CN112906950B - Sponge city planning and designing system - Google Patents
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Abstract
The invention relates to a sponge city planning and designing system, which comprises: the sponge facility determining module is used for selecting sponge facilities suitable for project use from the selectable facilities according to the design rules; the sponge simulation model is used for obtaining a sponge model simulation result through system calculation, site calculation and LID facility calculation based on the selected sponge facilities; the sponge optimization evaluation module is used for carrying out optimization analysis on the sponge model based on a discrete search algorithm by taking a combination of an optimization target and constraint conditions as an optimization condition to obtain an optimal design scheme set; the optimization targets comprise minimum construction investment, maximum runoff control targets, maximum surface runoff pollution control targets, minimum pollutant load, minimum flood peak flow and minimum operation maintenance cost; the constraint conditions comprise annual runoff total control rate, runoff pollution control rate, flood peak flow and four guiding indexes. The invention improves the design efficiency and scientificity of LID facilities in sponge city construction.
Description
Technical Field
The invention belongs to the technical field of sponge city planning, and particularly relates to a sponge city planning and designing system.
Background
The sponge city construction mainly advocates simulating natural conditions, and the hydrologic characteristics after regional development are basically consistent with those before development by using some miniature decentralized ecological treatment technologies at the source, so that the influence of land development on the ecological environment is ensured to be minimized. Through low impact development and construction, ecological objectives that can be achieved are: protecting water quality, reducing runoff, prolonging runoff accumulation time, reducing flood peak, supplementing groundwater, reducing land erosion, etc. The ecological system is recovered to the level before development and construction as much as possible through comprehensive utilization of low-impact development rainwater, damage to the ecological system due to land development activities is repaired, and the aims of controlling non-point source pollution, protecting water bodies and supplementing river base flows are further achieved.
In the prior art, the sponge model is mainly (1) a single model, is not combined with the whole system, and lacks practicality; (2) The black box model is a lot and lacks necessary theoretical basis support; (3) The foreign computing engine and simulation algorithm are directly adopted, and the innovation is lacking. (4) Laboratory research results are usually adopted for calibration and verification, and engineering is lacking.
Disclosure of Invention
The invention aims to provide a sponge city planning and design system, which is used for planning or designing an optimized LID facility configuration design scheme according to project positions and water quantity and quality control targets or total investment limits.
The invention provides a sponge city planning and designing system, which comprises:
the sponge facility determining module is used for selecting sponge facilities suitable for project use from the selectable facilities according to the design rules;
the sponge simulation model is used for obtaining a sponge model simulation result through system calculation, site calculation and LID facility calculation based on the selected sponge facilities;
the sponge optimization evaluation model is used for carrying out optimization analysis on the sponge model based on a discrete search algorithm by taking a combination of an optimization target and constraint conditions as an optimization condition to obtain an optimal design scheme set; the optimization targets comprise minimum construction investment, maximum runoff control targets, maximum surface runoff pollution control targets, minimum pollutant load, minimum flood peak flow and minimum operation maintenance cost; the constraint conditions comprise annual runoff total control rate, runoff pollution control rate, flood peak flow and four guiding indexes; the four guiding indexes comprise green roof proportion, sinking green land proportion, permeable pavement and impermeable water pad surface runoff control proportion;
the optimal scheme output module is used for outputting an optimal design scheme set in the form of an optimal analysis report form, so that a user can select a proper optimal scheme.
Further, the selectable facilities comprise preset facilities and custom facilities, wherein the preset facilities comprise green roofs, blue roofs, biological detention facilities, permeable pavement, sinking greenbelts, permeation ponds, permeation wells, vegetation furrows, rain buckets and wetlands.
Further, the preset facilities are obtained according to the planning and design of occupied area, catchment area, use conditions and terrain elements, and the custom facilities are obtained according to the planning and design of geographical positions and climates of the places where the projects are located, the inheritance of resource energy, the development level and scale, the technical maturity, regional requirements and economic and technical characteristic elements.
