position information, the surface topographic data and the land utilization data, and performing production convergence calculation by adopting a comprehensive unit line method so as to construct a hydrological model of the non-built area;
generating river network topological relation data, river surge section assigned data, gate dam hydraulic structure parameters and scheduling regulation data according to river water system data, river terrain data, hydraulic structure basic data and scheduling scheme data;
analyzing according to the topological relation data of the river network, the assigned value data of the river section, the parameters of the hydraulic structure of the gate dam and the scheduling regulation data, thereby constructing a one-dimensional river model;
performing irregular mesh generation on the surface terrain data to generate irregular mesh surface terrain data;
encrypting the irregular grid ground surface terrain data, road data, railway data, sluice data, pump station data, embankment data and water conservancy junction data to generate a middle two-dimensional ground surface model;
constructing a two-dimensional ground surface model according to the middle two-dimensional ground surface model, the position information, the house data, the road data, the dike data and the culvert data;
constructing an underground pipe network drainage model according to the pipe network data, the drainage port data and the pump station data;
according to the embodiment, a non-built-up area hydrological model is constructed according to basic geographic information data analysis, a one-dimensional river surge model is constructed according to basic geographic information data, hydraulic structure basic data and scheduling data analysis, a two-dimensional surface model is constructed according to surface topographic data, hydraulic structure basic data and scheduling scheme data analysis, and an underground pipe network drainage model is constructed according to urban rainwater pipe network data analysis, so that a basic model data set is constructed according to multi-source and multi-type original sampling data, and premise preparation is made for constructing a flood simulation model in the next step.
In an embodiment of the present invention, step S2 specifically includes:
constructing a middle flooding simulation model according to the hydrological model, the one-dimensional river channel model, the two-dimensional earth surface model and the underground pipe network drainage model of the non-built area;
and carrying out parameter calibration, verification and inspection on the flood simulation model according to hydrology and historical disaster data to generate the flood simulation model.
According to the embodiment, the intermediate flood simulation model is analyzed and constructed according to the basic data set, and the parameter calibration, verification and inspection are carried out on the intermediate flood simulation model, so that the flood simulation model is generated, and preconditions are provided for the next step of generating flood dynamic simulation process data.
In an embodiment of the present invention, step S3 specifically includes:
generating a flood encounter situation acquisition control;
acquiring flood encounter scenario data through the flood encounter scenario acquisition control, wherein the flood encounter scenario data comprises flood data, tide data, flood embankment data and scheduling data;
generating typical situation condition directory data of flood disasters according to the flood encounter situation data;
and generating flood dynamic simulation process data for the flood simulation model according to the typical situation condition catalog data of the flood disaster.
According to the method and the device, the flood encounter situation data is obtained through the flood encounter situation generation obtaining control, and the flood disaster typical situation condition catalogue data is generated through the flood encounter situation data analysis, so that the flood simulation model is analyzed to generate the flood dynamic simulation process data, and precondition preparation is made for generating the maximum flood submerging water depth raster map in the next step.
In an embodiment of the present invention, step S4 specifically includes:
generating space-time raster data according to the flood dynamic simulation process data;
generating a single grid point data set according to the space-time grid data;
and combining the single grid point data sets to generate a maximum flooding water depth grid map of the flooding.
According to the embodiment, the space-time raster data are generated according to the flood dynamic simulation process data, and the space-time raster data are analyzed to generate the single-raster-point data set, so that the maximum flooding water depth raster map is generated in a combined mode, and the premise preparation is made for the next generation of the flood flooding simulation process data.
In an embodiment of the present invention, step S5 specifically includes:
generating a rainstorm data acquisition control;
acquiring rainstorm data through a rainstorm data acquisition control, wherein the rainstorm data comprises typical working condition rainstorm data and atypical working condition rainstorm data;
generating a space-time rainfall grid map according to typical working condition rainstorm data;
constructing an intermediate intelligent prediction model according to the space-time rainfall grid map, the original sampling data and the maximum flooding water depth grid map of the flooding;
generating a maximum flooding water depth grid map of the atypical working condition flooding according to the atypical working condition rainstorm data;
generating a flood submerging fast intelligent prediction model for the intermediate intelligent prediction model according to the atypical working condition flood maximum submerging water depth grid map;
and generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model.
The embodiment acquires the rainstorm data by generating a rainstorm data acquisition control and acquiring the rainstorm data through the rainstorm data acquisition control; generating a space-time rainfall grid diagram according to typical working condition rainstorm data; constructing an intermediate intelligent prediction model according to the space-time rainfall grid map, the original sampling data and the maximum flooding water depth grid map of the flooding; generating a maximum flooding water depth grid map of the atypical working condition flooding according to the atypical working condition rainstorm data; generating a flood submerging fast intelligent prediction model for the intermediate intelligent prediction model according to the atypical working condition flood maximum submerging water depth grid map; and generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model, thereby preparing for generating disaster-affected data in the next step.
