A method for collaborative optimization design of gray-green facility systems in urban watersheds

Abstract

The present application relates to the technical field of rainwater and domestic sewage treatment, and in particular to a method for collaborative optimization design of gray-green facility systems in urban watersheds, wherein the method comprises: collecting basic information of gray-green facility systems in urban watersheds to be planned, and constructing a variety of climate change scenarios and social development uncertainty scenarios for the gray-green facility systems in urban watersheds to be planned during the construction planning period based on the basic information; constructing a water environment state transfer equation that can take into account the pollution control effects, climate change effects and social development uncertainties of various gray-green facility systems based on a water quality model of the urban watershed water system; according to the water environment state transfer equation, determining an optimal value function with water quality indicators and cost indicators as optimization targets, and using a preset heuristic algorithm to solve the optimal value and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system in the urban watershed to be planned corresponding to the optimal value.

Description

Technical Field

[0001] The present application relates to the technical field of rainwater and domestic sewage treatment, and in particular to a method for collaborative optimization design of gray-green facility systems in urban watersheds. Background Art

[0002] The urban grey-green infrastructure system includes grey infrastructure and green infrastructure. Grey infrastructure includes rainwater pipe networks and sewage pipe networks (both are collectively referred to as “pipe networks”), storage tanks, overflow treatment devices, sewage treatment plants, etc. Green infrastructure includes rainwater gardens, green roofs, permeable pavement, etc. The urban grey-green infrastructure system can reduce the interference of human activities in urban watersheds on the water environment. Specifically, in the absence of rainfall, domestic sewage is collected and treated through grey infrastructure; in rainfall conditions, rainwater is infiltrated, purified and reused through green infrastructure, and rainfall runoff and domestic sewage are collected and treated through grey infrastructure. The planning and design of the urban grey-green infrastructure system includes four elements: (1) selecting appropriate grey and green infrastructure, (2) determining the construction location of each type of facility, (3) determining the construction time of each type of facility, and (4) determining the construction scale of each type of facility.

[0003] The existing urban watershed grey-green facility system design methods generally first fix or simplify one or two of the four design elements listed above through certain methods, and then optimize the other elements. The functions achieved by the three background technologies listed below are all to optimize the remaining elements after fixing or simplifying one or two elements, but fail to achieve the coordinated optimization of the above elements. Summary of the invention

[0004] The present application provides a method for the coordinated optimization design of the gray-green facility system in an urban watershed, in order to solve the problems that the existing planning methods can only locally optimize factors such as the construction scale and construction location, fail to take water quality as the optimization target, and the existing planning methods lack comprehensive consideration of the uncertainties faced by system development.

[0005] The first aspect of the present application provides a method for collaborative optimization design of gray-green facility systems in urban watersheds, comprising the following steps: collecting basic information of gray-green facility systems in urban watersheds to be planned, and constructing a variety of climate change scenarios and social development uncertainty scenarios for the gray-green facility systems in urban watersheds to be planned during the construction planning period based on the basic information; constructing a water environment state transfer equation that can take into account the pollution control effects, climate change effects and social development uncertainties of various gray-green facility systems based on a water quality model of the urban watershed water system, wherein the water quality model of the urban watershed water system includes an urban module that simulates the urban treatment process by adopting a dynamic material flow analysis method, a river module constructed based on a series completely mixed reactor model CSTR, and a lake module composed of a lake mechanics model EFDC and an LSTM metamodel; according to the water environment state transfer equation, an optimal value function is determined with water quality indicators and cost indicators as optimization targets, and a preset heuristic algorithm is used to solve the optimal value and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system in the urban watershed to be planned corresponding to the optimal value;

[0006] The water environment state transfer state equation is:

[0007]

[0008] Among them, s t For the social development uncertainty scenario, a t is the climate change scenario, T is the water quality model of the urban watershed water system, C t is the concentration of each pollutant at time t, is the different concentrations of different pollutants at time t in scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, p i Building strategies for grey-green facilities.