Further, the design rule includes:
selecting sponge facilities according to the regional characteristics of the project; and constructing a design scheme according to the target and constraint conditions required by the user.
Further, the sponge simulation model comprises a system calculation module, a site calculation module and a LID facility calculation module;
the system calculation module is used for calculating rainfall and evaporation in unit time, taking the rainfall and evaporation as input parameters of the site calculation module and the LID facility calculation module, and outputting calculation results of the site calculation module and the LID facility calculation module to a result data table of the system calculation module after the site calculation module and the LID facility calculation module are operated;
the site calculation module is used for calculating the production convergence condition of the permeable surface and the impermeable surface in the site and the corresponding pollutant accumulation and attenuation condition according to the rainfall and evaporation parameters provided by the system calculation module, counting the hydrologic water quality data of the LID facilities in the converged site, and outputting the counted results to a site result data table;
the LID calculation module is used for calculating the water quantity and water quality change condition of the LID facility according to the rainfall and evaporation parameters provided by the system calculation module and the yield converging and pollutant accumulating parameters provided by the site calculation module, and outputting the result to the LID report data table.
Further, the sponge optimization evaluation model comprises a cost calculation function module, a constraint function module, an optimization algorithm module, an evaluation module and a search group module, and is used for obtaining an optimal LID configuration scheme by taking the sponge simulation model as an evaluator of the sponge optimization evaluation model according to an optimization target and an LID cost function.
By means of the scheme, through the sponge city planning and designing system, the LID design and layout in the sponge city construction scheme are optimized through the sponge optimization evaluation model, corresponding LID facility design and construction are assisted in decision making, and the design efficiency and scientificity of the LID facilities in the sponge city construction are improved.
The foregoing description is only an overview of the present invention, and is intended to provide a more thorough understanding of the present invention, and is to be accorded the full scope of the present invention.
Drawings
FIG. 1 is a block diagram of a sponge city planning design system of the present invention;
FIG. 2 is a diagram of a simulated sponge model of the present invention;
FIG. 3 is a flow chart of the calculation of the sponge simulation model of the present invention;
FIG. 4 is a diagram of a sponge optimization evaluation model of the present invention;
FIG. 5 is a schematic diagram of a reference set generated by the linear combination method of the present invention;
FIG. 6 is a flowchart of the optimization algorithm calculation steps of the present invention;
FIG. 7 is a schematic diagram of the drainage patterns in the south area according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of the drainage patterns in the north area according to an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Referring to fig. 1, this embodiment provides a sponge city planning and designing system, which includes:
a sponge facility determining module 10 for selecting a sponge facility suitable for use in the project from the selectable facilities according to the design rule;
a sponge simulation model 20 for obtaining a sponge model simulation result through system calculation, site calculation and LID facility calculation based on the selected sponge facility;
the sponge optimization evaluation model 30 is used for carrying out optimization analysis on the sponge model based on a discrete search algorithm by taking a combination of an optimization target and constraint conditions as an optimization condition to obtain an optimal design scheme set; the optimization targets comprise minimum construction investment, maximum runoff control targets, maximum surface runoff pollution control targets, minimum pollutant load, minimum flood peak flow and minimum operation maintenance cost; the constraint conditions comprise annual runoff total control rate, runoff pollution control rate, flood peak flow and four guiding indexes; the four guiding indexes comprise green roof proportion, sinking green land proportion, permeable pavement and impermeable water pad surface runoff control proportion;
the optimal solution output module 40 is configured to output the optimal design solution set in the form of an optimal analysis report form, so that a user can select a suitable optimal solution.
In this embodiment, the optional facilities include preset facilities and custom facilities, where the preset facilities include green roofs, blue roofs, bio-detention facilities, permeable pavement, submerged greenbelts, permeable ponds, permeable wells, vegetation furrows, rain buckets, and wetlands.
