CN116911699B - Method and system for fine dynamic evaluation of toughness of urban flood disaster response - Google Patents
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Abstract
The invention discloses a method and a system for the fine dynamic evaluation of toughness coping with urban flood disasters, wherein the method comprises the following steps: designing a toughness assessment framework for coping with urban flood disasters, and constructing an assessment index system; providing a weighting and quantization method of each evaluation index; collecting and processing target area data, constructing a hydrographic hydrodynamic coupling model, and acquiring a flood space-time process; calculating the weight of each index, carrying out dynamic and rasterization processing, and calculating the robustness and other indexes of the target area; fitting toughness dynamic processes of different scales, establishing a toughness curve and determining the physical significance of curve parameters; evaluating toughness grades of different scales; and identifying a weak area and characteristics of the toughness, analyzing influence factors, and constructing a toughness improvement strategy set. According to the invention, an evaluation system is established based on the toughness theory, and the dynamic evaluation of toughness is realized by utilizing the hydrologic hydrodynamic model, so that the evaluation precision is improved.
Description
Technical Field
The invention relates to a city flood control simulation evaluation technology, in particular to a method and a system for finely and dynamically evaluating the toughness of a city flood disaster.
Background
Due to heavy rainfall or river water backflow and the like, urban ground ponding or underground water level rises, so that an urban drainage system is invalid or overloaded to run, and urban flood disasters are often caused. Waterlogging disasters not only can cause casualties and property loss, but also can influence the normal operation and social stability of cities. With the acceleration of global climate change and urbanization progress, the frequency and severity of waterlogging disasters are increasing increasingly, and become a serious problem threatening urban safety and sustainable development.
In order to effectively cope with waterlogging disasters, the traditional disaster prevention and reduction concept mainly focuses on improving the resistance of urban infrastructure and reducing the adverse effects of natural conditions, and the waterlogging risk is reduced by means of measures such as construction of drainage engineering, land utilization adjustment, planning standard establishment and the like. However, this concept ignores the characteristics of cities as a complex system, and cannot fully consider interactions and feedback mechanisms between systems of cities, nor reflect the adaptation and recovery ability of cities after suffering from waterlogging. Therefore, simply relying on engineering measures to prevent and treat waterlogging cannot meet the current urban development requirements.
In order to realize the evaluation of the comprehensive capacity of the city to the waterlogging disasters, a scientific and reasonable toughness evaluation model needs to be established. At present, domestic and foreign scholars explore urban toughness assessment models for coping with waterlogging disasters, but most models have some defects. For example: some models only carry out disaster damage assessment, attach excessive importance to objective factors, and ignore subjective factors; some models only consider influencing factors of the top policy level, and lack research on the bottom policy level; some models do not fully utilize space data and dynamic data to carry out refined analysis, only can calculate quarter or year data, and in space, the model is processed by taking administrative areas as units; some models do not give specific toughness promotion strategies, etc. One common shortcoming of the existing model is that the constructed index system cannot perform fine simulation in time and space, and granularity is not fine enough.
Therefore, improvements and perfection of existing models are necessary to improve the accuracy and practicality of toughness assessment.
Disclosure of Invention
The invention aims to: a method for the fine dynamic assessment of the toughness of urban flood disasters is provided to solve the problems in the prior art. Further, a system is provided to implement the above method.
The technical scheme is as follows: according to one aspect of the application, a method for fine dynamic assessment of toughness of urban flood disaster response is provided, comprising the following steps:
s1, constructing a toughness comprehensive evaluation system based on a 4R measurement system of toughness, wherein the toughness comprehensive evaluation system at least comprises a target layer, a criterion layer and an element layer, the element layer comprises N evaluation indexes, and N is a natural number;
s2, dividing the type of the evaluation index and constructing a quantitative calculation formula and a weighting method of the evaluation index aiming at each evaluation index of the element layer;
s3, acquiring basic data of a target area, constructing a hydrological hydrodynamic coupling model of the target area, and simulating by taking the basic data as input data to acquire process data of the target area for reflecting the flood disaster space-time evolution;
step S4, calculating the numerical value of each evaluation index at each moment one by one based on the basic data and the process data, giving weight, carrying out rasterization and dynamic processing on the evaluation index, and calculating the numerical values of the criterion layer and the target respectively;
S5, dividing the target area into at least two scales, and respectively calculating the average value of the toughness surface of each scale; fitting the toughness dynamic process of the urban flood disaster response of different scales, and establishing a multi-scale urban flood disaster response toughness curve;
step S6, dividing a toughness threshold value and setting a toughness grade for each scale, evaluating the toughness grade of each grid of a target area with different scales, and outputting an evaluation result;
and S7, identifying urban flood disaster toughness weak areas and difference characteristics thereof at all moments based on the evaluation result, analyzing influence factors of the toughness weak areas, and constructing a toughness improvement strategy set.
According to one aspect of the application, the step S1 is further:
step S11, a toughness measurement system based on toughness screens toughness measurement indexes from the existing toughness research literature to form a toughness index candidate set;
step S12, constructing an index independence checking method set, and selecting at least one index independence checking method to perform correlation analysis on the toughness indexes in the toughness index candidate set to obtain a preferable toughness index candidate set;
step S13, performing independence test on the optimized toughness indexes, and forming an element layer of a toughness comprehensive evaluation system based on the toughness indexes passing the test, wherein an independence calculation formula is as follows: i 0 =1-abs（C）；I 0 Is an independence index; c is the Pearson's correlation coefficient,C∈[-1,1]the method comprises the steps of carrying out a first treatment on the surface of the abs represents absolute value;
step S14, forming a target layer, a criterion layer and an element layer of the toughness comprehensive evaluation system,
the target layer is the toughness of coping with urban flood disasters,
the criterion layer comprises robustness, intelligence, redundancy and restorability;
the element layer comprises actual flood control and drainage capability, economic system disaster resistance capability, personnel disaster resistance capability, forecasting capability, early warning capability, previewing capability, planning capability, flood control capability redundancy, infrastructure redundancy, disaster recovery capability and emergency rescue capability.
According to one aspect of the present application, the step S2 is further:
step S21, dividing the evaluation index types, including quantitative type indexes and qualitative type indexes, of each evaluation index of the element layer, and dividing the evaluation index types into raster data and vector data according to the data types of the evaluation indexes;
s22, constructing an evaluation index weighting method including a subjective weighting method, and constructing a relative importance degree matrix based on the relative importance degrees among different evaluation indexes; calculating weights of different evaluation indexes by adopting a chromatographic analysis method;
step S23, constructing a quantitative calculation formula for each evaluation index.