[0009] Optionally, the input to the water environment state transition equation should include the construction strategy of gray-green facilities p i The construction method includes: generalizing the construction stages of the gray-green facility system in the urban watershed to be planned; defining the gray-green facility system of a specific location, scale and type that needs to be constructed in each construction stage as an alternative project, and generalizing the construction status of the alternative project in each construction stage as a decision variable, and obtaining the allowable decision set of all construction stages based on the decision variables of each construction stage; generalizing the sequence composed of the allowable decision sets of all construction stages into multiple gray-green facility construction strategies p i .

[0010] Optionally, the optimal value function is:

[0011]

[0012] Among them, f i (p i ) is to adopt the strategy of gray-green facility construction i The obtained indicator function value, F quality is the water quality index function, F cost is the cost indicator function,

[0013] Among them, the water quality index function F quality It is the average exceeding standard of multiple types of pollutants under various climate scenarios. The calculation method is:

[0014]

[0015] Among them, c j,t,a is the concentration of pollutant j at time t under climate change scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, n a is the total number of climate change scenarios for the urban watershed to be planned, STD j is the water quality standard of pollutant j, f quality,j is the average value of the excess proportion of pollutant index j under various climate change scenarios, w j is the weight of each pollutant, ∑w j =1,T f is the total length of the planning period;

[0016] The cost indicator function F cost for:

[0017] F cost =TCC+TMC;

[0018] TCC=∑ t CC(p i ,t) / (1+r) t ;

[0019] TMC=∑ t MC(p i ,t) / (1+r) t ;

[0020] Among them, TCC is the total construction cost of various types of gray-green infrastructure during the construction planning period, TMC is the total maintenance cost of various types of gray-green infrastructure during the construction planning period, CC(p i ,t) is the construction cost function that returns the total construction cost of the gray-green facility constructed in year t, MC(p i ,t) is the maintenance cost function that returns the total maintenance cost of all built gray-green facilities in year t, r is the discount rate, p i Building strategies for multiple grey-green facilities.

[0021] The second aspect of the present application provides a device for collaborative optimization design of gray-green facility systems in urban watersheds, including: a first construction module, which is used to collect basic information of gray-green facility systems in urban watersheds to be planned, and construct a variety of climate change scenarios and social development uncertainty scenarios for the gray-green facility systems in urban watersheds to be planned during the construction planning period based on the basic information; a second construction module, which is used to construct a water environment state transfer equation that can take into account the pollution control effects, climate change effects and social development uncertainties of various gray-green facility systems based on a water quality model of the urban watershed water system, wherein the water quality model of the urban watershed water system includes an urban module that simulates the urban treatment process by adopting a dynamic material flow analysis method, a river module constructed based on a series completely mixed reactor model CSTR, and a lake module composed of a lake mechanics model EFDC and an LSTM metamodel; an acquisition module, which is used to determine the optimal value function based on the water environment state transfer equation, with water quality indicators and cost indicators as optimization targets, and use a preset heuristic algorithm to solve and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system in the urban watershed to be planned corresponding to the optimal value;

[0022] The water environment state transfer state equation is:

[0023]

[0024] Among them, s t For the social development uncertainty scenario, a t is the climate change scenario, T is the water quality model of the urban watershed water system, C t is the concentration of each pollutant at time t, is the different concentrations of different pollutants at time t in scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, p i Building strategies for grey-green facilities.

[0025] Optionally, the second construction module is also used to: generalize the construction stages of the gray-green facility system in the urban watershed to be planned; define the gray-green facility system of the location, scale and type that needs to be constructed in each construction stage as alternative projects, and generalize the construction status of the alternative projects in each construction stage into decision variables, and obtain the allowable decision set for all construction stages based on the decision variables of each construction stage; generalize the sequence composed of the allowable decision sets of all construction stages into multiple gray-green facility construction strategies.