In this embodiment, the preset facilities are obtained according to the planning and design of the occupied area, the catchment area, the use condition and the terrain element, and the custom facilities are obtained according to the planning and design of the geographical position and the climate of the project location, the inheritance of the resource energy, the state of development and scale, the technical maturity, the regional requirement and the economic and technical characteristic element.
In this embodiment, the design rule includes: (1) According to the regional characteristics of the project, a sponge facility (2) with a proper structure is recommended, and according to the target and constraint conditions required by a user, the most scientific design scheme is constructed.
Referring to fig. 2, the sponge simulation model (LID simulation model) is composed of three parts, namely a system calculation module, a site calculation module and a LID facility calculation module. The system computing module is mainly used for computing rainfall and evaporation in unit time, taking the rainfall and evaporation as input parameters of the site computing module and the LID facility computing module, and outputting computing results of the site computing module and the LID facility computing module to a result data table of the system computing module after the site computing module and the LID facility computing module are operated. The main function of the site calculation module is to calculate the production convergence condition of the permeable surface and the impermeable surface in the site and the corresponding pollutant accumulation and attenuation condition according to the rainfall and evaporation parameters provided by the system calculation module; meanwhile, the hydrologic water quality data of the LID facilities in the site are counted and collected, and the counted results are output to a site result data table. The main function of the LID calculation module is to calculate the water quantity and water quality change condition of the LID facility according to the rainfall and evaporation parameters provided by the system calculation module and the yield converging and pollutant accumulating parameters provided by the site calculation module, and output the result to the LID report data table.
When the LID simulation model is started, according to related parameters set by a user, rainfall data are read in each time step, rainfall and evaporation are calculated respectively, then the yield and convergence condition of each site are calculated respectively, and if a water quality simulation module is set, water quality calculation simulation of the sites is performed simultaneously. And then respectively calculating the hydrologic water quality change process of the LID facilities in each site according to the yield and water quality results of the underlying surface. And after the calculation of the LID facility is finished, submitting the result to a site module for summarizing the result of the LID module. After summarizing, the system calculation module summarizes the results of the site calculation module and outputs the results. The specific flow is shown in fig. 3.
In this embodiment, the main function of the sponge optimization evaluation model is to obtain an optimal LID configuration scheme according to an optimization target and LID cost function provided by a user. The optimization module consists of the following 5 parts, as shown in fig. 4.
(1) Cost calculation Function (Cost Function): since general optimization algorithms are all the most economical solutions to be sought, we call the cost calculation function. But it is also possible to calculate other functions, also called objective functions.
(2) Constraint function (Contraction Function): or constraint, which is typically a limitation of the optimization problem by the thing itself or externally.
(3) Optimization algorithm (Optimizer): algorithms for performing the optimization Search are generally general algorithms, and common algorithms include Genetic Algorithm (GA), discrete Search algorithm (Scatter Search), tabu algorithm (tab), and the like.
(4) Evaluation method (Evaluator): typically some model or formula such as a water volume simulation model, a water quality simulation model, etc.
(5) Searching groups: refers to a collection of objects that can be used by an optimization algorithm to optimize, such as available LID facilities, etc. The optimization algorithm will eventually determine the optimal object in the search cluster.
Objective function:
the objective function minimizes the overall cost of all LID facilities in the assessment scheme.
Wherein: c (C) i For the cost of the ith LID facility.
C all For the total cost of all LID facilities.
n is the number of LID facilities that need to be optimized.
As can be seen from the above equation, calculating the objective function requires determining the cost of each LID facility, and thus requires cost function establishment for each LID facility. Usually the cost of the LID facility is related to the design size, design specification, land price, price of the materials used, and construction cost of the LID facility. Thus, the present assessment model uses the following standard LID facility cost functions to make a single LID facility cost calculation.
