Low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation

Abstract

The invention discloses a low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation. The method comprises the following steps: collecting related data of a target area; constructing a rainstorm management model; determining an objective function, a decision variable and a constraint condition, and constructing a multi-objective optimization model based on a second-generation non-dominated sorting genetic algorithm; coupling the rainstorm management model with the multi-objective optimization model to obtain a coupling model, and performing iterative optimization on the coupling model to obtain an optimal solution set; determining a multi-target weight through an entropy weight method and an analytic hierarchy process, and comprehensively evaluating the optimal solution set according to the multi-target weight through an approximate ideal solution sorting method to obtain a low-impact development measure planning scheme; the objective function is determined based on the investment cost, hydrological control capability and environmental benefits of the target area. The method can effectively balance the multi-objective relation, achieves scientific planning of low-impact development measure layout, gives consideration to economic benefits, hydrological regulation and control and environment improvement, and can be widely applied to the technical field of urban planning.

Description

Technical Field

[0001] This application relates to the technical field of urban planning, and particularly to a low impact development measure planning method based on multi-objective optimization and comprehensive evaluation. Background Art

[0002] With the acceleration of the urbanization process, problems such as flood disasters and water pollution caused by urban rainwater runoff have become increasingly serious. Traditional rainwater management models often focus on rapid drainage, ignoring the effective utilization of rainwater resources and the protection of the ecological environment. The concept of low impact development emerged as the times require, which emphasizes simulating natural hydrological processes through decentralized and small-scale source control measures to reduce rainwater runoff and its pollutant emissions, and protect and improve the urban ecological environment. However, in the process of planning and designing low impact development measures, various challenges are faced. For example, it is necessary to control construction and maintenance costs while achieving good hydrological control effects and significant environmental benefits. Previous planning methods are difficult to comprehensively consider these multi-objective factors and optimize them effectively. Summary of the Invention

[0003] To solve the above technical problems, the purpose of the present invention is to provide a low impact development measure planning method based on multi-objective optimization and comprehensive evaluation, which can comprehensively consider multi-objective factors.

[0004] To achieve the above purpose, one aspect of the embodiments of the present application proposes a low impact development measure planning method based on multi-objective optimization and comprehensive evaluation, including the following steps:

[0005] Collect relevant data of the target area;

[0006] Construct a stormwater management model based on the relevant data of the target area, and the stormwater management model is used to simulate the current situation of urban runoff and pollution load in the target area;

[0007] Determine the objective function, decision variables and constraint conditions according to the relevant data of the target area, and construct a multi-objective optimization model based on the second-generation non-dominated sorting genetic algorithm according to the objective function, the decision variables and the constraint conditions;

[0008] Couple the stormwater management model with the multi-objective optimization model to obtain a coupled model, and then solve the coupled model to obtain an optimal solution set;

[0009] Determine multi-objective weights through the entropy weight method and the analytic hierarchy process, and comprehensively evaluate the optimal solution set according to the multi-objective weights through the technique for order preference by similarity to an ideal solution to obtain a low impact development measure planning scheme.

[0010] In some embodiments, the target area-related data includes at least one or a combination of topographic data, land use data, rainfall data, drainage network data, or low impact development measure data.

[0011] In some embodiments, determining the objective function, decision variables, and constraint conditions according to the target area-related data specifically includes:

[0012] According to the target area-related data, calculate the investment cost, hydrological control ability, and environmental benefits;

[0013] According to the target area-related data, obtain the construction area and construction scope corresponding to different types of low impact development measures;

[0014] Use the investment cost, the hydrological control ability, and the environmental benefits as the objective function, use the construction area as the decision variable, and use the construction scope as the constraint condition.

[0015] In some embodiments, calculating the investment cost, hydrological control ability, and environmental benefits according to the target area-related data specifically includes:

[0016] According to the target area-related data, obtain the construction area, construction cost, operation cost, and maintenance cost corresponding to different types of low impact development measures, and then calculate the investment cost according to the construction area, the construction cost, the operation cost, and the maintenance cost;

[0017] According to the target area-related data, calculate the runoff reduction rate and the comprehensive pollutant reduction rate of the target area, and then calculate the hydrological control ability according to the runoff reduction rate and the comprehensive pollutant reduction rate;

[0018] According to the target area-related data, calculate the runoff carbon emission reduction amount, pollutant carbon emission reduction amount, annual carbon sequestration amount of plants, and building energy-saving carbon emission reduction amount of the target area, and then calculate the environmental benefits according to the runoff carbon emission reduction amount, the pollutant carbon emission reduction amount, the annual carbon sequestration amount of plants, and the building energy-saving carbon emission reduction amount.