[0026] Optionally, the optimal value function is:

[0027]

[0028] Among them, f i (p i) is to adopt the strategy of gray-green facility construction i The obtained indicator function value, F quality is the water quality index function, F cost is the cost indicator function,

[0029] Among them, the water quality index function F quality It is the average exceeding standard of multiple types of pollutants under various climate scenarios. The calculation method is:

[0030]

[0031] Among them, c j,t,a is the concentration of pollutant j at time t under climate change scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, n a is the total number of climate change scenarios for the urban watershed to be planned, STD j is the water quality standard of pollutant j, f quality,j is the average value of the excess proportion of pollutant index j under various climate change scenarios, w j is the weight of each pollutant, ∑w j =1,T f is the total length of the planning period;

[0032] The cost indicator function F cost for:

[0033] F cost =TCC+TMC;

[0034] TCC=∑ t CC(p i , t) / (1+r) t ;

[0035] TMC=∑ t MC(p i , t) / (1+r) t ;

[0036] Among them, TCC is the total construction cost of various types of gray-green infrastructure during the construction planning period, TMC is the total maintenance cost of various types of gray-green infrastructure during the construction planning period, CC(p i ,t) is the construction cost function that returns the total construction cost of the gray-green facility constructed in year t, MC(p i ,t) is the maintenance cost function that returns the total maintenance cost of all built gray-green facilities in year t, r is the discount rate, p i Building strategies for multiple grey-green facilities.

[0037] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for collaborative optimization design of a gray-green facility system in an urban watershed as described in the above embodiment.

[0038] The fourth aspect of the present application provides a computer program product having a computer program stored thereon, which is executed by a processor to implement the method for collaborative optimization design of a gray-green facility system in an urban watershed as described in the above embodiment.

[0039] In the above implementation mode, basic information of the grey-green facility system of the urban watershed to be planned is collected, and based on the basic information, various climate change scenarios and social development uncertainty scenarios of the grey-green facility system of the urban watershed to be planned during the construction planning period are constructed; based on the water quality model of the urban watershed water system, a water environment state transfer equation is constructed that can consider the pollution control effects of various grey-green facility systems, climate change effects and social development uncertainties; according to the water environment state transfer equation, the optimal value function is determined with water quality indicators and cost indicators as optimization objectives, and a preset heuristic algorithm is used to solve the optimal value and the multi-stage, spatially heterogeneous construction strategy of the grey-green facility system of the urban watershed to be planned corresponding to the optimal value. As a result, it solves the problems that the existing planning methods can only locally optimize factors such as construction scale and construction location, fail to take water quality as the optimization target, and lack comprehensive consideration of the uncertainties faced by system development. It enables various types of grey-green infrastructure, facility construction locations, construction time, and construction scale to be coordinated and optimized under the same optimization framework. By providing a response relationship between pollutant emission reduction brought about by the grey-green facility system and the water quality improvement of the receiving water body, optimization with water quality as the target becomes possible. The method of incorporating uncertainty into the optimization framework ensures the long-term effectiveness of the grey-green facility system, improves the system's adaptability to the future, and ensures that the cost-effectiveness of the grey-green facility system is optimal throughout the planning period.

[0040] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0042] Figure 1 A flowchart of a method for collaborative optimization design of a gray-green facility system in an urban watershed provided according to an embodiment of the present application;

[0043] Figure 2It is a flowchart for implementing a method for collaborative optimization of gray-green infrastructure in urban watersheds according to one embodiment of the present application;

[0044] Figure 3 A schematic diagram showing a comparison between simulation results and measured results of lake water quality on a training set of a water quality model for an urban watershed water system according to an embodiment of the present application;

[0045] Figure 4 A schematic diagram showing a comparison between simulation results and measured results of lake water quality on a test set using a water quality model for an urban watershed water system according to an embodiment of the present application;

[0046] Figure 5 A schematic diagram of the generalization process of candidate projects, decisions, and strategies for the coordinated optimization of gray-green infrastructure in urban watersheds and their correlation according to an embodiment of the present application;

[0047] Figure 6 A schematic diagram showing comparison of index functions of Pareto solution sets at three urbanization development levels according to an embodiment of the present application;

[0048] Figure 7 A schematic diagram showing a special solution at a certain level of urbanization development according to an embodiment of the present application;

[0049] Figure 8 A schematic diagram of a device for collaborative optimization design of a gray-green facility system in an urban watershed according to an embodiment of the present application;