According to one aspect of the present application, the quantitative calculation formula of each evaluation index in step S23 is:
flood control and drainage capacity I 1 =（h max -h i ）/（h max -h min ）； h max The maximum ponding depth in all grids of the target area is set;h i the accumulated water depth of the ith grid of the target area;h min the minimum ponding depth in all grids of the target area is set;
disaster resistance capability of economic system:
；
TA i the total amount of assets for the ith grid of the target area,f i vulnerability functions relating to water depth and asset type for the ith grid of the target area, for calculating asset loss rates,h i for the water accumulation depth of the ith grid of the target area,P i asset type for the ith grid of the target area,nthe total number of grids for the target area;
personnel disaster resistance capability I 3 =（TP max -TP i ）/（TP max -TP min ）；
TP i =POP i ×α i When (when)HR i Alpha < 2.5 i =HR i 2.5 whenHR i When the temperature is more than or equal to 2.5,α i =1；
HR i =d i （v i +0.5）+DF i ，
TP i the number of disaster affected population for the ith grid of the target area,TP max for the maximum number of disaster recovery population for all grids in the target area,TP min the minimum disaster population number of all grids in the target area;POP i the population of the ith grid for the target area,α i disaster-stricken population proportion of the ith grid of the target area;HR i the method comprises the steps of taking a flood risk level coefficient of an ith grid of a target area as a flood risk level coefficient;d i for the water accumulation depth of the ith grid of the target area, v i For the flow rate of the ith grid of the target area,DF i the mud stone factor of the ith grid of the target area;
forecasting ability I 4 =1-(A o /A)；A o The control area of the first rain station for the target area o,Ais the total area of the target area;
early warning capability I 5 = 0.2×X 1 +0.2×X 2 +0.2×X 3 +0.2×X 4 +0.2×X 5 ；X 1 Is the coverage rate of the television,X 2 Is broadcast coverage rate,X 3 Is a mobile phone with a popular rate,X 4 In order for a fixed-line telephone to be popular,X 5 is the popularity of the Internet;
previewing capability I 6 Case capability i=1 or 0 7 =1 or 0;
flood protection capability redundancy I 8 =（Δh max -Δh i ）/（Δh max -Δh min ）；Δh max For the maximum value of the accumulated water depth difference of all grids in the target area before and after the three-dimensional space and the flood storage area participate in regulation, delta h is the accumulated water depth difference of the ith grid in the target area before and after the three-dimensional space and the flood storage area participate in regulation, delta h is min The minimum value of the accumulated water depth difference before and after the grid participates in regulation in the three-generation space and the flood storage area is set for all grids in the target area;
infrastructure redundancy I 9 = 0.44 X 6 +0.26X 7 +0.18 X 8 +0.12.X 9 ；X 6 Reserve capacity for the power system,X 7 Reserve capacity of the water supply system,X 8 Reserve capacity for communication system,X 9 The richness of the traffic road network is achieved;
disaster recovery ability I 10 =（t max -t i ）/（t max -t min ）；t max The depth of the accumulated water in all grids of the target area is reduced to a time maximum value below 0.15 m;t i the depth of the accumulated water of the ith grid of the target area is reduced to less than 0.15 m; t min A time minimum value for the depth of water accumulated in all grids of the target area to subside to below 0.15 m;
emergency rescue Capacity I 11 = (F i +H i +P i )/3；F i Fire department reachability for the ith grid of the target area,H i for hospital reachability of the ith grid of the target area,P i public security department reachability of the ith grid of the target area.
According to one aspect of the present application, the step S3 is further:
s31, acquiring basic data of a target area, wherein the basic data comprise water system distribution, river section, topography, land utilization, drainage pipe network arrangement and pipe diameter, gate pump position and scheduling rules, position and scale of a polder region and a flood storage region, relationship between water surface position and water level reservoir capacity of a lake, rainfall, evaporation, water level and flow data of the target area;
s32, constructing a hydrokinetic coupling model of a target area, wherein the hydrokinetic coupling model comprises a MIKE model and an Inforworks model;
step S33, calibrating, verifying and simulating a hydrologic and hydrodynamic model through basic data, and outputting a flood evolution process with a preset calculation step length according to given rainfall conditions; and obtaining process data of the space-time evolution of the flood disaster.
According to one aspect of the present application, the step S4 is further:
Step S41, acquiring process data and basic data, calculating the evaluation index value of the element layer for each grid at each moment,
step S42, rasterizing the vector class indexes by using a conversion tool rasterizing conversion tool in the arcgis tool;
step S43, carrying out dynamic processing on the data of the grid class; the dynamic treatment process comprises the following steps: setting a required time step, determining time nodes to be calculated, calculating index values at each time node, and arranging calculation results according to the time nodes to obtain a dynamic change process of the index;
and S44, respectively calculating the robustness, the intelligence, the redundancy, the restorability and the toughness of each scale of the target region according to the determined index weight and the index value.
According to one aspect of the present application, the step S5 is further:
step S51, dividing a target area into at least two scales, including a city scale, a county scale and a street scale; respectively calculating the average value of the toughness surface of each scale;
step S52, fitting the toughness values of different spatial scales by using a curve fitting module, screening a fitting function conforming to an expected target, and establishing a multi-scale urban flood disaster coping toughness change curve;
And step S53, analyzing the sensitivity of the parameters in the fitting function, and determining the physical meaning of the parameters in the fitting function.
According to one aspect of the present application, the step S6 is further:
step S61, dividing the toughness threshold value and setting the toughness grade for each scale, adopting the toughness relative value as a dividing standard in county scale and street scale,
step S62, comprehensively determining the membership degree of the flood disaster to the toughness by using a natural break method, a cloud model and a physical extension model, and defining a toughness grade division threshold;
and step S63, determining and evaluating the toughness grade of each grid area under different scales to obtain an evaluation result.
According to one aspect of the present application, the step S7 is further:
step S71, selecting representative moments of different stages of flood disaster development according to a preconfigured rule, and identifying weak areas of urban flood disaster toughness at different times and spatial changes thereof;
step S72, establishing a grading identification method of the influence factors of the toughness weak area; positioning basic characteristics affecting a toughness weak area, and realizing primary identification and secondary identification of factors affecting the toughness weak area;
step 73, providing a toughness improvement strategy of grid scale based on the influence factors of the toughness weak areas identified by each level, and constructing a toughness improvement strategy set.
According to another aspect of the present application, there is provided a refined dynamic assessment system of toughness in response to urban flood disasters, comprising:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,
the memory stores instructions executable by the processor for execution by the processor to implement the method for refined dynamic assessment of toughness against urban flood disasters of any one of the above-described technical schemes.
The beneficial effects are that: the toughness comprehensive evaluation system is constructed by constructing a 4R measurement system based on toughness, and in the system, the related indexes can be obtained by calculating the output result of a hydrographic hydrodynamic coupling model, so that the defect that in the prior art, fine simulation cannot be performed in time and space by calculating through quarternary or annual data is overcome. Related technical advantages are described in detail below in connection with specific embodiments.
Drawings
Fig. 1 is a flow chart of the present application.
Fig. 2 is a flowchart of step S1 of the present application.
Fig. 3 is a flowchart of step S2 of the present application.
Fig. 4 is a flowchart of step S3 of the present application.
Fig. 5 is a flowchart of step S4 of the present application.
Fig. 6 is a flowchart of step S5 of the present application.
Fig. 7 is a flowchart of step S6 of the present application.
Fig. 8 is a flowchart of step S7 of the present application.
Detailed Description
The innovations of the present invention are described below in conjunction with specific cases, which are abbreviated as such, as will be known or should be known to those skilled in the art.