[0019] In some embodiments, coupling the stormwater management model with the multi-objective optimization model to obtain a coupled model, and then solving the coupled model to obtain an optimal solution set specifically includes:

[0020] Call the database in the stormwater management model through the second-generation non-dominated sorting genetic algorithm to obtain the coupled model;

[0021] Randomly generate a combination of low impact development measure construction areas as a set of initial solutions, and construct a population based on the initial solutions;

[0022] Calculate the objective function values of each set of initial solutions in the population according to the coupling model;

[0023] Perform non-dominated sorting on each of the objective function values to obtain non-dominated ranks, and calculate the crowding degree for each of the objective function values to obtain the crowding degree distance;

[0024] Update the population according to the non-dominated ranks and the crowding degree distance;

[0025] Set an iteration termination condition, and stop updating the population according to the iteration termination condition to obtain the optimized solution set.

[0026] In some embodiments, the determination of the multi-objective weights by the entropy weight method and the analytic hierarchy process specifically includes:

[0027] Process the investment cost data, hydrological control ability data, and environmental benefit data in the optimized solution set by the entropy weight method to obtain multi-objective objective weights;

[0028] Compare the importance of the investment cost data, the hydrological control ability data, and the environmental benefit data by the analytic hierarchy process to obtain a judgment matrix;

[0029] Calculate the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue to obtain multi-objective subjective weights;

[0030] Perform weighted averaging on the multi-objective objective weights and the multi-objective subjective weights to obtain the multi-objective weights.

[0031] In some embodiments, the comprehensive evaluation of the optimized solution set by the technique for order preference by similarity to an ideal solution according to the multi-objective weights to obtain a low impact development measure planning scheme specifically includes:

[0032] Determine the positive ideal solution and the negative ideal solution according to the multi-objective weights and the optimized solution set;

[0033] Calculate the distances between each optimized solution in the optimized solution set and the positive ideal solution and the negative ideal solution;

[0034] Calculate the relative closeness of each optimized solution according to the distances, and determine the optimized solution with the maximum relative closeness as the low impact development measure planning scheme.

[0035] To achieve the above object, another aspect of the embodiments of the present application proposes a low impact development measure planning system based on multi-objective optimization and comprehensive evaluation, including:

[0036] A data collection module for collecting data related to the target area;

[0037] A rainstorm management model construction module for constructing a rainstorm management model according to the data related to the target area, where the rainstorm management model is used to simulate the current situation of urban runoff and pollution load in the target area;

[0038] A multi-objective optimization model construction module for determining an objective function, decision variables, and constraint conditions according to the data related to the target area, and constructing a multi-objective optimization model based on the second-generation non-dominated sorting genetic algorithm according to the objective function, the decision variables, and the constraint conditions;

[0039] A model solving module for coupling the rainstorm management model with the multi-objective optimization model to obtain a coupled model, and then solving the coupled model to obtain an optimized solution set;

[0040] A solution determination module for determining multi-objective weights by the entropy weight method and the analytic hierarchy process, and comprehensively evaluating the optimized solution set according to the multi-objective weights by the technique for order preference by similarity to an ideal solution to obtain a low impact development measure planning solution.

[0041] To achieve the above object, on the other hand, an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory. When the program is executed by the processor, it realizes the low impact development measure planning method based on multi-objective optimization and comprehensive evaluation as described above.

[0042] To achieve the above object, on the other hand, an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the low impact development measure planning method based on multi-objective optimization and comprehensive evaluation as described above.

[0043] The beneficial effects of the present invention are as follows: The low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation of the present invention can accurately simulate the current situation of urban runoff and pollution load by comprehensively collecting various types of data in the target area to construct a stormwater management model, and establish a multi-objective optimization model based on the second-generation non-dominated sorting genetic algorithm with the investment cost, hydrological control ability, and environmental benefits as the objective functions. Combining iterative and comprehensive evaluation methods, it not only fully considers the life-cycle cost of low-impact development measures but also can scientifically quantify hydrological and environmental related indicators. In addition, by using the entropy weight method, analytic hierarchy process, and technique for order preference by similarity to an ideal solution to reasonably determine the target weights and select the best low-impact development measure planning scheme, it can effectively balance the multi-objective relationship, realize the scientific planning of the layout of low-impact development measures, and take into account economic benefits, hydrological regulation, and environmental improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following introduces the drawings required to be used in the embodiments of the present invention. It should be understood that the drawings introduced below only facilitate the clear expression of some embodiments of the technical solutions in the present invention, and those skilled in the art can also obtain other drawings based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart of the steps of a low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation provided by an embodiment of the present invention;

[0046] Figure 2 It is a schematic diagram of the working principle flow of a low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation provided by an embodiment of the present invention;

[0047] Figure 3 It is a schematic diagram of the structure of a development measure planning system based on multi-objective optimization and comprehensive evaluation provided by an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions and advantages of this application more clear and understandable, the following further elaborates on this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application. When the following description involves the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the embodiments of this application. They are only examples of devices and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0050] It can be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "while...", or "in response to determining".

