A grey-green sponge facility optimization design method based on urban rain flood model and NSGA-III algorithm

Abstract

The application discloses a grey-green sponge facility optimization design method based on a city rain flood model and an NSGA-III algorithm, collects and arranges basic data such as a pipe network, terrain, rainfall and measured waterlogging accumulated water of a to-be-built area, constructs a city rain flood model of the to-be-built area, and carries out accuracy correction and testing of the model; the design scale, i.e. the maximum design scale, of the grey and green sponge facilities required to meet the sponge construction standard of the to-be-built area is determined through simulation; the benefit relationship of the grey and green facilities of different design scales to waterlogging accumulated water reduction and total runoff control is obtained through simulation by taking the maximum design scale as a constraint; curve fitting is carried out based on the relationship to establish a design scale-benefit polynomial function of the sponge facilities; the polynomial function and a cost function established above are taken as objective functions of a multi-objective optimization model, and an NSGA-III algorithm is used for iterative solution; and an optimal grey-green sponge configuration scheme is selected from a Pareto optimal solution set according to actual sponge construction standards of the to-be-built area and the principle of the lowest cost.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban waterlogging management and sponge facility optimization, and particularly relates to a grey-green sponge facility optimization design method based on a urban rainwater model and an NSGA-III algorithm. BACKGROUND

[0002] Under the dual effects of urban rapid development and frequent extreme weather, the phenomenon of "seeing the sea in the city" is becoming more and more serious, and the concept of sponge city provides an effective way to alleviate urban waterlogging problems. The sponge city construction project is huge and consumes a lot of funds, and the scheme configuration belongs to a high-dimensional multi-objective optimization problem. At present, most of the researches and practices rely on numerical models, and a plurality of LID schemes are set subjectively to perform simulation calculation, and then the optimal scheme is selected. However, the design and application of sponge facilities often involve the joint action of multiple objectives and multiple factors, and there is a mutual restriction between the facilities and economic benefits, so it is difficult to achieve real optimization by relying on a limited number of schemes set subjectively. In addition, when optimizing the sponge facilities, most of the current researches only consider the green LID facilities, storage facilities or rainwater pipe network, and lack of overall effect optimization of the combination of grey-green facilities. Moreover, since the urban rainwater model calculation efficiency is too low when considering the waterlogging accumulation factor, it is difficult to realize the coupling and iterative calculation with the intelligent algorithm, and few researches take the waterlogging accumulation reduction as the control index for optimization calculation in the construction of sponge city.

[0003] Therefore, in order to overcome the disadvantages such as great influence of subjective factors and lack of overallity of grey-green combination in traditional sponge optimization, a polynomial function fitting method is used to "link" the urban rainwater model and the intelligent algorithm, the intelligent algorithm calculation is used instead of the subjective selection of schemes, the grey-green combined facilities are optimized, and the optimal sponge configuration scheme of the grey-green combination is accurately and reliably calculated. SUMMARY

[0004] The application aims to provide a grey-green sponge facility optimization design method based on a urban rainwater model and an NSGA-III algorithm, so as to solve the problems in the prior art that it is difficult to ensure the real optimal sponge optimization by setting a limited number of schemes subjectively. The method takes the waterlogging accumulation reduction as the breakthrough point, considers the runoff control and cost target at the same time by means of the two-dimensional coupling technology of the urban rainwater model, and realizes the configuration optimization of the grey-green combined sponge facilities by establishing the "link" of the model calculation and the optimization algorithm through the polynomial fitting method.

[0005] To achieve the above object, the application provides the following technical scheme.

[0006] A grey-green sponge facility optimization design method based on a urban rainwater model and an NSGA-III algorithm, specifically comprising the following steps:

[0007] Step 1, collect and organize the basic data of the to-be-built area pipe network, terrain, underlying surface, rainfall and measured waterlogging accumulation, build the city rain flood model of the to-be-built area, and correct and test the accuracy of the model;

[0008] Step 2, simulate to determine the design size of gray and green sponge facilities required to meet the sponge construction standard of the to-be-built area, that is, the maximum design size;

[0009] Step 3, within the maximum size, design multiple different sizes of gray and green sponge facility configuration schemes, and obtain the benefit relationship of gray and green facilities of different design sizes for waterlogging accumulation reduction and total runoff control through simulation;

[0010] Step 4, based on the benefit relationship of gray and green facilities of different design sizes for waterlogging accumulation reduction and total runoff control in step 3, perform polynomial fitting to establish a design size-benefit (waterlogging accumulation reduction and total runoff control) polynomial function of sponge facilities;

[0011] Step 5, take the polynomial function established in step 4 and the corresponding cost function as the objective function of the multi-objective optimization model, and use NSGA-III algorithm to iteratively solve to obtain the Pareto optimal solution set;

[0012] Step 6, according to the actual sponge construction standard of the to-be-built area and the principle of minimum cost, select the optimal gray-green sponge configuration scheme from the Pareto optimal solution set.