As shown in fig. 1, a method for fine dynamic assessment of toughness of urban flood disaster response is provided, which comprises the following steps:
s1, constructing a toughness comprehensive evaluation system based on a 4R measurement system of toughness, wherein the toughness comprehensive evaluation system at least comprises a target layer, a criterion layer and an element layer, the element layer comprises N evaluation indexes, and N is a natural number. It should be noted that the at least predetermined evaluation indexes are constructed according to a hydrohydrodynamic coupling model, and the model output result is used as an independent variable;
s2, dividing the type of the evaluation index and constructing a quantitative calculation formula and a weighting method of the evaluation index aiming at each evaluation index of the element layer;
s3, acquiring basic data of a target area, constructing a hydrological hydrodynamic coupling model of the target area, and simulating by taking the basic data as input data to acquire process data of the target area for reflecting the flood disaster space-time evolution;
Step S4, calculating the numerical value of each evaluation index at each moment one by one based on the basic data and the process data, giving weight, carrying out rasterization and dynamic processing on the evaluation index, and calculating the numerical values of the criterion layer and the target respectively;
s5, dividing the target area into at least two scales, and respectively calculating the average value of the toughness surface of each scale; fitting the toughness dynamic process of the urban flood disaster response of different scales, and establishing a multi-scale urban flood disaster response toughness curve;
step S6, dividing a toughness threshold value and setting a toughness grade for each scale, evaluating the toughness grade of each grid of a target area with different scales, and outputting an evaluation result;
and S7, identifying urban flood disaster toughness weak areas and difference characteristics thereof at all moments based on the evaluation result, analyzing influence factors of the toughness weak areas, and constructing a toughness improvement strategy set.
It should be noted that, in the present embodiment, steps S1 to S7 are merely for convenience of description, and the order or juxtaposition may be exchanged without a data flow limitation relationship or a logic limitation relationship. For example, step S1 and step S2 have a precedence relationship, but step S3 and steps S1 to S2 may be parallel or the order may be exchanged. In other words, step S1 and step S2 to the process of constructing the index system, step S3 is the process of constructing the coupling model. The data flows and the logic are not in strict order.
In this embodiment, in the prior art, an evaluation frame is established based on a PSR (pressure-transition-response) frame or a flood risk frame (disaster-causing-disaster-resisting-carrying-disaster), and no system takes the toughness into consideration; based on the first principle of toughness, the embodiment constructs an index system based on 4R toughness theory, namely robustness, redundancy, intelligence and recoverability (rapidity), from the basic elements forming the toughness, so that evaluation of any basic characteristics of the toughness can be realized, and a more specific strategy can be provided for formulating a toughness improvement strategy.
By the scheme of the embodiment, more accurate simulation can be performed on the time scale and the space scale. On the time scale, the prior art mainly focuses on the change of the toughness of the year, but the embodiment can evaluate the change process of the toughness of any time of any precipitation, and has finer time granularity and higher precision. In terms of space scale, most of the prior art can only realize toughness characterization of administrative area scale, and can realize kilometer scale characterization at most, but the embodiment carries out toughness assessment of grid scale based on hydrodynamics simulation results, and theoretically, only fine basic data are needed, so that toughness assessment of any space scale of a target city, such as sub-meter scale, 30m, 90m and kilometer scale, can be realized. Therefore, the calculation of the index is more accurate, and more accurate toughness assessment can be provided.
As shown in fig. 2, according to an aspect of the present application, the step S1 is further:
step S11, a toughness measurement system based on toughness screens toughness measurement indexes from the existing toughness research literature to form a toughness index candidate set;
step S12, constructing an index independence checking method set, and selecting at least one index independence checking method to perform correlation analysis on the toughness indexes in the toughness index candidate set to obtain a preferable toughness index candidate set;
step S13, performing independence test on the optimized toughness indexes, and forming an element layer of a toughness comprehensive evaluation system based on the toughness indexes passing the test, wherein an independence calculation formula is as follows: i 0 =1-abs（C）；I 0 Is an independence index; c is the Pearson's correlation coefficient,C∈[-1,1]the method comprises the steps of carrying out a first treatment on the surface of the abs represents absolute value;
step S14, forming a target layer, a criterion layer and an element layer of the toughness comprehensive evaluation system,
the target layer is the toughness of coping with urban flood disasters,
the criterion layer comprises robustness, intelligence, redundancy and restorability;
the element layer comprises actual flood control and drainage capability, economic system disaster resistance capability, personnel disaster resistance capability, forecasting capability, early warning capability, previewing capability, planning capability, flood control capability redundancy, infrastructure redundancy, disaster recovery capability and emergency rescue capability.
Unlike the prior art, the prior art index is mostly derived from the data set of the statistical communique data/public release, and the time is more lag; in the embodiment, most of simulation data are adopted, so that the toughness state under any situation can be evaluated, the rainfall process is faithfully measured, the rainfall process is designed, the real-time dynamic evaluation of toughness can be realized by combining the rainfall real-time monitoring data, the evaluation timeliness is greatly improved, and the dynamic management of flood risk is supported.
For some of the evaluation index advantages of the element layer, the following is exemplified:
the flood control and drainage capacity is characterized by the prior art based on the flood control and drainage planning of the target city, such as 50 years, the storm protection capacity under the corresponding standard is obtained, and the drainage capacity of the drainage facility is characterized by adopting the rainfall.
In the embodiment, the two-dimensional hydrologic-hydrodynamic coupling model is constructed to simulate the flood control and drainage capacity of the target city, various flood control and drainage facilities such as embankments, sluice gates, river channels, drainage pump stations, drainage pipe networks and the like can be comprehensively considered, and the actual flood control and drainage capacity of the target city is evaluated by simulating hydrologic elements such as the water accumulation depth, the water flow speed and the like of each grid of the target city under different rainfall conditions in combination with the scheduling rules of the water conservancy facilities;
The disaster resistance of the economic system is achieved, in the prior art, statistical data of annual flood disaster loss of a target city are obtained through statistical gazettes, such as direct economic loss, and the direct economic loss is calculated to account for the proportion of GDP in the current year for characterization.
According to the embodiment, the ponding depth of each grid is obtained by combining the simulation result of the two-dimensional hydrologic hydrodynamic coupling model, the detailed asset space distribution data are constructed by combining POI data acquired by a big data technology, the loss rate of each grid is obtained through the vulnerability curves of different assets, namely the mapping relation between the submerged depth and the loss rate of each type of asset, the information of each grid comprises the asset type, the asset number and the ponding depth, so that the loss value corresponding to the grid can be obtained, and the loss value is used as the disaster resistance capacity of an economic system to be characterized.
Compared with the prior art, the space-time granularity of the result is more accurate, the prior art can only realize the vulnerability characterization of annual scale and administrative region level, the embodiment can realize the disaster resistance capability characterization of an economic system of any grid of a target city at any moment of scene rainfall, and the characteristics of flood disaster-causing intensity, economic density, asset type and the like are comprehensively considered;
In the prior art, statistical data of annual flood disaster loss of a target city are obtained through statistical gazettes, such as population ratio of disaster affected population or population ratio of different age groups (such as children aged 14 and aged 65) and are characterized through the data.