[0051] The terms "at least one", "multiple", "each", "any one", etc. used in this application, at least one includes one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any one refers to any one of the multiple.

[0052] With the acceleration of the urbanization process, problems such as flood disasters and water pollution caused by urban rainwater runoff have become increasingly serious. Traditional rainwater management models often focus on rapid drainage, ignoring the effective utilization of rainwater resources and the protection of the ecological environment. The concept of low impact development emerged as the times require. It emphasizes simulating natural hydrological processes through decentralized and small-scale source control measures to reduce rainwater runoff and its pollutant emissions, and protect and improve the urban ecological environment. However, in the process of planning and designing low impact development measures, there is a challenge of how to determine the best measure combination and layout while meeting multiple requirements. For example, it is necessary to control the construction and maintenance costs while achieving good hydrological control effects and significant environmental benefits. Previous planning methods are difficult to comprehensively consider these multi-objective factors and optimize them effectively.

[0053] To this end, the embodiments of the present invention propose a planning method for low-impact development measures based on multi-objective optimization and comprehensive evaluation. By comprehensively collecting various types of data in the target area to construct a stormwater management model, it can accurately simulate the current situation of urban runoff and pollution load. Moreover, a multi-objective optimization model based on the second-generation non-dominated sorting genetic algorithm is established with investment cost, hydrological control ability, and environmental benefits as the objective functions. Combining iterative and comprehensive evaluation methods, it fully considers the life-cycle cost of low-impact development measures and can scientifically quantify hydrological and environmental-related indicators. In addition, by using the entropy weight method, analytic hierarchy process, and technique for order preference by similarity to an ideal solution to reasonably determine the target weights and select the best low-impact development measure planning scheme, it can effectively balance multi-objective relationships, realize scientific planning of the layout of low-impact development measures, and take into account economic benefits, hydrological regulation, and environmental improvement.

[0054] Referring to Figure 1 and Figure 2 , Figure 1 FIG. is a flowchart of the steps of a planning method for low-impact development measures based on multi-objective optimization and comprehensive evaluation provided by the embodiments of the present invention. Figure 2 FIG. is a schematic diagram of the working principle flow of a planning method for low-impact development measures based on multi-objective optimization and comprehensive evaluation provided by the embodiments of the present invention. The embodiments of the present invention propose a planning method for low-impact development measures based on multi-objective optimization and comprehensive evaluation, and the method includes steps S101 to S105:

[0055] S101. Collect relevant data of the target area;

[0056] Further as an optional implementation manner, the relevant data of the target area includes at least one or a combination of topographic data, land use data, rainfall data, drainage network data, or low-impact development measure data.

[0057] Exemplarily, the topographic data may include data such as the elevation, slope, and aspect of the target area; the land use data may include the area and location of land use types such as residential, commercial, industrial, green space, and water area in the target area; the rainfall data may include the hourly rainfall data collected over the years in the target area, and the hourly rainfall data covers data such as rainfall time, intensity, and duration; the pipe network data may include the pipe diameter, pipe length, pipe material, connection points, and inspection wells of the drainage network; the low-impact development measure data may include the construction, operation, and maintenance costs of low-impact development measures and the design parameters in the stormwater management model (SWMM model).

[0058] S102. Construct a stormwater management model according to the relevant data of the target area, and the stormwater management model is used to simulate the current situation of urban runoff and pollution load in the target area;

[0059] In some alternative embodiments, when constructing a stormwater management model (SWMM model) to simulate the current situation of urban runoff and pollution load in the target area, the parameter settings of the stormwater management model (SWMM model) are calibrated and verified based on the actual data of the target area to ensure the accuracy of the simulation results. Through various parameters set in the model, such as the surface roughness coefficient and infiltration coefficient determined according to different land use types, combined with relevant parameters of the drainage pipe network, the confluence, runoff process of rainwater in the target area under the action of rainfall and the corresponding pollution load generated are simulated.

[0060] S103. Determine the objective function, decision variables and constraints according to the relevant data of the target area, and construct a multi-objective optimization model based on the second-generation non-dominated sorting genetic algorithm according to the objective function, decision variables and constraints;

[0061] Among them, the objective function is determined based on the investment cost, hydrological control ability and environmental benefits of the target area.

[0062] Specifically, in the multi-objective optimization model with the investment cost, hydrological control ability and environmental benefits as the objective function, the calculation of the investment cost considers the construction cost, annual operation and maintenance cost of low impact development measures; the hydrological control ability is quantified by indicators such as the total runoff control rate and pollutant reduction rate; the environmental benefits are evaluated in terms of urban runoff reduction, pollutant reduction, plant carbon sequestration and building energy conservation.