[0050] Fig. 9 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0052] The following describes the method for collaborative optimization design of gray-green facility systems in urban watersheds of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the existing planning methods mentioned in the above background technology can only locally optimize factors such as construction scale and construction location, fail to take water quality as the optimization target, and the existing planning methods lack comprehensive consideration of the uncertainty faced by system development, the present application provides a method for collaborative optimization design of gray-green facility systems in urban watersheds, in which basic information of gray-green facility systems in urban watersheds to be planned is collected, and based on the basic information, a variety of climate change scenarios and social development uncertainty scenarios of gray-green facility systems in urban watersheds to be planned during the construction planning period are constructed, based on the water quality model of the urban watershed water system, a water environment state transfer equation that can consider the pollution control effects, climate change effects and social development uncertainties of various gray-green facility systems is constructed, according to the water environment state transfer equation, the optimal value function is determined with water quality indicators and cost indicators as optimization targets, and a preset heuristic algorithm is used to solve the optimal value and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system in the urban watershed to be planned corresponding to the optimal value. As a result, it solves the problems that the existing planning methods can only locally optimize factors such as construction scale and construction location, fail to take water quality as the optimization target, and lack comprehensive consideration of the uncertainties faced by system development. It enables various types of grey-green infrastructure, facility construction locations, construction time, and construction scale to be coordinated and optimized under the same optimization framework. By providing a response relationship between pollutant emission reduction brought about by the grey-green facility system and the water quality improvement of the receiving water body, optimization with water quality as the target becomes possible. The method of incorporating uncertainty into the optimization framework ensures the long-term effectiveness of the grey-green facility system, improves the system's adaptability to the future, and ensures that the cost-effectiveness of the grey-green facility system is optimal throughout the planning period.

[0053] The related technology proposes an optimization design method for gray-green sponge facilities based on an urban stormwater model and the NSGA-II algorithm, and discloses a method for optimizing the scale of gray-green facilities given the type, construction time and construction location of the gray-green facilities; the related technology also proposes a study on the health assessment of urban small watershed ecosystems and the optimal configuration of blue-green infrastructure, and discloses a method for optimizing the scale of a variety of gray-green facilities given a construction time and construction location; the related technology discloses a method for collaboratively optimizing the layout location and scale of a variety of gray-green facilities given a construction time.

[0054] However, the existing optimization methods for urban watershed gray-green infrastructure systems have the following three significant deficiencies:

[0055] First, the existing planning methods generally optimize the location or scale of gray-green facilities, and have not yet achieved the coordinated optimization of multiple planning elements such as the type, location, time and scale of gray-green facilities. The essence of this is that the existing planning methods do not take into account the complex spatial and temporal correlations of various types of gray-green facilities. Underestimating the spatial correlation of projects or the impact of existing facilities on the construction of subsequent facilities may reduce the overall benefits of the gray-green project system with the same investment.

[0056] Second, the existing planning methods take runoff volume or runoff load as the target, and have not yet achieved the water quality of the receiving water bodies in the urban basin as the target. In other words, the existing planning methods do not consider the nonlinear response relationship between pollutant emissions and water quality changes. The lack of this nonlinear response relationship may result in the consequence that the emissions meet the standards but the water quality still fluctuates or even exceeds the standards.

[0057] Third, existing planning methods generally use robustness or elasticity indicators to quantitatively evaluate the performance of green-gray infrastructure under uncertain scenarios, but cannot determine the optimal construction time of facilities in combination with uncertain scenarios. Insufficient consideration of the uncertainties faced by the development of gray-green facility systems may cause the current optimal gray-green facility system to face the risk of being unable to adapt to the future development of the basin, thereby affecting the realization of water environment protection goals.

