A method for establishing an optimization model for sponge city design schemes

Abstract

The present invention discloses a method for establishing a sponge city design scheme optimization model. Existing methods for comparing and selecting sponge city design schemes are unreasonable. The present invention determines the comparison schemes, then simulates and determines the selected evaluation indicators; uses the analytic hierarchy process and the entropy weight method to calculate the weights of each evaluation indicator, and then uses the minimum information entropy to combine the weights of the two methods to obtain the combined weights; determines a regression model for the optimal sponge city infrastructure layout; establishes a response surface for the evaluation indicators to obtain a response surface function expression; uses the regression model as the overall objective function and the response surface function as the sub-objective function to obtain an optimization model for the sponge city design scheme; uses the optimization model as a regression model and selects a particle swarm optimization algorithm to find the optimal scheme. The present invention can quickly establish an optimization model for sponge city design schemes under the same rainfall conditions, and use this model to quickly find the optimal scheme.

Description

Technical Field

[0001] The present invention belongs to the technical field of rainwater control and utilization, and particularly relates to a method for establishing an optimization model for a sponge city design scheme. Background Art

[0002] With the continuous advancement of ecological civilization construction, engineering projects have higher requirements for rainwater control and utilization. As an important means of rainwater control and utilization, sponge city design has played a significant role in alleviating urban waterlogging and mitigating the urban heat island effect.

[0003] Today, during the sponge city design phase of engineering projects, designers often perform complex and tedious calculations in a sequential order: underlying surface analysis - calculation of the comprehensive runoff coefficient - catchment zoning - underlying surface analysis - facility layout - and finally indicator calculation. Any changes to the master plan, landscape design, or underlying surface properties require recalculation of all calculation indicators, a significant workload. Furthermore, reasonable plans can only be achieved by taking into account the annual runoff control rate. Furthermore, when comparing plans, only simple quantitative comparisons are conducted on project investment, while other calculation indicators are still compared qualitatively, which is significantly influenced by the evaluator's subjective perception. Summary of the Invention

[0004] In order to make up for the shortcomings of the existing technology, the present invention provides a method for establishing a sponge city design scheme optimization model, which can quickly establish a sponge city design scheme optimization model. Using this model, when comparing schemes in the same city, the same region or the same project, the optimal sponge city design scheme for the engineering project can be quickly found.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for establishing a sponge city design optimization model, characterized by comprising the following steps:

[0007] Step 1: Determination of rainwater control and utilization design parameters;

[0008] Step 2: Select the type of sponge city infrastructure for rainwater control, freely allocate and couple the infrastructure, and then select T options for comparison;

[0009] Step 3: Select evaluation indicators, among which the runoff reduction rate α, the total suspended solids reduction rate β at the outlet, and the flood peak reduction rate ω are selected as positive evaluation indicators, and the full life cycle of each facility γ is used as a negative evaluation indicator;

[0010] Step 4: Simulate the positive evaluation indicators of T types of sponge city infrastructure layout plans;

[0011] Step 5: Determine the negative evaluation index of different sponge city coupling schemes, that is, the full life cycle γ, and use the present value of cost to calculate the full life cycle γ of each coupling scheme. The formula is as follows:

[0012] PV = + * - *

[0013] =

[0014] Among them, PV ——Present value of the cost of gray-green measures of type i in year n (yuan);

[0015] ——Initial investment of type i grey-green measures in construction (yuan);

[0016] ——Operation and maintenance cost in year t (yuan);

[0017] ——the present value coefficient of the discount rate r in year t;

[0018] ——the present value coefficient of the discount rate r at the design service life in the nth year;

[0019] ——Residual value of the gray-green measure when the design life cycle is n years (yuan);

[0020] Step 6: Calculate the weight of each evaluation index using the hierarchical analysis method and the entropy weight method respectively, and then combine the weights of the two methods using the minimum information entropy to obtain the combined weight , combined weight The calculation formula is as follows:

[0021] =

[0022] Where: i=1~m, m is the number of evaluation indicators;

[0023] —Qualitative weights of evaluation indicators;

[0024] — quantitative weights of evaluation indicators;

[0025] Step 7: Determine the regression model of the optimal sponge city infrastructure layout, where the independent variables are i evaluation indicators and the dependent variable is the quality of the plan;

[0026] Step 8: Use the response surface method to establish the response surface of the i evaluation indicators in step 7, and obtain the response surface function expression of the i evaluation indicators;

[0027] Step 9: Use the regression model in step 7 as the overall objective function and the response surface function of the i-type evaluation index in step 8 as the sub-objective function to finally obtain the optimization model of the sponge city design scheme. The optimization model function expression is:

[0028] Max(F)=f (T solutions)

[0029] Step 10: Use the optimization model in step 9 as a regression model and use the particle swarm optimization algorithm to find the optimal solution among T sponge city design solutions when i evaluation indicators are used.