According to the embodiment, through combining simulation results of a two-dimensional hydrologic hydrodynamic coupling model, the ponding depth and the flow velocity of each grid are obtained, the flood risk value of each grid is calculated by combining the land utilization type of each grid, and the number of disaster-stricken population of each grid is obtained and is used as personnel disaster-resistant capacity to be represented by combining detailed population space distribution data;
forecasting capability, early warning capability, previewing capability and planning capability are collectively called as 'four-prediction' capability in the water conservancy industry. The index system constructed by the prior art does not comprehensively consider the concepts, and the main difficulty is the representation of the capability and the rasterization treatment. The system of the embodiment provides a computing and spatialization method of four pre-treatment capability, which is an important practice of the current water conservancy treatment concept.
The flood control capacity redundancy is characterized by mainly using the capacity of the flood storage area in the prior art, is a static quantity, and cannot embody the dynamic change process of the flood control capacity redundancy, because the capacity of the flood storage area is reduced along with the evolution of flood disasters, and the redundancy is continuously reduced. According to the embodiment, the simulation of the flood control capacity redundancy change process can be realized through the constructed two-dimensional hydrologic hydrodynamic coupling model, meanwhile, the comprehensive application of the three-dimensional space of the urban area, such as underground space, wetland, LID facilities and the like, can be further considered, the three-dimensional hydrodynamic coupling model is brought into the model to perform simulation, the flood control capacity change quantity of the target city before and after flood control in the flood storage area and the three-dimensional space is compared, the flood control capacity change quantity is used as the representation of the flood control capacity redundancy, and the dynamic process of each grid flood control capacity change is obtained.
The disaster recovery capability is more abstract in the characterization indexes of the prior art, such as the loss rate and the average income, the lower the personnel loss rate is considered, the higher the dominant income is, the quicker the employment recovery of the whole city is considered, in practice, from the first principle of toughness, the disaster recovery capability mainly refers to the post-disaster recovery capability of the city function, the employment recovery is only one aspect, the most fundamental factor is that flood rapidly subsides, such as flood peak subsidence and ground water subsidence, so that the whole city function rapidly recovers and normally operates, therefore, the disaster recovery capability adopted in the embodiment is the subsidence time of the waterlogging of the target city, and also the simulation result based on the two-dimensional hydrodynamics coupling model comprehensively considers various urban drainage facilities, such as drainage pump station, drainage pipe network, mobile drainage vehicle, river channel and the like, the resolution time of the city water is calculated, the disaster recovery is obviously different from other researches, the employment recovery of other researches cannot be reflected in time, and the disaster recovery rate cannot be quantified through simulation.
The emergency rescue capability in the prior art is characterized by an emergency plan, the number of emergency departments and the like, is also indirectly characterized, and cannot be specifically quantified.
According to the embodiment, the two-dimensional hydrologic hydrodynamic coupling model is combined, the water accumulation depth simulation of roads in each grid at any scene and at any time point can be realized, the passing speed acquisition of the big data technology and the passing speed attenuation model under the rainfall condition are further combined, the passing speed acquisition of the roads in each grid at any time is realized, therefore, the space distribution of the passing speeds of the roads in each grid at any scene and at any time is obtained, the space positions of emergency departments such as fire protection, hospitals and public security are obtained through the big data technology based on the space distribution, the calculation of the time of reaching the intended grid of each emergency department can be realized by combining the shortest path method of the ArcGIS, the accessibility space distribution map of the emergency department of each grid is drawn, the space distribution map is obviously different from the technical characteristics of the prior art, the representation of the emergency rescue capability is more specific, and the rasterization and the dynamics of the capability are realized.
As shown in fig. 3, according to an aspect of the present application, the step S2 is further:
step S21, dividing the evaluation index types, including quantitative type indexes and qualitative type indexes, of each evaluation index of the element layer, and dividing the evaluation index types into raster data and vector data according to the data types of the evaluation indexes;
the quantitative indexes refer to specific numerical values which can be obtained through statistical gazette or simulation, the qualitative indexes refer to the indexes which need to be further qualitatively evaluated through subjective judgment, and the quantitative indexes comprise the inter-flood control and drainage capacity, the disaster resistance capacity of an economic system, the disaster resistance capacity of personnel, the forecasting capacity, the flood control capacity redundancy, the infrastructure redundancy, the disaster recovery capacity and the emergency rescue capacity.
The qualitative indexes comprise early warning capability, previewing capability and planning capability, the data can be further divided into raster data and vector data according to different statistical apertures of the indexes, the raster data comprise actual flood control and drainage capability, economic system disaster resistance capability, personnel disaster resistance capability, flood control capability redundancy, infrastructure redundancy, disaster recovery capability and emergency rescue capability, and the vector data comprise forecasting capability, early warning capability, previewing capability and planning capability.
S22, constructing an evaluation index weighting method including a subjective weighting method, and constructing a relative importance degree matrix based on the relative importance degrees among different evaluation indexes; calculating weights of different evaluation indexes by adopting a chromatographic analysis method;
preferably, the subjective weighting method is a Delphi method and an analytic hierarchy process, the Delphi method is used for obtaining a relative importance degree matrix among different indexes, and the analytic hierarchy process is used for calculating weights of the different indexes.
Step S23, constructing a quantitative calculation formula for each evaluation index.
According to one aspect of the present application, the quantitative calculation formula of each evaluation index in step S23 is:
flood control and drainage capacity I 1 =（h max -h i ）/（h max -h min ）； h max The maximum ponding depth in all grids of the target area is set;h i the accumulated water depth of the ith grid of the target area;h min the minimum ponding depth in all grids of the target area is set;
disaster resistance capability of economic system:
；
TA i the total amount of assets for the ith grid of the target area,f i vulnerability functions relating to water depth and asset type for the ith grid of the target area, for calculating asset loss rates,h i for the water accumulation depth of the ith grid of the target area,P i asset type for the ith grid of the target area,nthe total number of grids for the target area;
Personnel disaster resistance capability I 3 =（TP max -TP i ）/（TP max -TP min ）；
TP i =POP i ×α i When (when)HR i Alpha < 2.5 i =HR i 2.5 whenHR i When the temperature is more than or equal to 2.5,α i =1；
HR i =d i （v i +0.5）+DF i ，
TP i the number of disaster affected population for the ith grid of the target area,TP max for the maximum number of disaster recovery population for all grids in the target area,TP min the minimum disaster population number of all grids in the target area;POP i the population of the ith grid for the target area,α i disaster-stricken population proportion of the ith grid of the target area;HR i the method comprises the steps of taking a flood risk level coefficient of an ith grid of a target area as a flood risk level coefficient;d i for the water accumulation depth of the ith grid of the target area,v i for the flow rate of the ith grid of the target area,DF i the mud stone factor of the ith grid of the target area;
the forecasting capability is calculated according to the control area of the monitoring station network, and the control area is calculated based on the Taylor polygon. I.e. forecasting ability I 4 =1-(A o /A)；A o The control area of the first rain station for the target area o,Ais the total area of the target area;
early warning capability I 5 = 0.2×X 1 +0.2×X 2 +0.2×X 3 +0.2×X 4 +0.2×X 5 ；X 1 Is the coverage rate of the television,X 2 Is broadcast coverage rate,X 3 Is a mobile phone with a popular rate,X 4 In order for a fixed-line telephone to be popular,X 5 is the popularity of the Internet;
X 1 =T 1 /T；T 1 the number of families with televisions is T, which is the total number of families in the target city;
X 2 =T 2 /T；T 2 to have the number of families of television, X 3 =T 3 /T；T 3 In order to have the number of households of the mobile phone,
X 4 =T 4 /T；T 4 a number of households having a fixed telephone; x is X 5 = T 5 /T；T 5 Is the number of households that own the internet.