[0058] In response to the problem that existing planning methods can only locally optimize factors such as construction scale and construction location, the embodiment of the present application proposes a generalized method for the multi-objective optimization problem of gray-green facilities in urban watersheds, so that various types of gray-green infrastructure, facility construction locations, construction time, and construction scale can be collaboratively optimized under the same optimization framework. In response to the problem that existing research has failed to take water quality as the optimization target, the embodiment of the present application constructs a water quality simulation method for urban water systems, providing a response relationship between pollutant emission reductions brought about by the gray-green facility system and the improvement of the water quality of the receiving water body, making optimization with water quality as the target possible. In response to the problem that existing planning methods lack comprehensive consideration of the uncertainties faced by system development, the embodiment of the present application proposes an uncertainty scenario construction technology and a method for incorporating uncertainty into the optimization framework to ensure the long-term effectiveness of the gray-green facility system and improve the system's adaptability to the future.

[0059] Specifically, Figure 1 A flow chart of a method for collaborative optimization design of a gray-green facility system in an urban watershed provided in an embodiment of the present application.

[0060] like Figure 1 As shown in FIG. 1 , the method for collaborative optimization design of the urban watershed gray-green facility system includes the following steps:

[0061] In step S101, basic information of the gray-green facility system in the urban watershed to be planned is collected, and based on the basic information, various climate change scenarios and social development uncertainty scenarios are constructed during the construction planning period of the gray-green facility system in the urban watershed to be planned.

[0062] The embodiment of the present application takes the Caohai Basin in Kunming City, Yunnan Province (hereinafter referred to as the Caohai Basin) as the urban basin to be planned. The Caohai Basin is a typical urban basin with seven rivers, all of which flow into the lake (Caohai) downstream of the city. Caohai faces a serious problem of excessive nutrients. For this reason, a gray-green facility system of a certain scale is assumed, but the existing facilities cannot completely solve the problem of deterioration of Caohai water quality.

[0063] Specifically, Figure 2 As shown in the figure, the collection of basic information is a necessary link for the coordinated planning of gray-green infrastructure in urban watersheds. The basic information required for planning is divided into seven categories, including natural geographical information, meteorological data, and socio-economic data, totaling 14 types, as shown in Table 1. Table 1 is a list of basic data collection for watersheds. Among them, natural geographical information and drainage system data are mainly used for boundary demarcation between subsystems of land, rivers, and lakes in the watershed system and for zoning and generalization within each subsystem. Meteorological data, especially rainfall data, are important external inputs to the water environment system of the watershed. At the same time, historical meteorological data are also the basis for formulating scenarios for future meteorological data changes. Socio-economic data are mainly used to determine the emission intensity of human activities to the water environment system, and are also an important basis for formulating social development scenarios. Water environment status data are mainly used for calibration and verification of parameters of the integrated water quality model of the watershed. Existing water pollution control project data are used for the selection of alternative projects and the generalization of their benefits and costs. Existing relevant planning data are mainly used as references for the formulation of relevant information, data, and scenarios in this planning method.

[0064] Table 1

[0065]

[0066]

[0067] Furthermore, we construct a variety of climate change scenarios and social development uncertainty scenarios:

[0068] Since the gray-green infrastructure in urban watersheds has a long lifespan, gray-green facilities will continue to face threats from external and internal factors after they are built. If future uncertainties are not adequately considered during the planning process, the risk of failure of gray-green infrastructure will increase and have an adverse impact on future construction. Urbanization and climate change are the two main factors affecting the uncertainty of gray-green infrastructure. In the embodiments of the present application, by constructing three climate scenarios and three urbanization scenarios, the impact of two types of uncertainty on gray-green facilities in urban watersheds is characterized.

[0069] The climate change scenario is the daily forecast data of six meteorological variables during the planning period. The six meteorological variables are atmospheric pressure, temperature, relative humidity, precipitation, solar radiation and wind speed. The construction method is as follows: First, based on the three typical concentration pathway scenarios of RCP 2.6, RCP 4.5 and RCP 8.5 released by the Intergovernmental Panel on Climate Change (IPCC), the global climate model (GCM) is used to obtain the daily time series of the six meteorological variables in the planning period of the region. Then, based on the measured data of the planning area, the meteorological variables obtained by GCM are localized by the relative rate of change method (as shown in formula (1)).

[0070]

[0071] Where M G is the meteorological data predicted by GCM, M0 is the localized meteorological data, is the mean of the GCM predicted meteorological data, is the mean of the local measured data.