[0013] The accuracy test of the model in step 1 requires at least two measured rainfall and corresponding waterlogging accumulation data, one of which is used for parameter calibration of the city rain flood model, and the other is used for verification to ensure the accuracy of the model simulation.

[0014] Step 2 determines the maximum design size of gray and green sponge facilities in the to-be-built area, which is as follows:

[0015] Step 2.1, take the rainfall specified in the city waterlogging prevention standard as the boundary condition of the model, and simulate the waterlogging accumulation in the to-be-built area;

[0016] Step 2.2, according to the city waterlogging disaster risk classification standard, identify the waterlogging disaster high-risk areas, which are the water accumulation points that need to be transformed;

[0017] Step 2.3, through initial assignment, trial calculation and repeated iteration, simulate the maximum rainwater required for storage of the water accumulation points in step 2.2, which is the maximum volume of gray facilities;

[0018] Step 2.4, take the rainfall specified in the total runoff control standard of the to-be-built area as the boundary condition of the model, and simulate the total runoff control in the to-be-built area;

[0019] Step 2.5, simulate the green facility area required to reach the total runoff control standard of each plot by initial assignment, trial, and repeated iteration, i.e. the maximum green facility area.

[0020] Step 3 describes the design of various sizes of gray and green sponge facility configuration schemes in the maximum scale range, and the relationship between the benefits of gray and green facilities of different design scales in reducing waterlogging and controlling total runoff is obtained by simulation. Step 3 is as follows:

[0021] Step 3.1, set multiple simulation schemes of different sizes based on the maximum design scale of gray and green facilities described in step 2, and in order to ensure the accuracy of the fitting in step 3.3, the simulation schemes are generally not less than 10;

[0022] Step 3.2, based on the gray and green sponge facility simulation schemes set in step 3.1, simulate to obtain the relationship between the benefits of gray and green facilities of different design scales in reducing waterlogging and controlling total runoff;

[0023] Step 4 describes the polynomial curve fitting based on the relationship between the benefits of gray and green facilities of different design scales in reducing waterlogging and controlling total runoff in step 3, and establishes a design scale-benefit (waterlogging reduction and total runoff control) polynomial function of sponge facilities. Step 4 is as follows:

[0024] Assume that there is a set of sample points: {(x1,y1)(x2,y2)…(x m ,y m )}, the number of samples is m, where each sample point represents a different gray and green facility design scale and the corresponding waterlogging reduction or total runoff control benefit value, then the continuous change trend of the sample set can be represented by an n-order polynomial function. The approximate function expression of the design scale-benefit of gray and green sponge facilities in the entire constraint range is:

[0025]

[0026] y(x,ω)——corresponding runoff control or waterlogging reduction value;

[0027] x——gray and green sponge facility configuration scale;

[0028] n——the order of the polynomial;

[0029] ω0,…,ω n ——polynomial coefficients, denoted as ω.

[0030] The polynomial function established in step 4 and the corresponding cost function are taken as objective functions of a multi-objective optimization model, and NSGA-III algorithm is used for iterative solution to obtain a Pareto optimal solution set. Step 5 is specifically as follows:

[0031] Step 5.1, the specific expression of the objective function is as follows:

[0032] Step 5.1.1, cost objective:

[0033]

[0034] In the formula:

[0035] Cost - cost function;

[0036] C B - total cost of gray-tone storage facilities, yuan;

[0037] C L - total cost of green LID facility construction, yuan;

[0038] N s - total number of gray-tone storage facilities to be laid in the area to be built, pieces;

[0039] C Bi - the cost of the i-th gray facility, yuan;

[0040] N k - the number of types of green LID facilities to be built in the area to be built;

[0041] P i - the unit price of the i-th green LID facility, yuan / m 2 .

[0042] A i - the construction area of the i-th green LID facility, m 2 .