Previewing capability I 6 =1 or 0, if the capability of construction, simulation and visualization is provided, it is 1, otherwise 0. The information is used for researching whether the target area has the capability of constructing, simulating and visualizing the real scenes such as the topography, the building, the hydraulic engineering and the like of the area where the preview object is located and the flood disaster scenes of different types and different orders by means of the prior art.
Capacity of plan I 7 =1 or 0; it is necessary to investigate whether the target city has a flood disaster related emergency plan, if so, it is 1, otherwise it is 0.
Flood protection capability redundancy I 8 =（Δh max -Δh i ）/（Δh max -Δh min ）；Δh max For the maximum value of the accumulated water depth difference of all grids in the target area before and after the three-dimensional space and the flood storage area participate in regulation, delta h is the accumulated water depth difference of the ith grid in the target area before and after the three-dimensional space and the flood storage area participate in regulation, delta h is min The minimum value of the accumulated water depth difference before and after the grid participates in regulation in the three-generation space and the flood storage area is set for all grids in the target area; the redundancy of flood control capability requires investigation of the regulation capacity and distribution of four types of three-living spaces, namely sponge facilities, wetlands, underground traffic tunnels and civil air defense engineering, of a target city and a flood storage area.
Infrastructure redundancy I 9 = 0.44 X 6 +0.26X 7 +0.18 X 8 +0.12.X 9 ；X 6 Reserve capacity for the power system,X 7 For providingSpare capacity of water system,X 8 Reserve capacity for communication system,X 9 The richness of the traffic road network is achieved;
X 6 =BE i /E i ；BE i for the power system backup capacity of the i-th administrative unit,E i the standby capacity of the water supply system is whether the target city has a standby water source after urban water supply source pollution caused by flood disasters, whether the power system has the standby power supply of the water supply system after damage and whether the water supply network has a standby network after leakage, the statistical data of the minimum administrative unit of the target city needs to be researched, the standby capacity is recorded as 1/3, the two capacities are 2/3, the total capacities are all 1, the standby capability of the communication system needs to investigate whether the target city is provided with emergency communication equipment or not, at least comprises one of a disaster resistant base station, an emergency communication vehicle, a high-throughput satellite portable station, an unmanned aerial vehicle high-altitude base station and a vehicle-mounted super radio station, if the emergency capability is provided with the disaster resistant base station, the emergency communication vehicle, the high-altitude base station is 1, otherwise the emergency capability is 0, further, the richness of the traffic road network needs to be determined by counting the sum of the outbound degree and the inbound degree of the nodes of the internal traffic road network of the minimum administrative unit of the target city, and the calculation formula is X 8 =（∑D max -∑D i ）/（∑D max -D min ），∑D max Sigma is the maximum value of the sum of the traffic node degrees in all the minimum administrative units in the current cityD i Sigma is the sum of traffic node degrees in the ith minimum administrative unit in the current cityD min The minimum value of the sum of the traffic node degrees in all the minimum administrative units in the current city;
the disaster recovery capacity is determined based on the simulation result of a two-dimensional hydrohydrodynamic coupling model. Disaster recovery ability I 10 =（t max -t i ）/（t max -t min ）；t max The depth of the accumulated water in all grids of the target area is reduced to a time maximum value below 0.15 m;t i the depth of the accumulated water of the ith grid of the target area is reduced to less than 0.15 m;t min the depth of water accumulated in all grids of the target area is reduced to a time minimum value below 0.15 m.
Emergency rescue Capacity I 11 = (F i +H i +P i )/3；F i Fire department reachability for the ith grid of the target area,H i for hospital reachability of the ith grid of the target area,P i public security department reachability of the ith grid of the target area.
The accessibility needs to be comprehensively determined based on a ponding depth simulated by a two-dimensional hydrologic dynamic model, spatial positions of fire protection, hospitals and public security, shortest path analysis, road network passing speed acquired by a big data technology and a passing speed attenuation model, further, the shortest path analysis needs to be realized based on a network analysis function in arcgis, the spatial positions of fire protection, hospitals and public security need to be acquired based on the big data technology by calling an API interface of a positioning function from a high/hundred-degree map open platform, the road network passing speed needs to be acquired by calling an API interface of a path planning function from the high/hundred-degree map open platform, and the passing speed attenuation model is as follows: v= (v) 0 /2)tanh((-x+a)/b)+ (v 0 /2)，vIs the driving speed;v 0 is the design vehicle speed of the place;xis the depth of accumulated water;ais the median of the critical water accumulation depth for the vehicle to stagnate;bis an attenuation elastic coefficient, represents the speed of the vehicle along with the attenuation of the water depth, generally takes 3 to 5,bthe smaller the value of (c), the faster the speed decay.
As shown in fig. 4, according to an aspect of the present application, the step S3 is further:
s31, acquiring basic data of a target area, wherein the basic data comprise water system distribution, river section, topography, land utilization, drainage pipe network arrangement and pipe diameter, gate pump position and scheduling rules, position and scale of a polder region and a flood storage region, relationship between water surface position and water level reservoir capacity of a lake, rainfall, evaporation, water level and flow data of the target area;
s32, constructing a hydrokinetic coupling model of a target area, wherein the hydrokinetic coupling model comprises a MIKE model and an Inforworks model; HEC-RAS, SWAT, etc. may also be used in some scenarios.
Step S33, calibrating, verifying and simulating a hydrologic and hydrodynamic model through basic data, and outputting a flood evolution process with a preset calculation step length according to given rainfall conditions; and obtaining process data of the space-time evolution of the flood disaster.
The construction of the two-dimensional hydrokinetic coupling model comprises one-dimensional hydrokinetic model construction and two-dimensional hydrokinetic model construction, further, the two-dimensional hydrokinetic model of a target city is required to be calibrated and verified according to the observed rainfall, evaporation, flow and water level data of historical flood events, the correlation coefficient R of a simulation value and an actual measurement value and the Nash efficiency coefficient NSE value are ensured to be more than 0.75, the peak relative error PE and the flood relative error VE are within 30%, the water level error of a water accumulation point is within 10%, the position accuracy of the water accumulation point is above 85%, further, the simulation of the evolution process is required to output a flood evolution process with a given calculation step length according to given rainfall conditions, further, the flood evolution process is a space-time change process of the water accumulation depth and the flow rate in the target city, and further, the flood evolution process is not longer in time as required to be in a tif format.