[0063] Further as an alternative implementation manner, the step of determining the objective function, decision variables and constraints according to the relevant data of the target area can be specifically divided into the following steps S1031 to S1033:

[0064] S1031. Calculate the investment cost, hydrological control ability and environmental benefits according to the relevant data of the target area;

[0065] Further as an alternative implementation manner, step S1031 can be specifically divided into the following steps S10311 to S10313:

[0066] S10311. Obtain the construction area, construction cost, operation cost and maintenance cost corresponding to different types of low impact development measures according to the relevant data of the target area, and then calculate the investment cost according to the construction area, construction cost, operation cost and maintenance cost;

[0067] Specifically, the investment cost of low impact development measures includes the construction cost, operation cost and maintenance cost, which is the sum of the construction cost, operation cost and maintenance cost. The calculation formula of the investment cost IC is shown as follows:

[0068]

[0069] Among them, IC represents the investment cost of low impact development measures in the target area, with the unit of yuan; IC C,i represents the construction cost corresponding to the i-th low impact development measure, with the unit of yuan / m 2 ; IC O,i and IC P,i respectively represent the annual operation and maintenance costs of the i-th low impact development measure, with the unit of yuan / m 2 ; N i represents the service life of the i-th low impact development measure; LID i represents the construction area of the i-th low impact development measure, with the unit of m 2 .

[0070] S10312. According to the relevant data of the target area, calculate the runoff reduction rate and the comprehensive pollutant reduction rate of the target area, and then calculate the hydrological control ability according to the runoff reduction rate and the comprehensive pollutant reduction rate;

[0071] Specifically, the hydrological control ability of low impact development measures is quantified from the runoff volume control rate and the comprehensive pollutant reduction rate. The calculation formula of the hydrological control ability HCC is as follows:

[0072] HCC = R 1 + R 2

[0073]

[0074] Among them, HCC represents the hydrological control ability; R 1 represents the runoff reduction rate; R 2 represents the comprehensive pollutant reduction rate; R base and R after respectively represent the runoff volumes before and after the implementation of low impact development measures, with the unit of m 3 ; PL base,k and PL after,k respectively represent the output loads of k kinds of pollutants before and after the implementation of low impact development measures, with the unit of kg; ω 1 , ω 2 , ω 3 , ω 4 respectively represent total suspended solids, chemical oxygen demand, total nitrogen and total phosphorus, and ω 1 = ω 2 = ω 3 = ω 4 = 0.25.

[0075] S10313. Calculate the runoff carbon emission reduction, pollutant carbon emission reduction, annual carbon sequestration of plants, and building energy-saving carbon emission reduction in the target area based on the relevant data of the target area, and then calculate the environmental benefits based on the runoff carbon emission reduction, pollutant carbon emission reduction, annual carbon sequestration of plants, and building energy-saving carbon emission reduction.

[0076] Specifically, the environmental benefits of low-impact development measures are quantified from aspects such as urban runoff reduction, pollutant reduction, plant carbon sequestration, and building energy conservation. The calculation formula for the environmental benefit EB is shown as follows:

[0077] EB = E 1 + E 2 + E 3 + E 4

[0078]

[0079] E 4 = αγ 1 LID GR

[0080] Among them, EB represents the environmental benefit; E 1 represents the carbon emission reduction due to the reduction of urban runoff (i.e., runoff carbon emission reduction), with the unit of kg / a; E 2 represents the carbon emission reduction due to the reduction of runoff pollution (i.e., pollutant carbon emission reduction), with the unit of kg / a; E 3 represents the annual carbon sequestration of low-impact development measures, with the unit of kg / a; E 4 represents the carbon emission reduction of building energy conservation brought by the green roof (i.e., building energy-saving carbon emission reduction), with the unit of kg / a; represents the operating energy consumption of pipe networks and pumping stations, with the unit of kwh / m 3 ; α represents the coal-fired power generation emission coefficient in China; represents the energy consumption of the sewage treatment plant for each runoff pollutant, with the unit of kwh / kg. Here, the total suspended solids are mainly considered, and other pollutants are converted using the energy consumption coefficient δ k for conversion; θ i represents the average carbon sequestration capacity of green plants, with the unit of kg / a; γ 1 represents the energy saved by the green roof every year, with the unit of kwh / m 2 .

[0081] S1032. Obtain the construction area and construction scope corresponding to different types of low-impact development measures according to the relevant data of the target area;

[0082] S1033. Take the investment cost, hydrological control ability, and environmental benefit as the objective function, the construction area as the decision variable, and the construction scope as the constraint condition.