[0043] Step 5.1.2, runoff control objective:

[0044]

[0045]

[0046] In the formula:

[0047] Runoff control - total runoff control rate objective function;

[0048] W bg - background runoff control rate of the area to be built;

[0049] N sThe total number of gray-scale storage facilities to be arranged in the area to be built, pieces

[0050] N k The number of types of green LID facilities to be built in the area to be built, pieces

[0051] V Xi The storage capacity of gray and green facilities X i , m 3 ;

[0052] P The total rainfall in the area, m 3 ;

[0053] y(x i , ω) The fitting function of each gray and green facility for j-point overflow reduction

[0054] n The order of the polynomial

[0055] ω0, …, ω n The coefficients of the polynomial

[0056] Step 5.1.3, waterlogging water reduction target:

[0057]

[0058]

[0059] In the formula:

[0060] Flooding reduce The waterlogging reduction function of S waterlogging points

[0061] O j The amount of waterlogging in S waterlogging points, m 3 ;

[0062] N s The total number of gray-scale storage facilities to be arranged in the area to be built

[0063] N k The number of types of green LID facilities to be built in the area to be built

[0064] x i The area of green LID facilities or the volume of gray-scale storage facilities

[0065] y(x i , ω) The fitting function of each gray and green facility for j-point waterlogging reduction, m 3 ;

[0066] n The order of the polynomial

[0067] ω0, …, ω n The coefficients of the polynomial

[0068] Step 5.2, constraint condition expression:

[0069] Step 5.2.1, grey facility constraint condition expression:

[0070] 0m 3 <V i <V i(max) m 3

[0071] In the formula:

[0072] V i — Volume of the i-th storage facility, m 3 ;

[0073] V i(max) — Maximum volume of the i-th storage facility, m 3 .

[0074] Step 5.2.2, green LID facility constraint expression:

[0075] 0m 2 <A i <A i(max) m 2

[0076] In the formula:

[0077] A i — Construction area of the i-th LID facility, m 2 ;

[0078] A i(max) — Maximum construction area of the i-th LID facility, m 2 .

[0079] Step 5.3, use NSGA-III algorithm to iteratively calculate the multi-objective optimization model, set population size N, maximum genetic generation G, crossover probability Pc, and compilation probability Pm, and calculate N sets of non-inferior solutions in the optimal Pareto frontier solution set.

[0080] Step 6: According to the actual sponge construction standards of the to-be-built area and the principle of minimum cost, the optimal grey-green sponge configuration scheme is selected from the Pareto optimal solution set. Step 6 is as follows:

[0081] Step 6.1: Based on the runoff control standard, select the standard solution from the N sets of non-inferior solutions to obtain x sets of solutions;

[0082] Step 6.2: Based on the waterlogging prevention standard, continue to select from the x sets of solutions to obtain y sets of solutions;

[0083] Step 6.3. Select the solution with the lowest cost from the y groups of solutions selected in step 6.2 on the principle of cost minimization, and record the solution as z, then z is the optimal configuration scheme of the gray-green sponge facility meeting the sponge construction standard of the to-be-built area.

[0084] The beneficial effects of the present application are: the present application uses the polynomial fitting method as the link between the urban rainwater model and the optimization algorithm, establishes a gray-green sponge facility design scale-benefit function based on the polynomial curve fitting method, uses the function as the objective function of waterlogging accumulation and runoff control in multi-objective optimization, solves the optimal scheme solving problem that the optimization algorithm and the urban rainwater model cannot be directly coupled due to the low calculation efficiency of the urban rainwater model; makes the sponge configuration optimization considering waterlogging accumulation possible; overcomes the disadvantages of large subjective factors and lack of overall gray-green combination in traditional sponge optimization; uses intelligent algorithm calculation instead of subjective scheme screening to optimize the gray-green combination facility, and accurately and reliably calculates the optimal gray-green combination sponge configuration scheme. BRIEF DESCRIPTION OF DRAWINGS

[0085] Fig. 1 is the implementation flowchart of the gray-green sponge facility optimization design method based on the urban rainwater model and the NSGA-III algorithm of the present application;

[0086] Fig. 2 is a comparison chart of the gray-green facility configuration scale and cost before and after optimization.