In an embodiment of the present invention, step S6 specifically includes:
acquiring standing population data, land utilization layer data, placement point position data and placement point capacity data, wherein the standing population data comprise village population data and community standing population, and the land utilization layer data comprise residential area data and business area data;
generating population space distribution map data according to the data of the standing population, the data of the residential area and the data of the business area;
generating disaster-stricken data according to the flooding simulation process data and the population space distribution map data, wherein the disaster-stricken data comprises disaster-stricken area data and disaster-stricken population data;
in the embodiment, population space distribution map data are generated according to the population data of the permanent residence, the land use map layer data, the position data of the placement points and the capacity data of the placement points; and disaster suffering data is generated according to the flood inundation simulation process data and the population space distribution map data, so that preparation is made for real-time analysis and scheduling decision of risk avoiding transfer generation in the next step.
In an embodiment of the present invention, step S7 specifically includes:
calculating by a risk avoidance transfer real-time analysis calculation formula according to the disaster suffered data to generate risk avoidance transfer real-time analysis and scheduling decision data, wherein the risk avoidance transfer real-time analysis and scheduling decision data comprise recommended route transfer route data, predicted transfer time consumption data and predicted emplacer data, the risk avoidance transfer real-time analysis calculation formula comprises constraint conditions, and the constraint conditions comprise a transfer process safety degree constraint condition, a emplacer point capacity constraint, a maximum distance and time consumption constraint;
the risk avoidance transfer real-time analysis and calculation formula is specifically as follows:
the transfer process safety degree constraint conditions are specifically as follows:
the placement point capacity constraints are specifically:
the maximum distance and time-consuming constraints are specifically:
wherein,
for transferring an object
The total length of the bar path;
the length of a sub road section in a transfer route;
in order to allow for the maximum time consumption,
for transferring the object
The time taken for the bar route;
time is consumed for sub-road sections in the transfer route;
is a time consumption coefficient;
the maximum time consumption allowed.
The risk avoidance transfer real-time analysis and scheduling decision data are generated by calculating according to disaster-suffering data through a risk avoidance transfer real-time analysis calculation formula, wherein the risk avoidance transfer real-time analysis calculation formula is set to be the shortest overall transfer of all groups, and a plurality of constraint conditions are set, wherein the constraint conditions comprise a transfer process safety degree constraint condition, a placement point capacity constraint, a maximum distance constraint and a time-consuming constraint, so that the risk avoidance transfer requirements under various disaster conditions can be obtained more accurately and more stably; the embodiment fully considers the constraint problem in the real condition, thereby providing an efficient risk avoidance transfer real-time analysis and scheduling decision.
In an embodiment of the present invention, step S8 specifically includes:
calculating according to risk avoidance transfer real-time analysis and scheduling decision data through a settling point calculation formula, and constructing a material allocation model;
the arrangement point calculation formula is specifically as follows:
according to the above formula, the material satisfaction of each placement point can be expressed as:
calculating through a material allocation analysis formula according to the material allocation model to generate material allocation real-time data and scheduling decision data, wherein the material allocation analysis formula comprises material supply path constraint conditions and maximum path constraint conditions;
wherein the material allocation analysis formula is specifically as follows:
the material supply path constraint conditions are specifically as follows:
The maximum distance constraint conditions are specifically as follows:
The method comprises the steps of calculating according to risk avoidance transfer real-time analysis and scheduling decision data through a settling point calculation formula, constructing a material allocation model, calculating according to the material allocation model through a material allocation analysis formula, and generating material allocation real-time data and scheduling decision data, wherein the settling point calculation formula considers the supply quantity required by an individual, the predicted settling number of settling points and the material quantity of the settling points so as to generate more accurate material supply quantity required by the settling points; the method comprises the steps that a material allocation analysis formula is calculated by setting the highest material supply satisfaction degree of arrangement points as a target, sufficient material support data are provided for people to transfer to emergency disaster avoidance in disaster, the material allocation analysis formula fully considers the supply satisfaction degree of the arrangement points and the highest value obtained by summing the supply satisfaction degree of each arrangement point under the constraints of material supply path constraint conditions and maximum distance constraint conditions, and technical support more fitting actual constraint conditions, material allocation in emergency risk avoidance and people requirements is provided.
The invention provides a flood disaster emergency refuge and material real-time allocation method by combining a multidisciplinary multi-type model, solves the problems of unreasonable refuge transfer and material allocation scheme and lack of dynamic consideration in a static pre-plan, realizes real-time analysis and scheduling of emergency refuge and material allocation in a flood disaster scene, and effectively supports flood prevention and disaster prevention emergency decision.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting implementations with reference to the accompanying drawings in which:
FIG. 1a is a flowchart illustrating steps of an emergency risk avoiding and real-time material allocation method for flood disaster according to an embodiment of the present invention;
FIG. 1b is a flowchart illustrating steps of an emergency risk avoiding and real-time material allocation method for flood disaster according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a flood inundation rapid intelligent prediction model of the flood disaster emergency risk avoiding and material real-time allocating method according to an embodiment of the present invention.