[0072] The social development uncertainty scenario is the annual forecast data of three socioeconomic factors during the planning period. The three socioeconomic factors are population, per capita water consumption and proportion of green space. The construction method is as follows: First, based on the existing socioeconomic data, determine the current levels of the three socioeconomic factors. Then, combined with relevant planning data, determine the values ​​of population, per capita water consumption and proportion of green space at the end of the planning under the three situations of rapid economic development, development at the current level, and slow development. Through linear difference, determine the population, per capita water consumption and proportion of green space for each year in the planning period. In the above three situations, the social development uncertainty scenarios composed of various socioeconomic factors are recorded as s1, s2 and s3 respectively.

[0073] In step S102, based on the water quality model of the urban watershed water system, a water environment state transfer equation is constructed that can take into account the pollution control effects of various gray-green facility systems, climate change effects and social development uncertainties. The water quality model of the urban watershed water system includes an urban module that simulates the urban treatment process by adopting a dynamic material flow analysis method, a river module constructed based on a series completely mixed reactor model CSTR, and a lake module composed of a lake mechanics model EFDC and a LSTM metamodel.

[0074] This step generates a water quality model for urban watershed water systems suitable for optimizing urban grey-green infrastructure, which can effectively describe the daily-scale impacts of pollutants generated in urban areas on receiving water bodies.

[0075] The model consists of three submodules: the city module uses a dynamic material flow analysis method to simulate urban-related processes, including runoff generation, sewage collection and sewage treatment. This module provides the amount of water discharged to the river and conventional water quality indicators (including COD, TN, TP, NH3-N) from each sub-catchment area; the river module uses a relatively mature series complete mixed reactor model (CSTR) to simulate the change process of river water quality. The river module can simulate the changes in water volume, conventional water quality indicators and dissolved oxygen; the lake module uses an LSTM metamodel to simulate the concentration of pollutants in the lake flowing from the river. First, under various extreme climate change and pollutant emission scenarios, the commonly used lake mechanics model EFDC is used to simulate the water quality changes of the lake, and the response relationship of the lake water quality to the input variables is obtained to form a discharge-concentration response relationship data set. Then, the LSTM metamodel is trained using this data set. The state variables contained in the lake EFDC model include algae, organic carbon, nitrogen, phosphorus and other categories, as well as 22 variables subdivided under the states of insoluble organic particles, soluble organic particles, dissolved organic state and inorganic state. The time accuracy of the three modules is daily. The comparison between the simulation results and measured results of the urban watershed water system water quality model on the lake water quality in the training set is shown in the figure below. Figure 3 As shown in the figure, the simulation results of the urban watershed water system water quality model on the test set for lake water quality are compared with the measured results. Figure 4 shown.

[0076] Module integration requires a conversion relationship between space and state variables. The spatial connectivity between sub-basins and rivers, and between rivers and lakes is determined by the adjacency matrix. Since the lake model requires more water quality indicators to be input, the conventional water quality indicators obtained by the river model need to be converted to obtain the input of the lake model. The embodiment of the present application should determine the conversion relationship table between the two modules through literature research and field monitoring, as shown in Table 2. Table 2 is the EFDC model water quality state variables and their conversion relationship with the river water quality state variables.

[0077] Table 2

[0078]

[0079]

[0080] Furthermore, in some embodiments, the input of the water environment state transfer equation includes the gray-green facility construction strategy p iThe construction method of this strategy includes: generalizing the construction stages of the gray-green facility system in the planned urban watershed; defining the gray-green facility system with a specific location, scale and type that needs to be built in each construction stage as an alternative project, and generalizing the construction status of the alternative project in each construction stage as a decision variable, and obtaining the allowable decision set of all construction stages based on the decision variables of each construction stage; generalizing the sequence composed of the allowable decision sets of all construction stages into multiple gray-green facility construction strategies p i .