[0030] Furthermore, the principles and methods for determining the various indicators in the full life cycle γ in step 5 are as follows:

[0031] a. Sponge infrastructure refers to the replacement of existing green spaces, roofs, and roads with architectural landscape expertise, excluding the construction costs of occupied land;

[0032] b. Operation and maintenance costs include maintenance costs and management costs, which can be determined by referring to other similar local projects;

[0033] c. The residual value (SV) of a project refers to the recoverable value of the sponge infrastructure when its service life approaches its design life. It is calculated as follows:

[0034] SV=1(1- )COM;

[0035] in, —The time interval between the last maintenance work and the designed service life (years);

[0036] ——Designed service life of grey-green measures (years);

[0037] COM——Annual operation and maintenance cost (yuan / year).

[0038] Furthermore, the entropy weight method in step six is defined as:

[0039] = ( )

[0040] in, ——entropy of the i-th indicator;

[0041] =-k

[0042] Where, j is the number of gray-green coupling schemes, j=1, 2, 3, , n;

[0043] ——The proportion of the jth evaluation object under the i-th indicator to the indicator; when =0, let =0.

[0044] Furthermore, the response surface method in step eight is to establish a simulation model between performance indicators and influencing factors through appropriate experimental or simulation data, suitable mathematical models and fitting methods.

[0045] Furthermore, the particle swarm optimization method in step 10 is a method for finding the optimal solution through cooperation and information sharing among individuals in a group.

[0046] Beneficial effects of the present invention:

[0047] The present invention can quickly establish an optimization model for sponge city design schemes under the same rainfall conditions. This model can be used to quickly find the optimal scheme for sponge city design of engineering projects. In subsequent sponge city designs, other similar projects with the same rainfall conditions and similar construction scales can directly use this model to select the optimal scheme, eliminating the tedious comparison process in the previous scheme design and avoiding the subjective influence in the previous comparison scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart of the present invention;

[0049] Figure 2 This is a graph showing the fitness change of the optimal evolved individuals of the particle swarm algorithm of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be described in detail below with reference to specific embodiments.

[0051] like Figure 1 As shown, the present invention specifically includes the following steps:

[0052] Step 1: Determination of rainwater control and utilization design parameters;

[0053] SWMM model parameters were selected based on the design of a stormwater pipe at a Xi'an subway depot, including pipe diameter, length, slope, catchment area, and area. Other parameters, such as infiltration rate, were determined by referring to the SWMM user manual and relevant literature. Specific parameters are shown in the table below.

[0054] Table 1 SWMM simulation parameter settings for a subway depot in Xi'an

[0055]

[0056] Step 2: Design of a Rainwater Control and Utilization Plan: Based on the project site's sponge city design plan, guidelines and standards, the project owner's requirements, and upstream professional design data, this example selected five types of sponge city infrastructure: grass-lined ditches, sunken green spaces, permeable pavement and green roofs, and PP modular reuse pools, in accordance with the "Xi'an Sponge City Special Plan," "Xi'an Sponge City Construction Implementation Plan," "Technical Specifications for Building and Community Rainwater Control and Utilization Engineering," and the requirements of the rail company's owner. These five infrastructure types were combined in varying proportions, resulting in 240 combinations of sponge facilities in this example.

[0057] Step 3: Select evaluation indicators, among which the runoff reduction rate α, the outlet total suspended solids reduction rate β and the flood peak reduction rate ω are selected as positive evaluation indicators, and the full life cycle of each facility γ is used as a negative evaluation indicator;

[0058] Step 4: Using the SWMM model established in Step 1, simulate the positive evaluation indicators of 240 sponge city infrastructure layout plans, namely, runoff reduction rate α, TSS reduction rate β, and flood peak reduction rate ω;

[0059] Step 5: Determine the negative evaluation index (i.e., the entire life cycle γ) of the 240 solutions. This invention uses the present value of cost to calculate the entire life cycle of the gray-green setting. The formula is as follows:

[0060] PV = + * - *

[0061] =

[0062] Among them, PV ——Present value of the cost of gray-green measures of type i in year n (yuan);

[0063] ——Initial investment of type i grey-green measures in construction (yuan);

[0064] ——Operation and maintenance cost in year t (yuan);

[0065] ——the present value coefficient of the discount rate r in year t;

[0066] ——the present value coefficient of the discount rate r at the design service life in the nth year;