Detailed Description
The technical method of the invention is described in detail with reference to the accompanying drawings, and it is obvious that the described embodiments are a part of the embodiments of the invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor methods and/or microcontroller methods.
The invention provides an emergency risk avoiding and material real-time allocating method for flood disasters, which comprises the following steps of:
step S1: acquiring original sampling data, and constructing a basic model data set according to the original sampling data, wherein the basic model data set comprises a one-dimensional river surge model, a two-dimensional earth surface model and an underground pipe network drainage model;
step S2: constructing a flood simulation model according to the basic model data set;
and step S3: generating dynamic simulation process data of flood according to the flood simulation model;
and step S4: generating a maximum flood submerging water depth grid map according to the data of the flood dynamic simulation process;
step S5: constructing a flood submerging rapid intelligent prediction model according to the maximum flood submerging water depth grid map, and generating flood submerging simulation process data according to the flood submerging rapid intelligent prediction model;
step S6: generating disaster-affected data according to the flooding simulation process data;
step S7: generating danger-avoiding transfer real-time data and scheduling decision data according to the disaster-suffered data;
step S8: and constructing a material allocation model according to risk avoidance transfer real-time analysis and scheduling decision analysis, and generating material allocation real-time data and scheduling decision data according to the material allocation model to execute material allocation.
Specifically, for example, according to a specific engineering research area, multisource and multi-type data such as regional basic geographic information, hydrology, historical disasters and engineering structures are collected, a flood simulation model based on hydrology, hydrodynamic force and pipe network multi-model coupling is constructed, computational analysis of regional flood disaster-causing processes under various typical incoming water and rainstorm scenes is achieved, and a flood dynamic simulation process result under the typical scenes is output.
And rasterizing and superposing the results according to the dynamic simulation process results of the flood under the typical scene output by the flood simulation model to generate a maximum flood submerging water depth raster map. Based on data such as rainstorm, water coming, tide level and maximum flood submergence water depth grid map under typical working conditions, a ConvLSTM neural network is used for constructing a flood submergence rapid intelligent prediction model, calculating and outputting results.
And forecasting and analyzing the population suffering from the disaster according to the flooding simulation result output by the intelligent prediction model in real time. The shortest overall transfer time of the population in the disaster area is taken as a general target, multidimensional factors such as arrangement capacity, transfer routes and the like are comprehensively considered, a risk avoiding transfer model is constructed, and real-time analysis and scheduling decision of risk avoiding transfer under the flood and disaster situation are predicted.
On the basis of real-time risk avoidance transfer analysis results, a total goal of highest personnel material supply degree of a placement point is taken, multidimensional factors such as material distribution capacity and distribution speed are comprehensively considered, a material distribution model is built, and real-time material distribution analysis and scheduling decision under the situation of predicting risk avoidance transfer are achieved.
The method comprises the steps of acquiring original sampling data and analyzing and constructing a basic model data set according to the original sampling data; constructing flood dynamic simulation process data according to the basic model data set; generating dynamic simulation process data of flood according to the flood simulation model; generating a maximum flood submerging water depth grid map according to the data of the flood dynamic simulation process; constructing a flood inundation rapid intelligent prediction model according to the maximum flood inundation water depth grid map, and generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model; generating disaster-affected data according to the flooding simulation process data; generating real-time analysis and scheduling decisions of risk avoidance transfer according to the disaster-suffered data; and constructing a material allocation model according to real-time risk transfer analysis and scheduling decision analysis, and generating real-time material allocation analysis and scheduling decision according to the material allocation model, so that technical support is provided for flood disaster emergency risk avoiding scheduling decision and material allocation when flood disasters occur, and efficient and convenient disaster area rescue is provided.