[0083] Specifically, taking the construction areas of low impact development measures such as bioretention ponds, green roofs, permeable pavements, and grass swales as decision variables, by reasonably adjusting the construction areas of low impact development measures, the optimal solution under the requirements of multiple objectives is sought. The objective function constructed with investment cost, hydrological control ability, and environmental benefits is shown as follows:

[0084] min F = {IC, - HCC, - EB}

[0085] Among them, IC represents investment cost, HCC represents hydrological control ability, and EB represents environmental benefits.

[0086] The constraint conditions constructed with the construction scope are shown as follows:

[0087] A min,i ≤ LID i ≤ A max,i

[0088] Among them, A min,i and A max,i respectively represent the lower limit and upper limit of the construction area of the i-th low impact development measure in the study area.

[0089] S104. Couple the stormwater management model with the multi-objective optimization model to obtain a coupled model, and then solve the coupled model to obtain an optimized solution set;

[0090] Specifically, combine the constructed stormwater management model (SWMM model) with the multi-objective optimization model, and perform iterative optimization on the implementation areas of low impact development measures to obtain a series of optimized solution sets. These solution sets contain multiple groups of data on investment cost, hydrological control ability, and environmental benefits under different combinations of construction areas of low impact development measures, providing multiple options for subsequent comprehensive evaluation.

[0091] Further, as an optional implementation method, the step of coupling the stormwater management model with the multi-objective optimization model to obtain a coupled model, and then solving the coupled model to obtain an optimized solution set can be specifically divided into the following steps S1041 to S1046:

[0092] S1041. Call the database in the stormwater management model through the second-generation non-dominated sorting genetic algorithm to obtain a coupled model;

[0093] Specifically, call the dynamic database of the stormwater management model (SWMM model) through the second-generation non-dominated sorting genetic algorithm (NSGA-II algorithm), so that it can provide runoff and pollution load data under different combinations of low impact development measures for the multi-objective optimization model to calculate the objective function value.

[0094] S1042. Randomly generate a combination of the construction areas of low impact development measures as a set of initial solutions, and construct a population based on the initial solutions;

[0095] S1043. Calculate the objective function values of each set of initial solutions in the population according to the coupling model;

[0096] S1044. Perform non-dominated sorting on each objective function value to obtain the non-dominated rank, and calculate the crowding degree for each objective function value to obtain the crowding distance;

[0097] S1045. Update the population according to the non-dominated rank and the crowding distance;

[0098] S1046. Set the iteration termination condition, and stop updating the population according to the iteration termination condition to obtain the optimized solution set.

[0099] Specifically, first set the parameters of the algorithm. Exemplarily, set the population size to 100, the maximum number of iterations to 100, the crossover probability to 0.9, and the mutation probability to 0.1. Then initialize the population, and randomly generate a combination of the construction areas of low impact development measures as the initial solution. Then enter the iterative loop. In each iteration, calculate the objective function values (i.e., investment cost, hydrological control ability, and environmental benefits) of each individual (i.e., a combination of the construction areas of low impact development measures) in the population, and then generate a new population according to the genetic operations (selection, crossover, mutation) of the NSGA-II algorithm. Perform non-dominated sorting and crowding degree calculation on the new population to obtain the non-dominated rank and the crowding distance, and then select excellent individuals according to the non-dominated rank and the crowding distance to enter the next generation population. After each iteration, check whether the predetermined iteration termination condition is reached, or whether the optimized solution set converges (which can be judged by comparing the changes in the optimal solutions of consecutive generations of populations, such as the change in the objective function value is less than a certain threshold), or whether a specific accuracy requirement is met (such as the error of each objective function value is controlled within a certain range). If the iteration termination condition is satisfied, stop the iteration and output the optimized solution set; otherwise, continue the iteration.

[0100] S105. Determine the multi-objective weights through the entropy weight method and the analytic hierarchy process, and comprehensively evaluate the optimized solution set according to the multi-objective weights through the technique for order preference by similarity to an ideal solution to obtain the low impact development measure planning scheme.

[0101] Specifically, in the process of determining the weights of each objective by combining the entropy weight method and the analytic hierarchy process (AHP), the entropy weight method is used to objectively determine the initial weights of each objective based on the degree of data dispersion, and the AHP is used to introduce expert experience to adjust and improve the weights, so as to obtain the weights of each objective that comprehensively consider subjective and objective factors. In the comprehensive evaluation of the optimization solution set by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS method), the TOPSIS method calculates the distances between each solution and the ideal solution and the negative ideal solution based on the weights of each objective and the performance of each solution in the optimization solution set on each objective, so as to determine the best implementation solution, and analyze and compare it with the current situation simulation to obtain the best low impact development measure planning solution.