Cost＝(A a ×Aera i A )×(D a ×Depth i D )+LandCost×Area+FixCost
Wherein: aa, ai are area coefficients; da, di is the depth coefficient; area and Depth are the Area and total Depth of the LID facility, respectively; land cost is the price per unit area; fixCost is the other cost (material cost, etc.).
Constraint conditions:
the constraint conditions mainly comprise three major categories of runoff control targets, flood peak reduction targets and pollutant control targets. The optimized LID facility scheme is required to meet the set three main targets. The runoff control targets comprise total outflow, total infiltration and total accumulation, and runoff control rate; the flood peak reduction targets comprise maximum outflow flow, average outflow flow and flood peak reduction rate; the pollutant control targets include total effluent pollutant load, maximum effluent pollutant concentration, and pollutant load reduction rate.
The evaluation method comprises the following steps:
the evaluation method is that the constructed low-influence water quantity and water quality calculation simulation model of the development facility is utilized to simulate the water quantity and water quality of the LID facility construction scheme constructed by the user, the simulated result is obtained, and the runoff control target, the flood peak reduction target and the pollutant control target are counted. I.e. the LID simulation model will act as an evaluator of the LID optimization evaluation model.
Searching groups:
searching for clusters, i.e., LID facilities optimization solutions. The number of LIDs, the type of LID, and the specification of LID are mainly included. The three aspects directly determine whether the preset runoff control target, the flood peak reduction target and the non-point source pollution reduction amount can be achieved in the sponge city construction process, and also determine the overall cost of the whole design scheme. The three possible value spaces are the search groups of the LID optimization evaluation model. The model will find the optimal solution set satisfying the constraint condition in the search cluster using the optimization algorithm.
Optimization algorithm:
there are many optimization algorithms available for multi-objective optimization such as genetic algorithm, multi-objective particle swarm algorithm MOPSO, multi-objective MOSCEM-UA, discrete search, etc. The algorithms have the advantages of high iteration efficiency, high approximation speed and the like.
(1) Genetic algorithm
Genetic algorithms are algorithms designed based on "survival in the right" mechanisms inspired by the phenomenon of biological evolution. The method represents the problem as the adaptation process of a chromosome, and finally converges to an individual in the optimal environment through continuous evolution (replication, crossover and mutation) operation, so as to obtain the optimal solution.
(2) MOPSO (metal-oxide-semiconductor field effect transistor) of multi-target particle swarm algorithm
Population intelligence resulting from actions such as collaboration and competition among individuals within a biological population can often provide an efficient solution to certain specific problems. Birds may communicate and share information between individuals during a search for food, and each member may benefit from the discovery and experience of other members. The advantage of such cooperation is decisive when the distribution of the food source is unpredictable: far greater than the disadvantages associated with competing foods. For the multi-target particle swarm algorithm MOPSO, the advantages are as follows: the algorithm has strong universality and does not depend on problem information; searching the group, and reserving optimal information of local and global groups, wherein the group has memory capacity; the principle is simple and easy to realize; and collaborative searching, and guiding searching by utilizing the local information and the group information of the individuals.
(3) Multi-target MOSCEM-UA
The multi-target MOSCEM-UA is an improved multi-target optimization algorithm proposed by Vrugt and the like on the basis of a single-target optimization algorithm of the SCE-UA. Firstly, generating offspring sample points by using a MOSCEM-UA algorithm in a covariance-based metaplis-analysis method instead of a descending simplex method in the SCE-UA algorithm, so as to avoid deterministic transfer of evolutionary computation to a single mode; and secondly, in the process of generating offspring through evolution, MOSCEM-UA does not decompose the complex form further, and different sample point updating processes are adopted, so that the trend of sinking into a local posterior density region is effectively avoided. The MOSCEM-UA algorithm can exchange parallel evolution sequence information in the evolution process by inheriting the Metroplis algorithm, controlling the random search, competing the evolution and the compound method. And (3) according to the transition probability of the Markov chain self-adaption adjustment, the continuous updating and evolution of the posterior probability density of the parameters are guaranteed, and finally the purposes of identifying the non-inferior parameters and posterior distribution are achieved. The method has wide application in the aspect of optimizing the hydrologic model, has less iteration times, and can be applied to the problem of multi-objective parameter optimization.