In an embodiment of the present invention, step S1 specifically includes:
acquiring original sampling data, wherein the original sampling data comprises position information, basic geographic information data, hydraulic structure basic data and scheduling scheme data, hydrological and historical disaster data and urban rainwater pipe network data, the basic geographic information data comprises river water system data, river terrain data, surface terrain data and land utilization data, the land utilization data comprises house data, road data and railway data, the hydraulic structure basic data and scheduling scheme data comprises sluice data, pump station data, embankment data, culvert data and water conservancy junction data, the hydrological and historical disaster data comprises historically measured rainfall data, historical flood flooding water level data and historical flood flooding range data, and the urban rainwater pipe network data comprises pipe network data and water outlet data;
specifically, for example, the data required for analyzing flood in urban areas include basic geographic information, hydrology and flood, flood control and drainage (water) projects and structures, flood scheduling schemes and project scheduling rules, land utilization, historical flood and the like, and the data collection specifically includes the following steps: the basic geographic information data comprise vector information and elevation data of terrain and landform, river water system, river section, underwater terrain, administrative division, residential area distribution, traffic road and the like which are generated or updated recently in the calculation range. The scale bar of the basic base map is not less than 1:2000, the ratio of the river section to the river underwater topography map is not less than 1:2000, the scale of the deep sea chart is not less than 1:25000, the scale of the chart in the shallow sea is not less than 1:10000. the basic geographic information should meet the requirements of timeliness and practicability. 2) The hydrological and flood data comprise actual measurement data such as rainfall, water level, flow, tide and the like, design data such as design rainstorm, design flood, design tide level and the like, data such as water level-flow relation, water level-area-volume relation and the like of a hydrological control station reflecting storage and discharge characteristics of rivers, lakes, reservoirs and stagnant and flooded areas, related data of drainage (water) areas, spatial position information of related hydrological stations, water level stations, tide level stations and rainfall stations and the like. The hydrologic data should meet reliability, consistency and representativeness requirements. 3) The flood control and drainage engineering and structure data comprise reservoir, dike, gate dam, pump station, drainage pipe network, bridge and culvert, underground facilities and other engineering, and characteristic parameters and spatial position information of linear ground objects which are 0.5m above the ground. Engineering and construction data should meet the requirements of reality, timeliness and accuracy. 4) The flood scheduling scheme and the engineering scheduling rule comprise flood control plans of all levels, a flood scheduling scheme, a flood defense scheme, reservoir, flood storage and stagnation areas, pump stations, flood diversion channels, gate dam flood control scheduling application rules and the like. The flood scheduling scheme and the engineering scheduling rules should meet the requirements of timeliness and authority. 5) The land utilization data comprises land utilization, remote sensing images, crop types and distribution during flood, and the like. The scale of the soil utilization map is not less than 1:10000, the resolution of the remote sensing image is not lower than 2m. The land utilization data should meet the requirements of practicability and timeliness. 6) The historical flood data comprises historical flood (rainstorm, storm surge, dam break and the like) hydrological characteristics (actually measured water level or flood mark in a station flood process, river course and a submerged area, a submerged range, submerged duration, flood arrival time and the like), dam burst (overflow) condition, project and project scheduling when flood occurs and the like.
Constructing a hydrological model of the non-built area according to the position information, the surface topographic data and the land utilization data, wherein the constructing step is specifically to construct the model by adopting a comprehensive unit line method and perform production convergence calculation;
generating river network topological relation data, river surge section assigned data, gate dam hydraulic structure parameters and scheduling regulation data according to river water system data, river terrain data, hydraulic structure basic data and scheduling scheme data;
analyzing according to the topological relation data of the river network, the assigned value data of the river section, the parameters of the hydraulic structure of the gate dam and the scheduling regulation data, thereby constructing a one-dimensional river model;
performing irregular mesh generation on the surface terrain data to generate irregular mesh surface terrain data;
encrypting the irregular grid ground surface terrain data, road data, railway data, sluice data, pump station data, embankment data and water conservancy junction data to generate a middle two-dimensional ground surface model;
constructing a two-dimensional earth surface model according to the middle two-dimensional earth surface model, the position information, the house data, the road data, the embankment data and the culvert data;
and constructing an underground pipe network drainage model according to the pipe network data, the water outlet data and the pump station data.
Specifically, for example, based geographic information data such as river water systems, river terrain, surface terrain, land utilization, and the like of an area are collected according to a specific study area; collecting basic data and scheduling scheme data of hydraulic structures such as sluice, pump station, dike, hydro-junction and the like; collecting hydrological and historical disaster data such as historical measured rainfall, historical flood submerging level and range; collecting urban rainwater pipe network data; when the important river channel terrain and surface terrain data are lacked, river channel section terrain measurement and surface terrain aviation flight measurement tasks are carried out.
Constructing a hydrological model of a non-built area: hydrologic zoning is performed on non-built areas in the research area. The non-built areas are mainly mountainous areas, and a model is built by adopting a comprehensive unit line to calculate the production convergence.
Constructing a one-dimensional river model: and (4) utilizing the river and hydraulic data to complete the establishment of the topological relation of the river network, the assignment of the section of the river, the setting of the parameters and the dispatching rules of the hydraulic buildings of the gate dam and the examination of rationality.
Constructing a two-dimensional earth surface model: and (3) adopting irregular grids to subdivide the earth surface grids, and automatically encrypting the grids to adapt to the change of the terrain and the ground features according to the terrain in the calculation area, the distribution and the trend of a water system, a dike, a road, a railway and the like. Generalizing the water-blocking function of houses, roads and dikes in the area and the water passing function of culverts.
Constructing a drainage model of the underground pipe network: and checking the topological relation of the pipe network by utilizing the pipe network data, combining the parameters and the scheduling rules of hydraulic buildings such as pipe network sections, water outlet distribution, pump stations and the like, and performing reasonability examination.
Constructing a flood control and drainage scheduling model: the flood control and drainage scheduling model is based on multi-factor combined scheduling of reservoirs, gate dams, pump stations and the like.
According to the embodiment, a non-built-up area hydrological model is constructed according to basic geographic information data analysis, a one-dimensional river surge model is constructed according to basic geographic information data, hydraulic structure basic data and scheduling data analysis, a two-dimensional surface model is constructed according to surface topographic data, hydraulic structure basic data and scheduling scheme data analysis, and an underground pipe network drainage model is constructed according to urban rainwater pipe network data analysis, so that a basic model data set is constructed according to multi-source and multi-type original sampling data, and premise preparation is made for constructing a flood simulation model in the next step.