[0081] Specific as Figure 5 As shown, first, the construction phase is generalized:

[0082] The construction phase of the generalized urban watershed gray-green facility system means that after a given urban watershed, its planning period is determined and the planning period is appropriately divided into several interrelated construction phases so that its state variables can be solved in a certain order. The phase variables are represented by k. That is:

[0083] k=1,2,…,n k ; (2)

[0084] Where n k is the maximum value of the stage variable k.

[0085] For example, if the planning period for gray-green infrastructure construction is the next 30 years and the decision-making period of the local government is 5 years, the planning period can be divided into 6 stages. That is, k = 1, 2, ..., 6, n k =6.

[0086] Furthermore, the decision variables are generalized to allow for the set of decisions:

[0087] The decision variables of the urban watershed gray-green facility system represent the type, scale and location of the gray-green facility system that needs to be built at a certain construction stage. In this method, the gray-green facility of a specific type, location and scale is defined as an alternative project f, and the variable recording the construction status of the alternative project at this stage is defined as x f,k If the current equipment construction project is x f , then x f =1, otherwise x f = 0. If d k represents the decision variable in the kth stage, then d k It is composed of alternative project x f,k The vector formed by

[0088]

[0089] Where n f is the total number of alternative projects.

[0090] At each stage, the types, scales and locations of feasible grey-green facility construction are restricted by factors such as the hydrology and land use conditions in the implementation area. Their possible values ​​are within a certain range, which is called the allowed decision set, denoted as D k , obviously there is d k ∈D k .

[0091] Further, the grey-green facility construction strategy is generalized to the set of permitted strategies:

[0092] The gray-green facility construction strategy is a sequence of decision functions composed of the decisions of each stage arranged in order, referred to as strategy, denoted by p i ,Right now

[0093]

[0094] In the problem of optimizing gray-green facilities in urban watersheds, there is a certain range of strategies available for selection, which is called the allowed strategy set and is represented by P. Obviously, there is p i ∈P. Finding the most effective strategy from the set of allowed strategies is called the optimal gray-green facility construction strategy. Figure 5 The paper shows the generalization process of construction phases, alternative projects, decision-making, and strategies for the coordinated optimization of gray-green infrastructure in urban watersheds, as well as the correlation between each process.

[0095] Further, the state is generalized and the water environment state transfer equation is constructed:

[0096] For the urban watershed gray-green facility system, the key state variable is the water quality change of sensitive water bodies, which is expressed by the vector C t Record the concentration of various pollutants at time t. This state is affected by four factors. The first type of influencing factors is the population in the basin and the underlying surface type, recorded as s t , provided by the social development scenario. The second type of influencing factor is meteorological conditions, denoted by a t , provided by climate change scenarios. The third type of influencing factor is the construction status of gray-green facilities, which is provided by the gray-green facility construction strategy. i supply.

[0097] The fourth type of influencing factor is the nonlinear response relationship between discharge and water quality at the next moment under the current water quality conditions, which is provided by the urban watershed water system model (denoted as T), as shown in formula (5).

[0098] In step S103, according to the water environment state transfer equation, the optimal value function is determined with water quality indicators and cost indicators as optimization targets, and the optimal value and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system in the planned urban watershed corresponding to the optimal value are solved by a preset heuristic algorithm; the water environment state transfer state equation is:

[0099]

[0100] Among them, s t For the social development uncertainty scenario, a t is the climate change scenario, T is the water quality model of the urban watershed water system, C t is the concentration of each pollutant at time t, is the different concentrations of different pollutants at time t in scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, p i Building strategies for grey-green facilities.

[0101] The optimal value function represents the process of the urban watershed system from the initial stage to the final stage of the planning period, and the optimal value of the index function obtained by the gray-green facility strategy. Specifically, the goal of gray-green facility construction is to protect the water environment quality, and the cost is the construction cost and maintenance cost. Therefore, the index function in the embodiment of the present application is the water quality index function and the cost index function.