[0087] DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application, please refer to Figs. 1-2 The embodiments of the present application are as follows:

[0089] A gray-green sponge facility optimization design method based on an urban rainwater model and an NSGA-III algorithm, specifically comprising the following steps:

[0090] Step 1. Collect and arrange the basic data of the pipe network, terrain, underlying surface, rainfall and measured waterlogging accumulation of the to-be-built area, construct the urban rainwater model of the to-be-built area, and correct and test the accuracy of the model;

[0091] Step 2. Simulate to determine the design scale of the gray-green sponge facility meeting the sponge construction standard of the to-be-built area, that is, the maximum design scale;

[0092] Step 2 determines the maximum design scale of the gray-green sponge facility of the to-be-built area, and step 2 specifically comprises the following steps:

[0093] Step 2.1. Taking the rainfall specified in the urban waterlogging prevention standard of the to-be-built area as the boundary condition of the model, simulating the waterlogging accumulation in the to-be-built area;

[0094] Step 2.2, according to the urban waterlogging disaster risk classification standard, the high-risk area of waterlogging disaster is identified, which is the waterlogging point that needs to be transformed;

[0095] Step 2.3, the maximum rainwater volume required for storage of the waterlogging point in step 2.2 is simulated through initial assignment, trial calculation and repeated iteration, which is the maximum volume of gray facilities;

[0096] Step 2.4, the rainfall amount specified in the total runoff control standard of the to-be-built area is used as the boundary condition of the model to simulate the total runoff control of the to-be-built area;

[0097] Step 2.5, the area of green facilities required to achieve the total runoff control standard of each plot is simulated through initial assignment, trial calculation and repeated iteration, which is the maximum area of green facilities.

[0098] Step 3, in the maximum scale range, design multiple gray and green sponge facility configuration schemes of different scales, and obtain the benefit relationship of gray and green facilities of different design scales for waterlogging water reduction and total runoff control through simulation;

[0099] Step 3, in the maximum scale range, design multiple gray and green sponge facility configuration schemes of different scales, and obtain the benefit relationship of gray and green facilities of different design scales for waterlogging water reduction and total runoff control through simulation. Step 3 is as follows:

[0100] Step 3.1, set multiple simulation schemes of different scales based on the maximum design scale of gray and green facilities in step 2, and in order to ensure the accuracy of the fitting in step 3.3, the simulation schemes are generally not less than 10;

[0101] Step 3.2, based on the gray and green sponge facility simulation schemes set in step 3.1, simulate to obtain the benefit relationship of gray and green facilities of different design scales for waterlogging water reduction and total runoff control;

[0102] Step 4, based on the benefit relationship of gray and green facilities of different design scales for waterlogging water reduction and total runoff control in step 3, perform polynomial fitting to establish a polynomial function of the design scale-benefit (waterlogging water reduction and total runoff control) of sponge facilities;

[0103] Step 4, based on the benefit relationship of gray and green facilities of different design scales for waterlogging water reduction and total runoff control in step 3, perform polynomial curve fitting to establish a polynomial function of the design scale-benefit (waterlogging water reduction and total runoff control) of sponge facilities. Step 4 is as follows:

[0104] Suppose there is a set of sample points: {(x1,y1)(x2,y2)…(x m ,y mm, where each sample point represents a different grey, green facility design scale and the corresponding value of the waterlogging accumulation reduction or runoff total control benefit, the continuous trend of the sample set can be represented by an n-order polynomial function. The approximate function expression of the grey, green sponge facility design scale-benefit in the entire constraint range is:

[0105]

[0106] y(x,ω)——corresponding runoff total control or waterlogging accumulation reduction value;

[0107] x——grey, green sponge facility configuration scale;

[0108] n——the order of the polynomial;

[0109] ω0,…,ω n ——polynomial coefficients, denoted as ω.

[0110] Step 5, the polynomial function established in step 4 and the corresponding cost function are used as the objective function of the multi-objective optimization model, and the NSGA-III algorithm is used for iterative solution to obtain the Pareto optimal solution set;

[0111] Step 5 of the polynomial function established in step 4 and the corresponding cost function are used as the objective function of the multi-objective optimization model, and the NSGA-III algorithm is used for iterative solution to obtain the Pareto optimal solution set. Step 5 is as follows:

[0112] Step 5.1, the specific expression of the objective function is as follows:

[0113] Step 5.1.1, cost objective:

[0114]

[0115] In the formula:

[0116] Cost——cost function;

[0117] C B ——total cost of grey tone storage facilities, yuan;

[0118] C L ——total cost of green LID facility construction, yuan;

[0119] N s ——total number of grey tone storage facilities to be built in the area, units;

[0120] C Bi ——the cost of the i-th grey facility, yuan;

[0121] N k—The types and quantities of green LID facilities to be constructed in the area to be developed;

[0122] P i —The unit price of the i-th type of green LID facility, in yuan / m² 2 ;

[0123] A i —The construction area of ​​the i-th type of green LID facility, in m 2 .