As shown in fig. 5, according to an aspect of the present application, the step S4 is further:
step S41, acquiring process data and basic data, calculating the evaluation index value of the element layer for each grid at each moment,
Step S42, rasterizing the vector class indexes by using a conversion tool rasterizing conversion tool in the arcgis tool;
step S43, carrying out dynamic processing on the data of the grid class; the dynamic treatment process comprises the following steps: setting a required time step, determining time nodes to be calculated, calculating index values at each time node, and arranging calculation results according to the time nodes to obtain a dynamic change process of the index;
and S44, respectively calculating the robustness, the intelligence, the redundancy, the restorability and the toughness of each scale of the target region according to the determined index weight and the index value.
Specifically, according to the above calculation formula, the weights of the criterion layer and the element layer in the evaluation index system are calculated. Furthermore, the grid indexes comprise actual flood control and drainage capacity, economic system disaster resistance capacity, personnel disaster resistance capacity, flood control capacity redundancy, disaster recovery capacity and emergency rescue capacity, dynamic processing is needed, the time required for setting is not long, time nodes needing to be calculated are determined, index values at each time node are calculated, and the calculation result is arranged according to the time nodes to obtain a dynamic change process of the indexes; and for vector class index forecasting capability, early warning capability, previewing capability, planning capability and infrastructure redundancy, the transformation tool-to-grid-surface rotation grid in the arcgis tool is used for realizing the rasterization processing of vector data.
As shown in fig. 6, according to an aspect of the present application, the step S5 is further:
step S51, dividing a target area into at least two scales, including a city scale, a county scale and a street scale; respectively calculating the average value of the toughness surface of each scale;
step S52, fitting the toughness values of different spatial scales by using a curve fitting module, screening a fitting function conforming to an expected target, and establishing a multi-scale urban flood disaster coping toughness change curve;
and step S53, analyzing the sensitivity of the parameters in the fitting function, and determining the physical meaning of the parameters in the fitting function.
For example, respectively calculating the average value of the toughness surfaces of the city scale, the county scale and the street scale; then using CurveExpert Professional 2.6.5 software or other curve fitting tools to fit toughness values of different spatial scales, screening the best fitting function, and establishing a toughness change curve of the multi-scale urban flood disaster; and finally, analyzing the sensitivity of the urban flood disaster response toughness to the parameters in the fitting function, and determining the physical meaning of the parameters in the fitting function by combining the mathematical meaning of the parameters of the fitting function.
As shown in fig. 7, according to an aspect of the present application, the step S6 is further:
Step S61, dividing a toughness threshold value and setting a toughness grade according to each scale, and taking a toughness relative value as a dividing standard in county scales and street scales;
step S62, comprehensively determining the membership degree of the flood disaster to the toughness by using a natural break method, a cloud model and a physical extension model, and defining a toughness grade division threshold;
and step S63, determining and evaluating the toughness grade of each grid area under different scales to obtain an evaluation result.
In a certain embodiment, for the toughness of the city scale, a toughness interval is defined, wherein the toughness interval is respectively low toughness (0, 0.3), medium toughness (0.3, 0.7) and high toughness (0.7,1), further, the toughness grade of the whole city is evaluated, for the toughness values of the county scale, the street scale and the grid scale, a toughness relative value is adopted as a dividing standard, further, the dividing standard comprehensively determines the membership degree of the flood disaster to the toughness by using a natural breaking method, a cloud model and a matter extension model, a toughness grade dividing threshold is defined, and further, the toughness grade is divided into low toughness, medium toughness and high toughness according to the toughness grade dividing threshold, and the toughness grades under different scales are evaluated.
In a further embodiment, the process is as follows:
Step 601, for urban scale toughness, defining a toughness interval, namely low toughness (0, 0.3), medium toughness (0.3, 0.7) and high toughness (0.7,1), and further evaluating the toughness grade of the whole city;
step 602, regarding the toughness values of county scale, street scale and grid scale, adopting the relative toughness values as dividing standard, further, the dividing standard comprehensively determines the membership degree of the flood disaster to the toughness by using a natural break method, a cloud model and a primitive extension model, defining a toughness grade dividing threshold, further, dividing the toughness grade into low toughness, medium toughness and high toughness according to the toughness grade dividing threshold, and evaluating the toughness grades under different scales.
As shown in fig. 8, according to an aspect of the present application, the step S7 is further:
step S71, selecting representative moments of different stages of flood disaster development according to a preconfigured rule, and identifying weak areas of urban flood disaster toughness at different times and spatial changes thereof;
step S72, establishing a grading identification method of the influence factors of the toughness weak area; positioning basic characteristics affecting a toughness weak area, and realizing primary identification and secondary identification of factors affecting the toughness weak area; the grading identification method is characterized in that the basic characteristics affecting the weak area of the toughness are positioned according to the evaluation results of robustness, intelligence, redundancy and recoverability at the representative moment, the primary identification of the influence factors of the weak area of the toughness is realized, and further, the specific indexes affecting the weak area of the toughness are analyzed according to the positioned characteristics, so that the secondary identification of the influence factors of the weak area of the toughness is realized;
Step 73, providing a toughness improvement strategy of grid scale based on the influence factors of the toughness weak areas identified by each level, and constructing a toughness improvement strategy set.
In a further embodiment, step S71 specifically includes the steps of:
step S71a, constructing a rule set, selecting representative moments of different stages of flood disaster development according to each rule in the rule set, and giving toughness simulation data with at least 3 accuracies according to each representative moment;
step S71b, constructing a toughness change threshold set, dividing a research area into at least 2 subregions according to toughness values, and forming a toughness value contour line of the research area;
step S71c, extracting toughness simulation data of each precision, calculating whether the change value of the boundary line of the toughness region is smaller than a threshold value under different precision, and if so, selecting the toughness simulation data with low precision; repeating the judging process until the toughness simulation data with the lowest precision is selected;
step S71d, based on the different toughness simulation data of each subarea, giving an optimal simulation precision distribution map of the research area;
and step S71e, based on the optimal simulation precision distribution diagram, weak areas and spatial changes of the toughness of the urban flood disasters at different moments are given.
In other words, since the conditions of the respective blocks (sub-regions) of the investigation region are different, at different accuracies, some regions are not obvious in the toughness variation at high accuracy and low accuracy, and the toughness distribution is relatively uniform. In some areas, in the high-precision map, toughness can be obviously differentiated, and if some areas have obviously different toughness from adjacent areas, a plurality of units with obviously different toughness values are formed. Therefore, in order to increase the speed and efficiency of toughness simulation, in some cases, high-precision simulation is not required for the global, and of course, low-precision simulation cannot be performed for the global. Therefore, different simulation precision is provided for different places, so that the simulation precision and the requirements of users can be met, and the simulation speed can be improved.
In some embodiments, step S71a further comprises:
constructing a simulation scene set aiming at each moment, and forming simulation data with at least three accuracies aiming at each key moment of each simulation scene; and forming a mapping relation between the simulation scene and the simulation precision.