(4) Discrete Search algorithm (Scatter Search)
The discrete search method is also a cluster-based Meta-heuristic optimization algorithm like the genetic algorithm. SS is an evolving method established by combining other methods. The method first generates an initial set through diversity, and then selects a group of reference sets from the initial set. Based on the reference set, a new solution set is constructed by some other optimization algorithm (such as a linear combination method), and a new reference set is formed by an improved strategy.
The discrete search method and the tabu algorithm are combined to solve the complex optimization problem. The tabu algorithm is one of the efficient optimization algorithms. The tabu algorithm first randomly generates a set of sets and then selects its best neighbor set. The tabu algorithm uses a tabu list (tabu list) to record the most recently selected solution set. The tabu motion method is used to find a new preferred solution. The criteria for tabu search stops is when the number of optimizations reaches a set maximum number and the current optimal solution is not improved or has indeed been sought.
In discrete searching, an initial set is first generated using diversity, and then a reference set is selected from the initial set. The reference set is used as a basis for the production of a new reference set. And distinguishing solutions in the reference set, wherein the solutions are generated by crossing a good set and a bad set by adopting a linear combination method. After a new cross reference set is generated, the fitness of the cross reference set is improved by utilizing a tabu method; and returning the reference set as a new reference set to perform the next searching algorithm.
1) The diversity generation initial set method comprises the following steps:
the generation of two initial sets V' and V "(solving for the maximum capacity n) from an initial seed V using an integer h.
V'[1+k*h]＝1-V[1+k*h]k＝1,2,3,…n/h，k<n；
And is also provided with
2) Linear bonding method:
the process of cross-generating with a good set (X1, X2, X3) and a bad set (Y1, Y2) is shown in fig. 5:
comparing genetic algorithm with discrete search method, the discrete search method has high efficiency of finding out optimal solution rapidly.
Another important feature of the discrete search method is its method of dynamically handling constraints. In SS, a dynamic penalty function is employed to penalize schemes that violate constraints. The penalty function is executed taking into account the degree of violation of the constraint, the history of violation, and the like. For example, when no conditional feasible scheme exists, a conditional infeasible scheme will be more penalized than when a conditional feasible scheme exists.
The discrete search method has high efficiency of quickly finding the optimal solution, and is a global optimization algorithm with strong robustness. Therefore, the model is preferably calibrated by adopting a discrete search method. The discrete search method comprises the following calculation steps:
1) Determining a search group: searching the group, namely the value domain of the rating parameter which needs to be determined.
2) Coupling the objective function, constraint function, evaluation method and stop condition into the discrete algorithm. Wherein the stop condition is:
m=ms or find the optimal value.
Wherein: m is the number of times the optimization algorithm calculates; ms is the number of initial settings, ms=5000.
3) Determining a start search point: firstly, dividing a search group into 4 subsets with equal numbers, and then respectively establishing starting points in two steps: (1) randomly selecting a set from the 4 subsets; (2) and then randomly selecting 3 points in the selected subset. The starting point typically selected is the starting point, ending point, intermediate point, or user-defined point of the search cluster. Thus, 3 actuation points are determined: v1, V2, V3.
4) Determining an initial reference set: generating initial reference sets R1, R2 and R3 from starting points V1, V2 and V3 respectively according to a diversity method;
5) Dividing 3 reference sets into a good set and a bad set respectively by calculating the objective function values of each point in the reference set R1;
6) Constructing new solution sets R1, R2 and R3 by adopting a linear combination method, so as to start searching;
7) The above searches were all performed three times to search out three parameter values. And calculating an objective function of the search, and judging whether the constraint condition is violated. Finally, according to the result of the constraint condition, calculating the objective function value again by using the punishment function;
8) Updating the reference set by using a tabu method: in the whole optimization searching process, the reference set is continuously updated, and the updated result is that a new solution becomes better or the diversity of the data in the reference set is improved;
9) When a result is searched, if the reference is not updated or the result is not improved on the last search result, an update reference set algorithm is executed;
10 When the search stop condition is met (i.e., the number of searches reaches the set number of times 5000, and the solution is improved; or the optimal solution is obtained), the search stops. And returning to the user each appropriate solution.