[0102] Further, as an optional implementation method, the step of determining the multi-objective weights by the entropy weight method and the AHP can be specifically divided into the following steps S1051 to S1054:

[0103] S1051. Process the investment cost data, hydrological control ability data, and environmental benefit data in the optimization solution set by the entropy weight method to obtain the multi-objective objective weights;

[0104] S1052. Compare the importance of the investment cost data, hydrological control ability data, and environmental benefit data by the AHP to obtain the judgment matrix;

[0105] S1053. Calculate the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue to obtain the multi-objective subjective weights;

[0106] Specifically, the entropy weight method is used to determine the objective weights of each objective, and the investment cost, hydrological control ability, and environmental benefit data in the optimization solution set are processed. The objective weights corresponding to each objective are calculated through the following process:

[0107] (1) Construct the evaluation index matrix X:

[0108] X=(x ij ) m×n

[0109] (2) Calculate the weight P ij of the index j: ij :

[0110]

[0111] (3) Calculate the entropy value e j of the index j:

[0112]

[0113] (4) Calculate the difference factor d j :

[0114] d j = 1 - e j

[0115] (5) Calculate the objective weights of the targets

[0116]

[0117] Then, use the analytic hierarchy process to introduce expert experience to adjust the weights. Through pairwise comparison by experts in related fields on the relative importance of investment cost, hydrological control ability, and environmental benefits, construct a judgment matrix, and then calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector to obtain the subjective weights corresponding to each target.

[0118] S1054. Perform weighted averaging on the multi-objective objective weights and multi-objective subjective weights to obtain the multi-objective weights.

[0119] Specifically, use the weighted averaging method to obtain the multi-objective weights of each target through the following formula:

[0120]

[0121] where ω j represents the multi-objective weights, represents the multi-objective objective weights, represents the multi-objective subjective weights, and α is the weight distribution coefficient.

[0122] Furthermore, as an optional implementation method, use the technique for order preference by similarity to ideal solution (TOPSIS) to comprehensively evaluate the optimized solution set according to the multi-objective weights to obtain the low-impact development measure planning scheme. This step can be specifically divided into the following steps S1055 to S1057:

[0123] S1055. Determine the positive ideal solution and the negative ideal solution according to the multi-objective weights and the optimized solution set;

[0124] S1056. Calculate the distances between each optimized solution in the optimized solution set and the positive ideal solution and the negative ideal solution;

[0125] S1057. Calculate the relative closeness of each optimized solution according to the distances, and determine the optimized solution with the maximum relative closeness as the low-impact development measure planning scheme.

[0126] In some alternative embodiments, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS method) is used to comprehensively evaluate the optimized solution set based on the determined target weights. That is, according to the target weights and the performance of each solution in the optimized solution set in terms of objectives such as investment cost, hydrological control ability, and environmental benefits, the distances from the positive ideal solution and the negative ideal solution are calculated. By comparing the relative distances of each solution from the positive ideal solution and the negative ideal solution, the solution that is closer to the positive ideal solution and farther from the negative ideal solution is determined as the best low-impact development measure planning solution, and the combination of the construction areas of the low-impact development measures corresponding to this solution is the optimal layout solution for the low-impact development planning of the target area.

[0127] Specifically, first, based on the final weights of each objective (i.e., the multi-objective weights), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS method) is used to comprehensively evaluate the optimized solution set, and then the positive ideal solution and the negative ideal solution are determined. The positive ideal solution is the combination of the objective function values corresponding to the solution with the lowest investment cost, the strongest hydrological control ability, and the highest environmental benefits under each objective weight, while the negative ideal solution is the opposite. Then, the distances of each solution from the positive ideal solution and the negative ideal solution are calculated. For a certain solution among them, the distances from the positive ideal solution and the negative ideal solution can be calculated using the Euclidean distance formula. And the relative closeness of each solution is calculated. The greater the relative closeness, the closer the solution is to the positive ideal solution and the farther it is from the negative ideal solution. Finally, the relative closeness of each solution is compared, and the solution with the largest relative closeness is selected as the best low-impact development measure planning solution. The calculation process of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS method) is as follows:

[0128] (1) Construct and normalize the decision matrix r′ ij :

[0129]

[0130] (2) Calculate the weighted normalized matrix v ij :

[0131] v ij = ω j × r′ ij

[0132] (3) Identify the positive ideal solution and the negative ideal solution

[0133]

[0134] (4) Calculate the distances of each solution from the positive ideal solution and the negative ideal solution and

[0135]

[0136] (5) Calculate the relative closeness degree C of each plan j :

[0137]

[0138] The above description is about the low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation in the embodiments of the present invention. It can be recognized that compared with the urban low-impact development measure planning method in the prior art, the embodiments of the present invention have the following advantages:

[0139] First, in the multi-objective optimization model, taking environmental benefits as one of the core objectives, not only considering the traditional hydrological control ability and pollutant reduction, but also introducing environmental indicators such as plant carbon sequestration and building energy conservation. Through the comprehensive quantitative evaluation of these indicators, it can comprehensively reflect the multi-faceted contributions of low-impact development measures to the ecological environment, overcoming the limitation that the traditional model does not fully consider environmental benefits;

[0140] Second, combining the multi-objective optimization and comprehensive evaluation methods, determining the weights of each objective through the entropy weight method and the analytic hierarchy process, and reasonably balancing the relationships between different objectives. The multi-objective optimization model first generates multiple optimization solution sets, and then uses the TOPSIS method to comprehensively evaluate the solution sets to select the optimal implementation plan. This method combines objective data and expert experience, avoids the bias of a single optimization method, and can provide a more scientific and comprehensive decision-making basis to ensure the balance of the economy, environment, and hydrological benefits of low-impact development planning.