[0124] Step 5.1.2, Runoff Control Objectives:

[0125]

[0126]

[0127] In the formula:

[0128] Runoffcontrol—the objective function for runoff control rate;

[0129] W bg —Background runoff control rate in the area to be developed;

[0130] N s —Total number of gray water storage facilities to be deployed in the area to be built;

[0131] N k —The types and quantities of green LID facilities to be constructed in the area to be developed;

[0132] V Xi —Gray and Green Facilities X i The water storage capacity, m 3 ;

[0133] P — Total rainfall in the region, m 3 ;

[0134] y(x i ,ω)——The fitting function for the overflow reduction amount at point j for each gray and green facility;

[0135] n — the order of the polynomial;

[0136] ω0,…,ω n — Coefficients of the polynomial.

[0137] Step 5.1.3, Target for Reducing Urban Flooding:

[0138]

[0139]

[0140] In the formula:

[0141] Flooding reduce — A function to reduce water accumulation at S flood-prone locations;

[0142] O j —Water volume at S waterlogging points, in meters 3 ;

[0143] N s —Total number of gray water storage facilities to be deployed in the area to be developed;

[0144] N k —The types and quantities of green LID facilities to be constructed in the area to be developed;

[0145] x i —The area of ​​green LID facilities or the volume of gray storage facilities;

[0146] y(xi,ω)——The fitting function for water reduction at point j by each gray and green facility, m 3 ;

[0147] n — the order of the polynomial;

[0148] ω0,…,ω n — Coefficients of the polynomial.

[0149] Step 5.2, the constraint conditions have the following expressions:

[0150] Step 5.2.1, Expression for gray facility constraint conditions:

[0151] 0m 3 <V i <V i(max) m 3

[0152] In the formula:

[0153] V i —The volume of the i-th storage facility, m 3 ;

[0154] V i(max) —The maximum volume of the i-th storage facility, m 3 .

[0155] Step 5.2.2, Green LID Facility Constraint Expression:

[0156] 0m 2 <A i <A i(max) m 2

[0157] In the formula:

[0158] A iThe i-th LID facility construction area, m 2 ;

[0159] A i(max) The i-th LID facility construction maximum area, m 2 .

[0160] Step 5.3, the NSGA-III algorithm is used for iterative calculation of the multi-objective optimization model, the population size N, the maximum genetic generation G, the crossover probability Pc, the compiling probability Pm are set, and N groups of non-inferior solutions in the optimal Pareto front solution set are calculated.

[0161] Step 6, according to the actual sponge construction standard of the to-be-built area and the principle of minimum cost, the optimal gray-green sponge configuration scheme is screened from the Pareto optimal solution set;

[0162] Step 6 of the actual sponge construction standard of the to-be-built area and the principle of minimum cost, the optimal gray-green sponge configuration scheme is screened from the Pareto optimal solution set. Step 6 is as follows:

[0163] Step 6.1, based on the runoff control standard, select the standard solution from the N groups of non-inferior solutions to obtain x groups of solutions;

[0164] Step 6.2, based on the waterlogging prevention standard, continue to screen from the x groups of solutions to obtain y groups of solutions;

[0165] Step 6.3, according to the principle of minimum cost, select the group of solutions with the lowest cost from the y groups of solutions selected in step 6.2, and mark it as z, then z is the optimal configuration scheme of the gray-green sponge facility meeting the sponge construction specification of the to-be-built area.

[0166] The accuracy test of the model in step 1 needs at least two measured rainfall and corresponding waterlogging data, one of which is used for parameter calibration of the urban rainwater model, and the other is used for verification to ensure the accuracy of the model simulation.

[0167] The technical scheme adopted by the application is: firstly, the construction of the urban rainwater model is completed according to the basic data of the project area; the gray-green sponge facility design scale-benefit function is constructed based on the polynomial curve fitting method, which is used as the link between the urban rainwater model and the intelligent optimization algorithm calculation, that is, the objective function of waterlogging and runoff control in multi-objective optimization; the NSGA-III algorithm is used for iterative calculation to find the optimal design scheme of the gray-green sponge with the minimum waterlogging, the maximum runoff control and the most economical cost, and realize the automatic optimization process of the gray-green combined sponge reconstruction.