The above steps are shown in fig. 6.
Referring to fig. 7 and 8, a certain project is a public green land, the annual runoff total control rate of the project is 97% (the corresponding design rainfall is 80 mm), the land is divided into a north area and a south area according to the trend of on-site rainwater, and the south area is mainly a hardened road surface, so that water permeable pavement is mainly adopted, a linear drainage system is combined, and filtering and infiltration facilities are combined at the tail end to purify water quality. The north area has a part of green land except for a part of hardened pavement, so that a combined LID facility of water permeable pavement, grass planting ditch, rainwater garden, water storage module is adopted. The specific rainwater flow chart is as follows:
1) Optimization objective
The objective of LID solution optimization is to minimize the overall cost of the solution while meeting constraints (runoff control, etc.).
According to the project budget list, the construction cost of the water storage module comprises earth excavation, earth backfill, yu Fang discarding, cushion construction, module installation, pipeline equipment connection, installation and debugging cost, and the comprehensive cost is 1680 yuan/m < 3 >; the construction cost of the rainwater garden comprises earth excavation, yu Fang discarding, soil detection, gravel water storage layer, soil layer configuration, covering layer and greening part, and the comprehensive cost is 350 yuan/m 2.
2) Constraint conditions
Because the design requirement of the project is to control 80mm rainfall, the target of the optimization is set to control 80mm rainfall under the working condition of 24 hours of 20 years of rainfall. I.e. the runoff control rate reaches 97%.
3) Optimizing objects
Because the main LID facilities in the south area are water permeable pavement, and the water permeable pavement of the project is a hard requirement, the south area cannot be optimized, and the object of the optimization is a north catchment area. According to the field investigation, the construction position of the grass planting ditch in the north area is relatively fixed, the grass planting ditch cannot be optimized, and only the scales of two rainwater gardens and the water storage module can be optimized. The values of the specific parameters are shown in the following table.
4) Optimizing results
By checking the result document, the optimal scheme of the optimization, the corresponding control quantity and the corresponding cost are obtained as follows:
optimizing results table
| Rainwater garden_9 | Rainwater garden_10 | Water storage module_11 | Totals to | |
| Area (m 2) | 110 | 120 | 50 | -- |
| Price (Yuan) | 38500 | 42000 | 84000 | 164500 |
| Control quantity (mm) | 84.5 | -- |
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and it should be noted that it will be apparent to those skilled in the art that several improvements and modifications can be made without departing from the technical principle of the present invention, and these improvements and modifications should also be considered as the scope of the present invention.