In an embodiment of the present invention, step S2 specifically includes:
constructing a middle flood simulation model according to the hydrological model, the one-dimensional river channel model, the two-dimensional surface model and the underground pipe network drainage model of the non-built area;
and carrying out parameter calibration, verification and inspection on the flood simulation model according to the hydrologic and historical disaster data to generate the flood simulation model.
Specifically, for example, multi-model coupling: the model coupling of the built-up area is completely coupled within a time step, so that the water exchange between the confluent or overflowing river and the ground surface, the drainage or reverse flow of the pipe network and the river are reflected in real time;
and water exchange occurs at the drainage port, and water exchange between the earth surface and the pipe network inspection well such as confluence or full pipe back irrigation occurs. The production convergence calculation model of the drainage basin of the non-built area can be used as a boundary condition to be coupled with the built area model, and then the flood simulation model of the whole drainage basin/area is finally built.
Model calibration, verification and inspection: parameter calibration, verification and inspection are carried out on the watershed flood simulation model according to historical flood survey data, related design reports (water surface line calculation results designed in the initial design stage) of each river channel and mutual evidence of a mathematical model, wherein the adopted actual measurement data mainly are historical water immersion point distribution data of the watershed (region).
According to the embodiment, the intermediate flood simulation model is constructed according to the basic data set analysis, and the parameter calibration, verification and inspection are carried out on the intermediate flood simulation model, so that the flood simulation model is generated, and the precondition is prepared for generating the flood dynamic simulation process data in the next step.
In an embodiment of the present invention, step S3 specifically includes:
generating a flood encounter situation acquisition control;
acquiring flood encounter scenario data through the flood encounter scenario acquisition control, wherein the flood encounter scenario data comprises flood data, tide data, crash and flood dike data and scheduling data;
generating typical situation condition catalog data of flood disasters according to the flood encounter situation data;
and generating flood dynamic simulation process data for the flood simulation model according to the typical situation condition catalog data of the flood disaster.
Specifically, for example, based on the calculation analysis of regional flood disaster-causing processes under various typical incoming water and rainstorm scenes, the dynamic simulation process results of flood under typical scenes are output. The following table shows a simulation result table at a certain time outputted by the flooding model.
According to the embodiment, the flood encounter situation data is obtained through the flood encounter situation obtaining control, and the flood disaster typical situation condition catalogue data is generated through the flood encounter situation data analysis, so that the flood dynamic simulation process data is analyzed and generated through the flood simulation model, and precondition preparation is made for generating the maximum flood submerging water depth raster map in the next step.
In an embodiment of the present invention, step S4 specifically includes:
generating space-time grid data according to the flood dynamic simulation process data;
generating a single grid point data set according to the space-time grid data;
and combining the single grid point data sets to generate a maximum flooding water depth grid map of the flooding.
Specifically, for example, the output dynamic simulation process results of flooding under each typical scenario are marked as vector grid data and each grid water depth data; rendering is carried out according to the grid water depth data and the submerging water depth, and the grid water depth data is converted into space-time grid data, so that the space-time grid data is sent to a ConvLSTM neural network as input to be trained to obtain space-time grid data under typical situations, wherein the rasterization operation of the achievement is realized by GIS operation software such as ArcGIS, supermap and the like, and can also be realized by researching and developing a special format conversion program by combining with space data processing open source libraries such as GDAL, dotSpatial and the like;
performing superposition analysis according to space-time raster data under typical situations, and circularly reading raster points in a raster image layer at each moment; combining the maximum pixel value (water depth value) of each grid point at each moment to generate a flooding submerging water depth grid map;
specifically, for example, the flooding model is output to the result to be subjected to rasterization processing, a flooding water depth map is generated, integration application is performed in the information system, and after the result rasterization processing is integrated, a user can visually see the dynamic flooding condition predicted at each moment in the system.
According to the embodiment, the space-time raster data are generated according to the flood dynamic simulation process data, and the space-time raster data are analyzed to generate the single-raster-point data set, so that the maximum flooding water depth raster map is generated in a combined mode, and the premise preparation is made for the next generation of the flood flooding simulation process data.
In an embodiment of the present invention, step S5 specifically includes:
generating a rainstorm data acquisition control;
acquiring rainstorm data through a rainstorm data acquisition control, wherein the rainstorm data comprises typical working condition rainstorm data and atypical working condition rainstorm data;
generating a space-time rainfall grid diagram according to typical working condition rainstorm data;
constructing an intermediate intelligent prediction model according to the space-time rainfall grid map, the original sampling data and the maximum flooding water depth grid map of the flooding;
generating an atypical working condition flood maximum submerging water depth grid map according to atypical working condition rainstorm data;
generating a flood submerging fast intelligent prediction model for the intermediate intelligent prediction model according to the atypical working condition flood maximum submerging water depth grid map;
and generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model.