[0168] With the invention, the grey-green sponge facility configuration optimization is carried out by taking Xiaozhai area in Xi'an as an example. The total area of the area is 2015.09 ha. According to the pipe network, terrain and other data, the urban rain flood model of the to-be-built area is constructed, which contains 269 pipelines, 2 discharge ports, 268 nodes and 268 sub-catchment areas. The two-dimensional terrain is divided into 1351770 grids, and the grid accuracy is 5 m. The grey and green sponge facilities are selected as storage tanks, permeable pavement, sunken green land, rainwater garden and green roof. According to the calculation of the sponge construction standard of the to-be-built area, the maximum design total scale meeting the standard is 2.21 million m 3 , 52.75 ha, 26.16 ha, 17.74 ha and 23.80 ha. According to the optimization calculation by the above method, under the premise of meeting the sponge construction standard, the configuration scale and construction cost of the optimized grey-green sponge facilities are greatly reduced. The scale of the storage tank, permeable pavement, sunken green land, rainwater garden and green roof is 1.86 million m 3 , 37.65 ha, 20.61 ha, 8.67 ha and 15.56 ha. Compared with the preliminary design scheme without optimization, the cost of the optimal configuration scheme is reduced by 127 million yuan, as shown in the following table. Fig. 2

[0169] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any skilled person in the art can make equivalent replacement or change according to the technical scheme and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.​

Claims

1. A grey-green sponge facility optimization design method based on an urban rain flood model and an NSGA-III algorithm, characterized in that, Specifically comprising the following steps: Step 1, collect and organize the basic data of the pipe network, terrain, underlying surface, rainfall and measured waterlogging in the area to be built, construct the urban rainwater model of the area to be built, and correct and test the accuracy of the model; Step 2, simulate to determine the design size of gray and green sponge facilities required to meet the sponge construction standard of the area to be built, that is, the maximum design size; Step 3, within the maximum size, design multiple different sizes of gray and green sponge facility configuration schemes, and obtain the benefit relationship of gray and green facilities of different design sizes for waterlogging reduction and total runoff control through simulation; Step 4, based on the benefit relationship of gray and green facilities of different design sizes for waterlogging reduction and total runoff control in step 3, perform polynomial fitting to establish a polynomial function of design size-benefit (waterlogging reduction and total runoff control) of sponge facilities, as follows: Suppose there is a set of sample points: {( x 1, y 1) ( x 2, y 2)… ( x m , y m )} with m sample points, where each sample point represents a different gray-green facility design scale and the corresponding benefit value of waterlogging ponding reduction or total runoff control, the continuous change trend of the sample set can be represented by an n-order polynomial function, and the approximate function expression of the gray-green sponge facility design scale-benefit in the entire constraint range is: ； y ( x,ω ) - a corresponding total runoff control or waterlogging reduction value; x - grey, green sponge facility configuration scale; n - the order of the polynomial; ω 0 …, ω n - coefficients of the polynomial, denoted ω; Step 5, take the polynomial function established in step 4 and the corresponding cost function as the objective function of the multi-objective optimization model, and use NSGA-Ⅲ algorithm to iteratively solve to obtain the Pareto optimal solution set, as follows: Step 5.1, the specific expression of the objective function is as follows: Step 5.1.1, cost objective: ； Cost Step 5.1.2, runoff control objective: - a cost function; C B - Total cost of grey-tone storage facilities, yuan; C L Total cost of green LID facility construction, yuan; N s Total number of gray-scale storage facilities to be built in the area: C Bi - 1st i grey facility cost, yuan; N k - the number of types of green LID facilities to be built in the area under construction; P i ——No. i The unit price of green LID facilities is RMB / m². 2 ; A i ——No. i The construction area of ​​green LID facilities, m 2 ; Runoffcontrol ω ω - a total amount of runoff control rate objective function; W bg - the background runoff control rate of the area to be built; N s - Total number of gray-scale storage facilities to be built in the area, units N k - Number of types of green LID facilities to be built in the area under construction, pieces; V Xi - grey, green infrastructure X i storage volume, m 3 ; P - the total amount of rainfall in the area, m 3 ; y x i ω j point overflow reduction fitting function;​​​ n - the degree of the polynomial; Step 5.1.3, waterlogging reduction objective: 0 …, Flooding reduce n - coefficients of a polynomial; ω ； ； ω ω - a waterlogging reduction function of S inner waterlogging points; O j - the amount of water in S inner waterlogged points, m 3 ; N s Total number of gray-scale storage facilities to be built in the area N k - the number of types of green LID facilities to be built in the area under construction; x i - Area of green LID facilities or volume of grey-tone storage facilities; y x i Step 5.2, the expression of the constraint condition is as follows: j 3 ;​​​​ n - the order of the polynomial; Step 5.2.1, the expression of the constraint condition of gray facilities is as follows: 0 …, Step 5.2.2, the constraint expression of green LID facilities is as follows: n coefficients of the polynomial; Step 5.3, use NSGA-Ⅲ algorithm to iteratively calculate the multi-objective optimization model, set the population size N, the maximum genetic generation G, the crossover probability Pc, and the mutation probability Pm, and calculate N groups of non-inferior solutions in the optimal Pareto front solution set; Step 6, according to the actual sponge construction standard of the area to be built and the principle of minimum cost, select the optimal gray-green sponge configuration scheme from the Pareto optimal solution set. ； The accuracy test of the model in step 1 requires at least two measured rainfall and corresponding waterlogging data, one of which is used for parameter calibration of the urban rainwater model, and the other is used for verification to ensure the accuracy of the model simulation. V i - the volume of the storage facility, m i 3 ;​ V i(max) - the maximum volume of the storage and retention facility, m i 3 ;​ Step 2, the maximum design size of gray and green sponge facilities in the area to be built is determined as follows: ； Step 2.1, take the rainfall specified in the urban waterlogging prevention standard of the area to be built as the boundary condition of the model to simulate the waterlogging in the area to be built; A i - the first i LID facility construction area, m 2 ; A i(max) - the first i LID is adapted to be applied to a maximum area, m 2 ; Step 2.2, according to the urban waterlogging disaster risk classification standard, identify the waterlogging disaster high-risk areas, which are the waterlogging points that need to be transformed; Step 2.3, simulate the maximum rainwater storage required for the waterlogging points in step 2.2 through initial assignment, trial calculation and repeated iteration, which is the maximum volume of gray facilities; 2. The grey-green sponge facility optimization design method based on the urban rain flood model and the NSGA-III algorithm according to claim 1, characterized in that, Step 2.4, take the rainfall specified in the total runoff control standard of the area to be built as the boundary condition of the model to simulate the total runoff control in the area to be built; 3. The grey-green sponge facility optimization design method based on the urban rain flood model and the NSGA-III algorithm according to claim 1, characterized in that, ​ ​ ​ ​ ​ Step 2.