[0141] Referring to Figure 3 , the embodiments of the present invention also provide a low-impact development measure planning system based on multi-objective optimization and comprehensive evaluation, including:

[0142] A data collection module for collecting relevant data of the target area;

[0143] A rainstorm management model construction module for constructing a rainstorm management model according to the relevant data of the target area, and the rainstorm management model is used to simulate the current situation of urban runoff and pollution load in the target area;

[0144] A multi-objective optimization model construction module for determining the objective function, decision variables, and constraint conditions according to the relevant data of the target area, and constructing a multi-objective optimization model based on the second-generation non-dominated sorting genetic algorithm according to the objective function, decision variables, and constraint conditions;

[0145] A model solution module for coupling the rainstorm management model and the multi-objective optimization model to obtain a coupling model, and then solving the coupling model to obtain an optimization solution set;

[0146] A solution determination module is used to determine multi-objective weights through the entropy weight method and the analytic hierarchy process, and comprehensively evaluate the optimization solution set according to the multi-objective weights through the technique for order preference by similarity to an ideal solution (TOPSIS) to obtain a low impact development measure planning solution;

[0147] Among them, the objective function is determined based on the investment cost, hydrological control ability, and environmental benefits of the target area.

[0148] The content in the embodiment of the low impact development measure planning method based on multi-objective optimization and comprehensive evaluation is applicable to the embodiment of the low impact development measure planning system based on multi-objective optimization and comprehensive evaluation. The functions specifically implemented by the embodiment of the low impact development measure planning system based on multi-objective optimization and comprehensive evaluation are the same as those in the embodiment of the low impact development measure planning method based on multi-objective optimization and comprehensive evaluation, and the beneficial effects achieved are also the same as those in the embodiment of the low impact development measure planning method based on multi-objective optimization and comprehensive evaluation.

[0149] An embodiment of the present invention also provides an electronic device. The electronic device includes: a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory. When the program is executed by the processor, it implements the above-mentioned low impact development measure planning method based on multi-objective optimization and comprehensive evaluation. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0150] As Figure 4 shown is a schematic hardware structure diagram of the electronic device provided by an embodiment of the present invention. Referring to Figure 4 , an embodiment of the present invention provides an electronic device, including:

[0151] A processor 1001, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention;

[0152] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1002 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1002 and are called by the processor 1001 to execute the low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation according to the embodiments of the present invention;

[0153] The input / output interface 1003 is used to implement information input and output;

[0154] The communication interface 1004 is used to implement communication interaction between this device and other devices. It can communicate through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0155] The bus 1005 transmits information between various components of the device (such as the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004);

[0156] Among them, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 are communicatively connected to each other inside the device through the bus 1005.

[0157] The embodiments of the present invention also provide a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned low-impact development measure planning method based on multi-objective optimization and comprehensive evaluation.

[0158] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] An embodiment of the present invention also discloses a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 the method shown.

[0160] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order mentioned in the operation diagrams. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the above-mentioned blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0161] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More precisely, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It can also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0162] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0163] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0164] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGA), field-programmable gate arrays (FPGA), etc.

[0165] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

[0166] The above is a specific description of the preferred embodiment of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation, characterized in that: The following steps are involved: Collect relevant data about the target area; Constructing a storm management model based on the target area related data, wherein the storm management model is used to simulate the current status of urban runoff and pollution load in the target area; Determine the objective function, decision variables and constraints according to the target area related data, and construct a multi-objective optimization model based on the second generation non-dominated sorting genetic algorithm according to the objective function, the decision variables and the constraints; The rainstorm management model is coupled with the multi-objective optimization model to obtain a coupled model, and then the coupled model is iteratively optimized to obtain an optimized solution set; Determine the multi-objective weights by using the entropy weight method and the hierarchical analysis method, and comprehensively evaluate the optimization solution set according to the multi-objective weights by using the approximate ideal solution sorting method to obtain a low-impact development measure planning scheme; Wherein, the objective function is determined based on the investment cost, hydrological control capability and environmental benefits of the target area.

2. A low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation according to claim 1, characterized in that: The target area related data includes at least one or a combination of terrain data, land use data, rainfall data, drainage network data or low-impact development measures data.