Specifically, for example, a rainstorm grid map and a flood maximum flooding water depth grid map under a plurality of rainstorm scenes are used as output, all typical working condition data are used as a training set, a ConvLSTM neural network is used for training, and an intelligent prediction model is constructed. The schematic diagram of the model is shown in fig. 2. The ConvLSTM neural network is suitable for space-time sequence grid data, and mainly comprises a multi-scale feature extraction sub-network and a multi-scale flooding identification sub-network in the embodiment. Through the composition of two self-networks and black box training, the rapid prediction and generation of the maximum flooding grid map under the rainstorm situation defined by a user are realized.
The embodiment acquires the rainstorm data by generating the rainstorm data acquisition control and acquiring the rainstorm data through the rainstorm data acquisition control; generating a space-time rainfall grid diagram according to typical working condition rainstorm data; constructing an intermediate intelligent prediction model according to the space-time rainfall grid map, the original sampling data and the maximum flooding water depth grid map of the flooding; generating a maximum flooding water depth grid map of the atypical working condition flooding according to the atypical working condition rainstorm data; generating a flood submerging fast intelligent prediction model for the intermediate intelligent prediction model according to the atypical working condition flood maximum submerging water depth grid map; and generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model, thereby preparing for generating disaster-affected data in the next step.
In an embodiment of the present invention, step S6 specifically includes:
acquiring standing population data, land utilization layer data, positioning point position data and positioning point capacity data, wherein the standing population data comprises village population data and community standing population, and the land utilization layer data comprises residential area data and business area data;
generating population space distribution map data according to the data of the standing population, the data of the residential area and the data of the business area;
generating disaster-stricken data according to the flooding simulation process data and the population space distribution map data, wherein the disaster-stricken data comprises disaster-stricken area data and disaster-stricken population data;
specifically, for example, with the goal of minimizing the overall transfer time consumption, risk avoidance transfer analysis is performed on disaster-affected points and placement points (risk avoidance points) in the area. The intra-area element information is shown in the following table.
According to the embodiment, population space distribution map data are generated according to the population data of the regular lives, the land use map layer data, the position data of the arrangement points and the capacity data of the arrangement points; and disaster-suffering data is generated according to the flooding simulation process data and the population space distribution map data, so that preparation is made for real-time analysis and scheduling decision of risk avoidance transfer generation in the next step.
In an embodiment of the present invention, step S7 specifically includes:
calculating by a risk avoidance transfer real-time analysis calculation formula according to the disaster suffered data to generate risk avoidance transfer real-time analysis and scheduling decision data, wherein the risk avoidance transfer real-time analysis and scheduling decision data comprise recommended route transfer route data, predicted transfer time consumption data and predicted emplacer data, the risk avoidance transfer real-time analysis calculation formula comprises constraint conditions, and the constraint conditions comprise a transfer process safety degree constraint condition, a emplacer point capacity constraint, a maximum distance and time consumption constraint;
the risk avoiding transfer real-time analysis and calculation formula specifically comprises:
for example, it can be simplified as:
the transfer process safety degree constraint conditions are specifically as follows:
for example, it can be simplified as:
the placement point capacity constraints are specifically:
for example, it can be simplified as:
the maximum distance and time consumption constraints are specifically as follows:
for example, it can be simplified as:
wherein,
or
For transferring the object
The total length of the bar path;
for sub-track length in transfer routes
Or
In order to allow for the maximum time-consuming,
or
For transferring the object
The time consumed by the bar route;
or
Time is consumed for sub-road sections in the transfer route;
is a time consumption coefficient;
or
The maximum time consumption allowed.
Specifically, for example, the risk avoidance transfer real-time analysis calculation formula is solved and calculated to generate an output result, wherein the solving and calculating method can select a simulated annealing algorithm, a genetic algorithm, a tabu search algorithm, a particle swarm algorithm or other intelligent optimization algorithms, and the model output result includes the recommended risk avoidance transfer route of each risk area/point, the predicted transfer time consumption of each transfer unit and the predicted number of the persons to be placed at each placement point.
According to the method, calculation is carried out through a risk avoidance transfer real-time analysis calculation formula according to disaster-stricken data, and risk avoidance transfer real-time analysis and scheduling decision data are generated, wherein the risk avoidance transfer real-time analysis calculation formula is set to be the shortest overall transfer of all groups, and a plurality of constraint conditions are set, wherein the constraint conditions comprise a transfer process safety degree constraint condition, a placement point capacity constraint, a maximum distance constraint and a time-consuming constraint, so that more accuracy and stability are obtained, and the risk avoidance transfer requirements under various disaster conditions can be adapted; the embodiment fully considers the constraint problem in the actual condition, thereby providing an efficient risk avoidance transfer real-time analysis and scheduling decision.