5. Simulate the green facility area required to reach the total runoff control standard of each plot by initial assignment, trial, and repeated iteration, i.e. the maximum green facility area.

4. The grey-green sponge facility optimization design method based on the urban rain flood model and the NSGA-III algorithm according to claim 1, characterized in that, Step 3.

1. Set multiple simulation schemes of different sizes as constraints based on the maximum design scale of the gray and green facilities in step 2, and in order to ensure the accuracy of the fitting in step 3.3, the simulation schemes are generally not less than 10; Step 3.

2. Based on the gray-green sponge facility simulation schemes set in step 3.1, simulate to obtain the benefit relationship of different design scales of gray and green facilities for waterlogging water reduction and total runoff control. Step 6. Select the optimal gray-green sponge configuration scheme from the Pareto optimal solution set according to the actual sponge construction standard of the area to be built and the principle of minimum cost; Step 6 is as follows:

5. The grey-green sponge facility optimization design method based on urban rain flood model and NSGA-III algorithm according to claim 1, characterized in that, Step 6. Select the optimal gray-green sponge configuration scheme from the Pareto optimal solution set according to the actual sponge construction standard of the area to be built and the principle of minimum cost; Step 6 is as follows: Step 6.

1. Select the satisfactory solution from N sets of non-inferior solutions based on the runoff control standard, and obtain x group solution; Step 6.2, based on the standard of waterlogging control, in x Group solutions continue to filter, get y Group solutions; Step 6.

3. Select the solution with the lowest cost from the solutions selected in step 6.2 y Step 6.

3. Select the solution with the lowest cost from the solutions selected in step 6.2 z Step 6.

3. Select the solution with the lowest cost from the solutions selected in step 6.2 z Step 6.

3. Select the solution with the lowest cost from the solutions selected in step 6.2

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