3. The low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation according to claim 1 is characterized in that: Determining the objective function, decision variables and constraint conditions according to the target area related data specifically includes: Calculating the investment cost, the hydrological control capability and the environmental benefit according to the target area related data; According to the relevant data of the target area, obtain the construction area and construction scope corresponding to different types of low-impact development measures; The investment cost, the hydrological control capability and the environmental benefit are used as the objective function, the construction area is used as the decision variable, and the construction scope is used as the constraint condition.

4. The low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation according to claim 3 is characterized in that: The investment cost, hydrological control capability and environmental benefits are calculated based on the relevant data of the target area, specifically including: According to the target area related data, the construction area, construction cost, operation cost and maintenance cost corresponding to different types of low-impact development measures are obtained, and then the investment cost is calculated according to the construction area, the construction cost, the operation cost and the maintenance cost; According to the target area related data, the runoff reduction rate and the comprehensive pollutant reduction rate of the target area are calculated, and then according to the runoff reduction rate and the comprehensive pollutant reduction rate, the hydrological control capacity is calculated; Based on the relevant data of the target area, the runoff carbon emission reduction, pollutant carbon emission reduction, annual plant carbon fixation and building energy-saving carbon emission reduction in the target area are calculated, and then the environmental benefit is calculated based on the runoff carbon emission reduction, the pollutant carbon emission reduction, the annual plant carbon fixation and the building energy-saving carbon emission reduction.

5. The low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation according to claim 1 is characterized in that: The coupling of the rainstorm management model with the multi-objective optimization model to obtain a coupled model, and then iteratively optimizing the coupled model to obtain an optimized solution set specifically includes: Calling the database in the rainstorm management model through the second-generation non-dominated sorting genetic algorithm to obtain the coupling model; Randomly generate low-impact development measures construction area combinations as a set of initial solutions, and construct a population based on the initial solutions; Calculating the objective function value of each group of the initial solutions in the population according to the coupling model; Performing non-dominated sorting on each of the objective function values ​​to obtain a non-dominated level, and performing congestion calculation on each of the objective function values ​​to obtain a congestion distance; updating the population according to the non-dominated level and the crowding distance; An iteration termination condition is set, and updating of the population is stopped according to the iteration termination condition to obtain the optimization solution set.

6. The low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation according to claim 1 is characterized in that: Determining the multi-objective weights by the entropy weight method and the hierarchical analysis method specifically includes: The investment cost data, hydrological control capacity data and environmental benefit data in the optimization solution set are processed by the entropy weight method to obtain multi-objective objective weights; Comparing the importance of the investment cost data, the hydrological control capacity data and the environmental benefit data by using the analytic hierarchy process to obtain a judgment matrix; Calculating the maximum eigenvalue of the judgment matrix and the eigenvector corresponding to the maximum eigenvalue to obtain the multi-objective subjective weight; The multi-objective weights are weighted averaged with respect to the multi-objective objective weights and the multi-objective subjective weights to obtain the multi-objective weights.

7. The low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation according to claim 1 is characterized in that: The method of sorting by approaching the ideal solution comprehensively evaluates the optimization solution set according to the multi-objective weights to obtain a low-impact development measures planning scheme, specifically including: Determine a positive ideal solution and a negative ideal solution according to the multi-objective weights and the optimization solution set; Calculating the distance between each optimized solution in the optimized solution set and the positive ideal solution and the negative ideal solution; The relative closeness of each of the optimization solutions is calculated based on the distance, and the optimization solution with the largest relative closeness is determined as the low-impact development measure planning scheme.

8. A low-impact development measures planning system based on multi-objective optimization and comprehensive evaluation, characterized in that: include: A data collection module, used to collect data related to the target area; A rainstorm management model building module, used to build a rainstorm management model based on the target area related data, and the rainstorm management model is used to simulate the current status of urban runoff and pollution load in the target area; A multi-objective optimization model building module is used to determine the objective function, decision variables and constraints according to the target area related data, and to build a multi-objective optimization model based on the second generation non-dominated sorting genetic algorithm according to the objective function, the decision variables and the constraints; A model solving module, used for coupling the rainstorm management model with the multi-objective optimization model to obtain a coupled model, and then solving the coupled model to obtain an optimized solution set; A scheme determination module is used to determine the multi-objective weights by using the entropy weight method and the hierarchical analysis method, and to comprehensively evaluate the optimization solution set according to the multi-objective weights by using the approximate ideal solution sorting method to obtain a low-impact development measure planning scheme; Wherein, the objective function is determined based on the investment cost, hydrological control capability and environmental benefits of the target area.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation as described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the low-impact development measures planning method based on multi-objective optimization and comprehensive evaluation as described in any one of claims 1 to 7.

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