In an embodiment of the present invention, step S8 specifically includes:
calculating according to the risk avoidance transfer real-time analysis and scheduling decision data through a mounting point calculation formula, and constructing a material allocation model;
the setting point calculation formula is specifically as follows:
for example, it can be simplified as:
according to the above formula, the material satisfaction of each placement point can be expressed as:
calculating through a material allocation analysis formula according to the material allocation model to generate material allocation real-time data and scheduling decision data, wherein the material allocation analysis formula comprises material supply path constraint conditions and maximum path constraint conditions;
wherein the material allocation analysis formula is specifically as follows:
the material supply path constraint conditions are specifically as follows:
The maximum distance constraint condition is specifically as follows:
for example, it can be simplified as:
Specifically, for example, the above calculation formula is solved and calculated, so as to generate a model output result, wherein the solution calculation method may select a simulated annealing algorithm, a genetic algorithm, a tabu search algorithm, a particle swarm algorithm or other intelligent optimization algorithms, the model output result is a result scheme set which is sorted according to the total material satisfaction degree from high to low, and the result scheme includes operation target placement points, transportation routes, material amounts of placement points and material satisfaction degrees of supply points and supply teams;
and analyzing and adjusting according to the result scheme set and preset experience conditions to generate real-time material allocation analysis and scheduling decisions under the situations of forecasting flood submergence and risk avoidance transfer.
Specifically, for example, the material allocation analysis is performed on the placement points in the region with the material supply degree as a target. The supply information of the supplies in the area is shown in the following table:
the method comprises the steps of calculating according to risk avoidance transfer real-time analysis and scheduling decision data through a settling point calculation formula, constructing a material allocation model, calculating according to the material allocation model through a material allocation analysis formula, and generating material allocation real-time data and scheduling decision data, wherein the settling point calculation formula considers the supply quantity required by an individual, the predicted settling number of settling points and the material quantity of the settling points so as to generate more accurate material supply quantity required by the settling points; the method comprises the steps that a material allocation analysis formula is calculated by setting the highest material supply satisfaction degree of arrangement points as a target, sufficient material support data are provided for people to transfer to emergency disaster avoidance in disaster, the material allocation analysis formula fully considers the supply satisfaction degree of the arrangement points and the highest value obtained by summing the supply satisfaction degree of each arrangement point under the constraints of material supply path constraint conditions and maximum distance constraint conditions, and technical support more fitting actual constraint conditions, material allocation in emergency risk avoidance and people requirements is provided.
The invention provides a flood disaster emergency refuge and material real-time allocation method by combining a multidisciplinary multi-type model, solves the problems of unreasonable refuge transfer and material allocation scheme and lack of dynamic consideration in a static pre-plan, realizes real-time analysis and scheduling of emergency refuge and material allocation in a flood disaster scene, and effectively supports flood prevention and disaster prevention emergency decision.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. A flood disaster emergency risk avoiding and material real-time allocation method is characterized by comprising the following steps:
step S1: acquiring original sampling data, and constructing a basic model data set according to the original sampling data, wherein the basic model data set comprises a one-dimensional river surge model, a two-dimensional earth surface model and an underground pipe network drainage model;
step S2: constructing a flood simulation model according to the basic model data set;
and step S3: generating flood dynamic simulation process data according to the flood simulation model;
and step S4: generating maximum flood submerging water depth raster map data according to the flood dynamic simulation process data;
step S5: constructing a flood inundation rapid intelligent prediction model according to the maximum flood inundation water depth raster map data, and generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model;
step S6: generating disaster-affected data according to the flooding simulation process data;
step S7: generating danger avoiding transfer real-time data and scheduling decision data according to the disaster-suffered data;
step S8: constructing a material allocation model according to the risk avoidance transfer real-time data and the scheduling decision data, and generating material allocation real-time data and scheduling decision data according to the material allocation model to execute material allocation;
wherein the step S3 specifically comprises the following steps:
generating a flood encounter situation acquisition control;
acquiring flood encounter scenario data through the flood encounter scenario acquisition control, wherein the flood encounter scenario data comprises flood data, tide data, flood embankment data and scheduling data;
generating typical situation condition catalog data of flood disasters according to the flood encounter situation data;
correcting the flood simulation model according to the typical situation condition catalog data of the flood disaster to generate dynamic simulation process data of the flood;
step S4 specifically includes:
generating space-time grid data according to the flood dynamic simulation process data;
generating a single grid point data set according to the space-time grid data;
combining the single grid point data sets to generate the maximum flooding submerging water depth grid map data;
the step S5 specifically comprises the following steps:
generating a rainstorm data acquisition control;
acquiring rainstorm data through a rainstorm data acquisition control, wherein the rainstorm data comprises typical working condition rainstorm data and atypical working condition rainstorm data;
generating a space-time rainfall grid diagram according to typical working condition rainstorm data;
constructing an intermediate intelligent prediction model according to the space-time rainfall raster image, the original sampling data and the maximum flood submerging water depth raster image data;