In this embodiment, the technical problem to be solved is that even in the same area, the distribution of the toughness values may change in different scenes, in other words, the distribution of the toughness values in space is related to not only various parameters of the space but also rainfall, flood and other factors. Therefore, aiming at different scenes, the mapping relation between different optimal simulation precision and the simulation scene is constructed, and the optimal simulation precision of each subarea or subunit of the research area under the current scene can be given.
Therefore, different simulation precision is formed through different subareas, so that the simulation speed can be increased. In some embodiments, parallel simulation may be implemented by distributed computing. If the toughness value of a certain region is changed after the accuracy is improved, the situation of the region is relatively complex, and the regions can be subjected to important research and high-accuracy simulation.
An example is given to illustrate the data processing process of the present application.
Basic data of a target city or region is collected and processed, wherein the basic data comprise water system distribution, river section, terrain, land utilization, drainage pipe network arrangement and pipe diameter, gate pump position and scheduling rules, levee region and impounded flood region position and scale, lake water surface position and water level reservoir capacity relation, rainfall, evaporation, water level, flow data and the like.
And constructing a two-dimensional hydrological hydrodynamic coupling model of the target city or region, inputting basic data into the model by utilizing the MIKE model or other model software, and calibrating and verifying the model to ensure the accuracy of a simulation result.
According to a given rainfall condition, a two-dimensional hydrologic hydrodynamic coupling model is operated, a flooding evolution process with a given calculation step length, namely a space-time variation process of ponding depth and flow velocity in a target city or region is output, and the evolution process is output as a tif format according to a required time node.
According to the toughness evaluation model disclosed by the invention, evaluation indexes are calculated one by one, wherein the evaluation indexes comprise actual flood control and drainage capacity, economic system disaster resistance capacity, personnel disaster resistance capacity, forecasting capacity, early warning capacity, previewing capacity, planning capacity, flood control capacity redundancy, infrastructure redundancy, disaster recovery capacity, emergency rescue capacity and the like, and the grid indexes are dynamically processed, namely index values at each time node are calculated.
And respectively calculating the robustness, the intelligence, the redundancy, the restorability and the toughness of the grid scale and the city scale of the target city or region according to the determined index weight and index value, and outputting the result as a tif format or a table format.
And respectively calculating the average value of the toughness surfaces of the target city or region city scale, the county scale and the street scale, fitting the toughness values of different space scales by using a curve fitting tool, screening the optimal fitting function, and determining the physical meaning of the parameters in the function.
Dividing the toughness grades into low toughness, medium toughness and high toughness according to the toughness grade dividing threshold, evaluating the toughness grades under different scales, and outputting the result in tif format or table format.
Selecting representative moments of different stages of flood disaster development, identifying weak areas and spatial changes of the weak areas of the flood disaster toughness of target cities or areas at different moments, analyzing main influencing factors of the weak areas of the flood disaster toughness, providing a grid-scale toughness improvement strategy, and outputting results in a tif format or a table format.
According to another aspect of the present application, there is provided a refined dynamic assessment system of toughness in response to urban flood disasters, comprising:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,
the memory stores instructions executable by the processor for execution by the processor to implement the method for refined dynamic assessment of toughness against urban flood disasters of any one of the above-described technical schemes.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.
Claims (8)
1. The method for the fine dynamic evaluation of the toughness of the urban flood disaster response is characterized by comprising the following steps of:
S1, constructing a toughness comprehensive evaluation system based on a 4R measurement system of toughness, wherein the toughness comprehensive evaluation system at least comprises a target layer, a criterion layer and an element layer, the element layer comprises N evaluation indexes, and N is a natural number;
s2, dividing the type of the evaluation index and constructing a quantitative calculation formula and a weighting method of the evaluation index aiming at each evaluation index of the element layer;
s3, acquiring basic data of a target area, constructing a hydrological hydrodynamic coupling model of the target area, and simulating by taking the basic data as input data to acquire process data of the target area for reflecting the flood disaster space-time evolution;
step S4, calculating the numerical value of each evaluation index at each moment one by one based on the basic data and the process data, giving weight, carrying out rasterization and dynamic processing on the evaluation index, and calculating the numerical values of the criterion layer and the target respectively;
s5, dividing the target area into at least two scales, and respectively calculating the average value of the toughness surface of each scale; fitting the toughness dynamic process of the urban flood disaster response of different scales, and establishing a multi-scale urban flood disaster response toughness curve;
step S6, dividing a toughness threshold value and setting a toughness grade for each scale, evaluating the toughness grade of each grid of a target area with different scales, and outputting an evaluation result;
Step S7, identifying urban flood disaster toughness weak areas and difference characteristics thereof at all times based on the evaluation result, analyzing influence factors of the toughness weak areas, and constructing a toughness improvement strategy set;
the step S2 is further:
step S21, dividing the evaluation index types, including quantitative type indexes and qualitative type indexes, of each evaluation index of the element layer, and dividing the evaluation index types into raster data and vector data according to the data types of the evaluation indexes;
s22, constructing an evaluation index weighting method including a subjective weighting method, and constructing a relative importance degree matrix based on the relative importance degrees among different evaluation indexes; calculating weights of different evaluation indexes by adopting a chromatographic analysis method;
s23, constructing a quantitative calculation formula for each evaluation index;
the quantitative calculation formula of each evaluation index in step S23 is:
flood control and drainage capacity I 1 =（h max -h i ）/（h max -h min ）； h max The maximum ponding depth in all grids of the target area is set;h i the accumulated water depth of the ith grid of the target area;h min the minimum ponding depth in all grids of the target area is set;
disaster resistance capability of economic system:
；
TA i the total amount of assets for the ith grid of the target area,f i vulnerability functions relating to water depth and asset type for the ith grid of the target area, for calculating asset loss rates, h i For the water accumulation depth of the ith grid of the target area,P i asset type for the ith grid of the target area,nthe total number of grids for the target area;
personnel disaster resistance capability I 3 =（TP max -TP i ）/（TP max -TP min ）；
TP i =POP i ×α i When (when)HR i Alpha < 2.5 i =HR i 2.5 whenHR i When the temperature is more than or equal to 2.5,α i =1；
HR i =d i （v i +0.5）+DF i ，
TP i the number of disaster affected population for the ith grid of the target area,TP max for the maximum number of disaster recovery population for all grids in the target area,TP min the minimum disaster population number of all grids in the target area;POP i the population of the ith grid for the target area,α i is the subject of the ith grid of the target areaDisaster population proportion;HR i the method comprises the steps of taking a flood risk level coefficient of an ith grid of a target area as a flood risk level coefficient;d i for the water accumulation depth of the ith grid of the target area,v i for the flow rate of the ith grid of the target area,DF i the mud stone factor of the ith grid of the target area;
forecasting ability I 4 =1-(A o /A)；A o For the control area of the o-th rain station of the target area,Ais the total area of the target area;
early warning capability I 5 = 0.2×X 1 +0.2×X 2 +0.2×X 3 +0.2×X 4 +0.2×X 5 ；X 1 Is the coverage rate of the television,X 2 Is broadcast coverage rate,X 3 Is a mobile phone with a popular rate,X 4 In order for a fixed-line telephone to be popular,X 5 is the popularity of the Internet;