Claims (1)
1. A sponge city planning and design system, comprising:
the sponge facility determining module is used for selecting sponge facilities suitable for project use from the selectable facilities according to the design rules;
the sponge simulation model is used for obtaining a sponge simulation model simulation result through system calculation, site calculation and LID facility calculation based on the selected sponge facilities;
the sponge optimization evaluation model is used for carrying out optimization analysis on the sponge simulation model based on a discrete search algorithm by taking a combination of an optimization target and constraint conditions as an optimization condition to obtain an optimal design scheme set; the optimization targets comprise minimum construction investment, maximum runoff control targets, maximum surface runoff pollution control targets, minimum pollutant load, minimum flood peak flow and minimum operation maintenance cost; the constraint conditions comprise annual runoff total control rate, runoff pollution control rate, flood peak flow and four guiding indexes; the four guiding indexes comprise green roof proportion, sinking green land proportion, permeable pavement and impermeable water pad surface runoff control proportion;
the optimal scheme output module is used for outputting an optimal design scheme set in the form of an optimal analysis report form so as to enable a user to select a proper optimal scheme;
the selectable facilities comprise preset facilities and custom facilities, wherein the preset facilities comprise green roofs, blue roofs, biological detention facilities, permeable pavement, sinking greenbelts, permeation ponds, permeation wells, vegetation grass furrows, rainwater tanks and wetlands;
the preset facilities are obtained according to the planning and design of occupied areas, catchment areas, using conditions and terrain elements, and the custom facilities are obtained according to the planning and design of geographical positions and climates of the project places, the endowment of resource energy sources, the development level and scale, the technical maturity, regional requirements and economic and technical characteristic elements;
the design rule includes:
selecting sponge facilities according to the regional characteristics of the project; constructing a design scheme according to an optimization target and constraint conditions required by a user;
the sponge simulation model comprises a system calculation module, a site calculation module and an LID facility calculation module;
the system calculation module is used for calculating rainfall and evaporation in unit time, taking the rainfall and evaporation as input parameters of the site calculation module and the LID facility calculation module, and outputting calculation results of the site calculation module and the LID facility calculation module to a result data table of the system calculation module after the site calculation module and the LID facility calculation module are operated;
the site calculation module is used for calculating the production convergence condition of the permeable surface and the impermeable surface in the site and the corresponding pollutant accumulation and attenuation condition according to the rainfall and evaporation parameters provided by the system calculation module, counting the hydrologic water quality data of the LID facilities in the converged site, and outputting the counted results to a site result data table;
the LID facility calculation module is used for calculating the water quantity and water quality change condition of the LID facility according to the rainfall and evaporation parameters provided by the system calculation module and the yield convergence and pollutant accumulation parameters provided by the site calculation module, and outputting the result to the LID report data table;
the sponge optimization evaluation model comprises a cost calculation function module, a constraint function module, an optimization algorithm module, an evaluation module and a search group module, and is used for obtaining an optimal LID configuration scheme by taking a sponge simulation model as an evaluator of the sponge optimization evaluation model according to an optimization target and an LID cost function;
the cost calculation function module comprises a cost calculation function, also called an objective function;
the constraint function module comprises a constraint function or constraint condition;
the optimization algorithm module is used for optimizing search, and performing parameter calibration by adopting a discrete search algorithm;
the evaluation module is used for simulating the water quantity and the water quality of the LID facility design scheme constructed by a user by utilizing the constructed low-influence development facility water quantity and water quality calculation simulation model to obtain a simulated result, and counting the annual runoff total control rate, the flood peak flow rate and the runoff pollution control rate;
the searching group of the searching group module is a possible value space of three layers of LID number, LID type and LID specification;
wherein the objective function minimizes the overall cost of all LID facilities in the assessment scheme;
wherein: c (C) i Cost for the ith LID facility; c (C) all Total cost for all LID facilities; n is the number of LID facilities to be optimized;
performing a single LID facility cost calculation using the following standard LID facility cost function;
Cost＝(A a ×Aera i A )×(D a ×Depth i D )+LandCost×Area+FixCost
wherein: aa, ai are area coefficients; da, di is the depth coefficient; area and Depth are the Area and total Depth of the LID facility, respectively; land cost is the price per unit area; fixCost is other cost;
the constraint conditions comprise three main categories of annual runoff total control rate, runoff pollution control rate and flood peak flow; the annual runoff total control rate comprises total outflow, total infiltration, total accumulation and runoff control rate; the peak flood flow comprises a maximum outflow flow, an average outflow flow and a peak flood peak shaving rate; the runoff pollution control rate comprises total load of effluent pollutants, maximum effluent pollutant concentration and pollutant load reduction.
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| CN113449435B (en) * | 2021-07-22 | 2024-04-05 | 北京大学 | Multi-objective optimization method for sponge city engineering design |
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