generating a maximum flooding water depth grid map of the atypical working condition flooding according to the atypical working condition rainstorm data;
correcting the intermediate intelligent prediction model according to the atypical working condition maximum flooding water depth grid map to generate a flooding fast intelligent prediction model;
generating flood inundation simulation process data according to the flood inundation rapid intelligent prediction model;
step S8 specifically includes:
calculating according to the risk avoidance transfer real-time data and the scheduling decision data through a mounting point calculation formula, and constructing a material allocation model;
the setting point calculation formula is specifically as follows:
calculating the material satisfaction degree of each arrangement point and expressing the material satisfaction degree as follows:
calculating through a material allocation analysis formula according to the material allocation model to generate material allocation real-time data and scheduling decision data, wherein the material allocation analysis formula comprises a material supply path constraint condition and a maximum path constraint condition;
wherein the material allocation analysis formula is specifically as follows:
the material supply path constraint conditions are specifically as follows:
the maximum distance constraint condition is specifically as follows:
2. The method according to claim 1, wherein step S1 is specifically:
acquiring original sampling data, wherein the original sampling data comprises position information, basic geographic information data, hydraulic structure basic data and scheduling scheme data, hydrologic and historical disaster data and urban rainwater pipe network data, the basic geographic information data comprises river water system data, river channel topographic data, surface topographic data and land utilization data, the land utilization data comprises house data, road data and railway data, the hydraulic structure basic data and scheduling scheme data comprises sluice data, pump station data, dike dam data, culvert data and water conservancy data, the hydrologic and historical disaster data comprises historically measured rainfall data, historical flood flooding water level data and historical flood flooding range data, and the urban rainwater pipe network data comprises pipe network data and water outlet data;
analyzing according to the position information, the surface topographic data and the land utilization data, and performing production convergence calculation by adopting a comprehensive unit line method so as to construct a hydrological model of the non-built area;
generating river network topological relation data, river surge section assigned data, gate dam hydraulic structure parameters and scheduling regulation data according to river water system data, river terrain data, hydraulic structure basic data and scheduling scheme data;
analyzing according to the topological relation data of the river network, the assigned value data of the river section, the parameters of the hydraulic structure of the gate dam and the scheduling regulation data, thereby constructing a one-dimensional river model;
performing irregular mesh generation on the surface terrain data to generate irregular mesh surface terrain data;
encrypting the irregular grid ground surface topographic data, road data, railway data, sluice data, pump station data, embankment data and hydro junction data into a grid to generate a middle two-dimensional ground surface model;
constructing a two-dimensional earth surface model according to the middle two-dimensional earth surface model, the position information, the house data, the road data, the embankment data and the culvert data;
and constructing an underground pipe network drainage model according to the pipe network data, the water outlet data and the pump station data.
3. The method according to claim 2, wherein step S2 is specifically:
constructing a middle flooding simulation model according to the hydrological model, the one-dimensional river surge model, the two-dimensional earth surface model and the underground pipe network drainage model of the non-built area;
and carrying out parameter calibration, verification and inspection on the flood simulation model according to the hydrologic and historical disaster data to generate the flood simulation model.
4. The method according to claim 2, wherein step S6 is specifically:
acquiring standing population data, land utilization layer data, positioning point position data and positioning point capacity data, wherein the standing population data comprises village population data and community standing population data, and the land utilization layer data comprises residential area data and business area data;
generating population space distribution map data according to the data of the standing population, the data of the residential area and the data of the business area;
and generating disaster-stricken data according to the flood inundation simulation process data and the population space distribution map data, wherein the disaster-stricken data comprises disaster-stricken area data and disaster-stricken population data.
5. The method according to claim 1, wherein step S7 is specifically:
calculating by a risk avoidance transfer real-time analysis calculation formula according to the disaster data to generate risk avoidance transfer real-time data and scheduling decision data, wherein the risk avoidance transfer real-time data and the scheduling decision data comprise recommended route transfer route data, predicted transfer time consumption data and predicted emplacer data, the risk avoidance transfer real-time analysis calculation formula comprises constraint conditions, and the constraint conditions comprise a transfer process safety degree constraint condition, a emplacer point capacity constraint and a maximum distance and time consumption constraint;
the risk avoidance transfer real-time analysis and calculation formula is specifically as follows:
the transfer process safety degree constraint conditions are specifically as follows:
the capacity constraint of the placement points is specifically as follows:
the maximum distance and time-consuming constraints are specifically:
wherein,
for transferring the object
The total length of the bar path;
the length of a sub road section in a transfer route;
in order to allow for the maximum time-consuming,
the time consumed for transferring the ith route of the object;
time is consumed for sub-road sections in the transfer route;
is a time consumption coefficient;
the maximum time consumption allowed.
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| CN104851360A (en) * | 2014-02-14 | 2015-08-19 | 杭州贵仁科技有限公司 | Method and system for generating flood risk map |
| CN111652777A (en) * | 2020-05-15 | 2020-09-11 | 长江勘测规划设计研究有限责任公司 | Flood emergency risk avoiding method |
| CN114240119A (en) * | 2021-12-10 | 2022-03-25 | 浙江水利水电学院 | Digital twin-based flood control and waterlogging prevention system and early warning method for whole elements of territorial universe |
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