[0067] ——Residual value of the gray-green measure when the design life cycle is n years (yuan);

[0068] The principles and methods for determining each indicator are as follows:

[0069] Gray-green facilities are green spaces, roofs, and roads that replace existing architectural landscapes. This paper does not include the construction costs of occupied land. Operation and maintenance costs include maintenance costs and management costs, which can be determined by referring to other similar local projects. The residual value (SV) of the project refers to the recoverable value of the gray-green facilities when their service life approaches the design life. It is calculated as follows:

[0070] SV=1(1- )COM;

[0071] in, —The time interval between the last maintenance work and the designed service life (years);

[0072] ——Designed service life of grey-green measures (years);

[0073] COM——Annual operation and maintenance cost (yuan / year).

[0074] The calculation results of this embodiment are shown in Table 2:

[0075] Table 2 Rainwater system life cycle cost table

[0076]

[0077] Step 6: Calculate the weights of each evaluation indicator using the analytic hierarchy process and entropy weight method. The entropy weight method is defined as:

[0078] = ( )

[0079] in, ——entropy of the i-th indicator;

[0080] =-k

[0081] Where, j is the number of gray-green coupling schemes, j=1, 2, 3, , n;

[0082] ——The proportion of the jth evaluation object under the i-th indicator to the indicator; when =0, let =0.

[0083] Then use the minimum information entropy to combine the weights of the two methods to get the combined weight , combined weight The calculation formula is as follows:

[0084] =

[0085] Where: i=1~m, m is the number of evaluation indicators;

[0086] —Qualitative weights of evaluation indicators;

[0087] — quantitative weights of evaluation indicators;

[0088] The calculation results of this embodiment are shown in Table 3:

[0089] Table 3 Subjective indicators, objective weights and comprehensive weights of various indicators

[0090]

[0091] Step 7: Determine the regression model of the optimal sponge city infrastructure layout, where the independent variables are the four evaluation indicators and the dependent variable is the quality of the plan; the regression model of the optimal plan in this embodiment = runoff reduction rate α * corresponding comprehensive weight + TSS reduction rate β*corresponding comprehensive weight +Flood peak reduction rateω*corresponding comprehensive weight +Full life cycle γ*corresponding comprehensive weight , specifically:

[0092]

[0093] Step 8: Use the response surface methodology to establish a response surface for the four evaluation indicators in step 7. The response surface methodology uses an appropriate amount of experimental or simulation data, a suitable mathematical model, and a fitting method to establish a simulation model between performance indicators and influencing factors. In the present invention, Design-Expert software is used to establish the response surface model. The response surface function expressions of the four evaluation indicators are as follows:

[0094] α=0.1345-0.0290*A+0.0960*B+0.1387*C+0.3804*D+0.6756*E+0.0162*A*B-0.0748*A*C-0.0509*A*D -0.0038*A*E+0.0703*B*C+0.0716*B*D-0.1166*B*E-0.0136*C*D-0.0875*C*E-0.2630*D*E+0.1119*A 2 +0.0101*E 2

[0095] =0.0091-0.0672*A+0.0152*B+0.4009*C+0.4895*D+0.5175*E+0.4192*A*B-0.0857*A*C+0.1061*A*D -0.0909*A*E-0.1443*B*C+0.0707*B*D-0.1507*B*E-0.4733*C*D-0.1549*C*E-0.1036*D*E-0.0403*A 2 -0.0169*E 2

[0096] ω=-0.0633-0.0090*A+0.0104*B+0.1304*C+0.2917*D+1.62*E-0.1037*A*B-0.1318*A*C-0.0563*A*D +0.0048*A*E+0.0630*B*C+0.1488*B*D-0.0179*B*E+0.1222*C*D-0.1266*C*E-0.2995*D*E+0.1663*A 2 -0.5939*E 2

[0097] γ=-0.2202+0.1325*A+0.1680*B+0.8383*C+0.3191*D+0.3457*E

[0098] Where A, B, C, and D are the proportions (%) of grass-planted ditches, sunken green spaces, green roofs, and permeable pavements, respectively; and E is the volume ratio of the storage tank (%).

[0099] Step 9: Use the regression model in step 7 as the overall objective function and the response surface functions of the four evaluation indicators in step 8 as sub-objective functions to finally obtain the optimization model of the sponge city design scheme. The optimization model function expression is:

[0100] Max(F)=f (T solutions)

[0101] Step 10: Using the optimization model of step 9 as a regression model, the particle swarm optimization algorithm PSO is used to find the optimal solution of 240 sponge city design schemes when using four evaluation indicators; the particle swarm optimization method is a method of finding the optimal solution through cooperation and information sharing between individuals in a group. In this invention, Matlab programming iteration is used to solve the optimal solution of gray-green coupling; the initial parameters of the ion swarm method used in this example are shown in Table 4, and the optimization process is as follows Figure 2 Finally, the optimal solution was achieved when the proportions of the five types of sponge city infrastructure were 5% (grass ditch), 5% (sunken green space), 5% (green roof), 15% (permeable pavement) and the storage tank volume ratio was 100%.