previewing capability I 6 Case capability i=1 or 0 7 =1 or 0;
flood protection capability redundancy I 8 =（Δh max -Δh i ）/（Δh max -Δh min ）；Δh max For the maximum value of the accumulated water depth difference of all grids in the target area before and after the three-dimensional space and the flood storage area participate in regulation, delta h is the accumulated water depth difference of the ith grid in the target area before and after the three-dimensional space and the flood storage area participate in regulation, delta h is min The minimum value of the accumulated water depth difference before and after the grid participates in regulation in the three-generation space and the flood storage area is set for all grids in the target area;
infrastructure redundancy I 9 = 0.44 X 6 +0.26X 7 +0.18 X 8 +0.12.X 9 ；X 6 Reserve capacity for the power system,X 7 Reserve capacity of the water supply system,X 8 Reserve capacity for communication system,X 9 The richness of the traffic road network is achieved;
disaster recovery ability I 10 =（t max -t i ）/（t max -t min ）；t max The depth of the accumulated water in all grids of the target area is reduced to a time maximum value below 0.15 m;t i the depth of the accumulated water of the ith grid of the target area is reduced to less than 0.15 m;t min a time minimum value for the depth of water accumulated in all grids of the target area to subside to below 0.15 m;
emergency rescue Capacity I 11 = (F i +H i +P i )/3；F i Fire department reachability for the ith grid of the target area,H i for hospital reachability of the ith grid of the target area,P i public security department reachability of the ith grid of the target area;
the fire department reachability calculation formula is as follows: f (F) i =（t F，max -t F，i ）/（t F，max -t F，min ），t F,max For the maximum fire emergency response time in all grids of the target city,t F i, fire emergency response time for the ith grid of the target city,t F,min for the minimum value of fire emergency response time in all grids of the target city, the fire department reachability calculation formula is as follows: h i =（t H,max -t H,i ）/（t H,max -t H,min ），t H,max For the maximum fire emergency response time in all grids of the target city, t H i, Fire emergency response time for the ith grid of the target city,t H,min for the minimum value of fire emergency response time in all grids of the target city, the fire department reachability calculation formula is as follows: p (P) i =（t P,max -t P,i ）/（t P,max -t p,min ），t P,max For the maximum fire emergency response time in all grids of the target city,t P i, fire emergency response time for the ith grid of the target city,t P,min the minimum fire emergency response time in all grids of the target city is obtained.
2. The method for the refined dynamic assessment of toughness against urban flood disasters according to claim 1, wherein the step S1 is further:
step S11, a toughness measurement system based on toughness screens toughness measurement indexes from the existing toughness research literature to form a toughness index candidate set;
step S12, constructing an index independence checking method set, and selecting at least one index independence checking method to perform correlation analysis on the toughness indexes in the toughness index candidate set to obtain a preferable toughness index candidate set;
step S13, performing independence test on the optimized toughness indexes, and forming an element layer of a toughness comprehensive evaluation system based on the toughness indexes passing the test, wherein an independence calculation formula is as follows: i 0 =1-abs（C）；I 0 Is an independence index; c is the Pearson's correlation coefficient, C∈[-1,1]The method comprises the steps of carrying out a first treatment on the surface of the abs represents absolute value;
step S14, forming a target layer, a criterion layer and an element layer of the toughness comprehensive evaluation system,
the target layer is the toughness of coping with urban flood disasters,
the criterion layer comprises robustness, intelligence, redundancy and restorability;
the element layer comprises actual flood control and drainage capability, economic system disaster resistance capability, personnel disaster resistance capability, forecasting capability, early warning capability, previewing capability, planning capability, flood control capability redundancy, infrastructure redundancy, disaster recovery capability and emergency rescue capability.
3. The method for the refined dynamic assessment of toughness against urban flood disasters according to claim 2, wherein the step S3 is further:
s31, acquiring basic data of a target area, wherein the basic data comprise water system distribution, river section, topography, land utilization, drainage pipe network arrangement and pipe diameter, gate pump position and scheduling rules, position and scale of a polder region and a flood storage region, relationship between water surface position and water level reservoir capacity of a lake, rainfall, evaporation, water level and flow data of the target area;
s32, constructing a hydrokinetic coupling model of a target area, wherein the hydrokinetic coupling model comprises a MIKE model and an Inforworks model;
Step S33, calibrating, verifying and simulating a hydrologic and hydrodynamic model through basic data, and outputting a flood evolution process with a preset calculation step length according to given rainfall conditions; and obtaining process data of the space-time evolution of the flood disaster.
4. The method for fine dynamic assessment of toughness in response to urban flood disasters according to claim 3, wherein the step S4 is further:
step S41, acquiring process data and basic data, calculating the evaluation index value of the element layer for each grid at each moment,
step S42, rasterizing the vector class indexes by using a conversion tool rasterizing conversion tool in the arcgis tool;
step S43, carrying out dynamic processing on the data of the grid class; the dynamic treatment process comprises the following steps: setting a required time step, determining time nodes to be calculated, calculating index values at each time node, and arranging calculation results according to the time nodes to obtain a dynamic change process of the index;
and S44, respectively calculating the robustness, the intelligence, the redundancy, the restorability and the toughness of each scale of the target region according to the determined index weight and the index value.
5. The method for the refined dynamic assessment of toughness against urban flood disasters according to claim 4, wherein the step S5 is further:
Step S51, dividing a target area into at least two scales, including a city scale, a county scale and a street scale; respectively calculating the average value of the toughness surface of each scale;
step S52, fitting the toughness values of different spatial scales by using a curve fitting module, screening a fitting function conforming to an expected target, and establishing a multi-scale urban flood disaster coping toughness change curve;
and step S53, analyzing the sensitivity of the parameters in the fitting function, and determining the physical meaning of the parameters in the fitting function.
6. The method for the refined dynamic assessment of toughness against urban flood disasters according to claim 5, wherein the step S6 is further:
step S61, dividing a toughness threshold value and setting a toughness grade according to each scale, and taking a toughness relative value as a dividing standard in county scales and street scales;
step S62, comprehensively determining the membership degree of the flood disaster to the toughness by using a natural break method, a cloud model and a physical extension model, and defining a toughness grade division threshold;
and step S63, determining and evaluating the toughness grade of each grid area under different scales to obtain an evaluation result.
7. The method for the refined dynamic assessment of toughness against urban flood disasters according to claim 6, wherein the step S7 is further:
Step S71, selecting representative moments of different stages of flood disaster development according to a preconfigured rule, and identifying weak areas of urban flood disaster toughness at different times and spatial changes thereof;
step S72, establishing a grading identification method of the influence factors of the toughness weak area; positioning basic characteristics affecting a toughness weak area, and realizing primary identification and secondary identification of factors affecting the toughness weak area;
step 73, providing a toughness improvement strategy of grid scale based on the influence factors of the toughness weak areas identified by each level, and constructing a toughness improvement strategy set.
8. A system for the refined dynamic assessment of toughness in response to urban flood disasters, comprising:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,
the memory stores instructions executable by the processor for execution by the processor to implement the method of refined dynamic assessment of urban flood disaster response toughness of any one of claims 1 to 7.
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