[0102] Table 4 Initial parameters of particle swarm optimization algorithm

[0103]

[0104] In the description of the present invention, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they may refer to fixed or detachable connections, or integration; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific contexts.

[0105] The content of the present invention is not limited to the embodiments listed. Any equivalent transformation of the technical solution of the present invention made by ordinary technicians in this field after reading the description of the present invention is covered by the claims of the present invention.

Claims

1. A method for establishing a sponge city design optimization model, characterized by: The specific steps include: Step 1: Determination of rainwater control and utilization design parameters; Step 2: Select the type of sponge city infrastructure for rainwater control, freely allocate and couple the infrastructure, and then select T options for comparison; Step 3: Select evaluation indicators, among which the runoff reduction rate α, the total suspended solids reduction rate β at the outlet, and the flood peak reduction rate ω are selected as positive evaluation indicators, and the full life cycle of each facility γ is used as a negative evaluation indicator; Step 4: Simulate the positive evaluation indicators of T types of sponge city infrastructure layout plans; Step 5: Determine the negative evaluation index of different sponge city coupling schemes, that is, the full life cycle γ, and use the present value of cost to calculate the full life cycle γ of each coupling scheme. The formula is as follows: PV = + * - * = Among them, PV ——Present value of the cost of gray-green measures of type i in year n (yuan); ——Initial investment of type i grey-green measures in construction (yuan); ——Operation and maintenance cost in year t (yuan); ——the present value coefficient of the discount rate r in year t; ——the present value coefficient of the discount rate r at the design service life in the nth year; ——Residual value of the gray-green measure when the design life cycle is n years (yuan); Step 6: Calculate the weight of each evaluation index using the hierarchical analysis method and the entropy weight method respectively, and then combine the weights of the two methods using the minimum information entropy to obtain the combined weight , combined weight The calculation formula is as follows: = Where: i=1~m, m is the number of evaluation indicators; —Qualitative weights of evaluation indicators; — quantitative weights of evaluation indicators; Step 7: Determine the regression model of the optimal sponge city infrastructure layout, where the independent variables are i evaluation indicators and the dependent variable is the quality of the plan; Step 8: Use the response surface method to establish the response surface of the i evaluation indicators in step 7, and obtain the response surface function expression of the i evaluation indicators; Step 9: Use the regression model in step 7 as the overall objective function and the response surface function of the i-type evaluation index in step 8 as the sub-objective function to finally obtain the optimization model of the sponge city design scheme. The optimization model function expression is: Max(F)=f (T solutions) Step 10: Use the optimization model in step 9 as a regression model and use the particle swarm optimization algorithm to find the optimal solution among T sponge city design solutions when i evaluation indicators are used.

2. The method for establishing a sponge city design optimization model according to claim 1, characterized in that: The principles and methods for determining the indicators in the full life cycle γ in step 5 are as follows: a. Sponge infrastructure refers to the replacement of existing green spaces, roofs, and roads with architectural landscape expertise, excluding the construction costs of occupied land; b. Operation and maintenance costs include maintenance costs and management costs, which shall be determined by reference to other similar local projects; c. The residual value (SV) of a project refers to the recoverable value of the sponge infrastructure when its service life approaches its design life. It is calculated as follows: SV=1(1- )COM; in, —The time interval between the last maintenance work and the designed service life (years); ——Designed service life of grey-green measures (years); COM——Annual operation and maintenance cost (yuan / year).

3. The method for establishing a sponge city design optimization model according to claim 2, characterized in that: The entropy weight method in step six is defined as: = （ ） in, ——entropy of the i-th indicator; =-k Where, j is the number of gray-green coupling schemes, j=1, 2, 3, , n; ——The proportion of the jth evaluation object under the i-th indicator to the indicator; when =0, let =0.

4. The method for establishing a sponge city design optimization model according to claim 3, characterized in that: The response surface method in step eight is to establish a simulation model between performance indicators and influencing factors through appropriate experimental or simulation data, suitable mathematical models and fitting methods.

5. The method for establishing a sponge city design optimization model according to claim 4, characterized in that: The particle swarm optimization method in step 10 is a method for finding the optimal solution through cooperation and information sharing among individuals in a group.

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