[0102] Optionally, in some embodiments, the optimal value function is:

[0103]

[0104] Among them, f i (p i ) is to adopt the strategy of gray-green facility construction i The obtained indicator function value, F quality is the water quality index function, F cost is the cost indicator function,

[0105] The definition of the water quality index function is: for each type of pollutant during the entire planning period, take the average value of the excess proportion under the three meteorological scenarios, and perform a weighted average for different pollutants, denoted as F quality , as shown in formula (7), where STD j is the water quality standard of pollutant j, f quality,j is the average value of the excess proportion of pollutant index j under three meteorological scenarios, w j is the weight of each pollutant, which is determined by the importance of pollutants and the water quality index function F quality for:

[0106]

[0107] Among them, c j,t,a is the concentration of pollutant j at time t under climate change scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, n ais the total number of climate change scenarios for the urban watershed to be planned, STD j is the water quality standard of pollutant j, f quality,j is the average value of the excess proportion of pollutant index j under various climate change scenarios, w j is the weight of each pollutant, ∑w j =1,T f is the total length of the planning period;

[0108] Cost index function F cost for:

[0109] F cost =TCC+TMC;

[0110] TCC=∑ t CC(p i , t) / (1+r) t ; (8)

[0111] TMC=∑ t MC(p i , t) / (1+r) t ;

[0112] Among them, TCC is the total construction cost of various types of gray-green infrastructure during the construction planning period, TMC is the total maintenance cost of various types of gray-green infrastructure during the construction planning period, CC(p i ,t) is the construction cost function that returns the total construction cost of the gray-green facility constructed in year t, MC(p i ,t) is the maintenance cost function that returns the total maintenance cost of all built gray-green facilities in year t, r is the discount rate, p i Building strategies for multiple grey-green facilities.

[0113] In formula (8), CC(p i ,t) is the sum of the construction costs of various alternative gray-green facility units constructed in the tth year, and its calculation method is shown in formula (9). Among them, the construction costs of various alternative gray-green basic units refer to the relevant information released by the housing and urban-rural construction department. First, the number of products and services required for the construction process of various alternative facilities is determined, and the unit price of products and services is combined to finally determine the construction cost of such facilities, as shown in formula (10). Taking various LID facilities as an example, their construction costs should refer to standard documents such as construction guidelines. Their construction costs are the sum of material costs, labor costs, machinery costs, measures costs, equipment purchase costs, technical consulting fees, etc.

[0114] CC(p i ,t)=∑ p (x p ×C p );(9)

[0115] Cp =a p ∑ kx (q kx ×p kx );(10)

[0116] In the formula, x p is a variable indicating the construction status of the alternative project. If the alternative project p is constructed during the current period, it is 1, otherwise it is 0. p is the unit price of the alternative project, a p is the scale of the alternative project, kx is a variable representing the various products and services required during the construction phase, and q kx and p kx They are the usage and unit price under the unit scale of kx respectively.

[0117] MC(p i ,t) is the sum of the maintenance costs of all types of alternative gray-green facility units constructed in year t. The calculation method of maintenance cost is similar to that of construction cost, as shown in formula (11). Among them, in order to determine the maintenance cost of alternative gray-green facility units, the types of daily maintenance projects of such gray-green facilities should be determined first, and the maintenance cost should be determined by referring to the standards and guidelines corresponding to the specific maintenance projects. Taking gray facilities such as drainage pipe networks and storage tanks as an example, their daily maintenance includes damage repair, dredging and energy supply. The price of damage repair, dredging and other activities can be determined by reference materials, and the energy supply needs to be determined in combination with the number, power, operation time and energy price of energy-consuming equipment such as mud pumps and submersible sewage pumps in the pipe network system.

[0118] MC(p i ,t)=∑ p (x p ×M p );(11)

[0119] M p =a p ∑ lx (q lx ×p lx ); (12)

[0120] In the formula, x p is a variable indicating the construction status of the alternative project. If the alternative project p is being constructed during the current period, it is 1, otherwise it is 0. p is the unit price of the alternative project, a p is the scale of the alternative project, lx is a variable representing the types of products and services required in the operation phase, and q lx and p lx They are the usage and unit price under the unit scale of lx respectively.

[0121] Further, the optimization constraints are generalized:

[0122] Considering that the construction cost of urban watershed grey-green facilities is high, this method allows facilities to be expanded in size, but does not allow facilities to be removed, because the latter means suboptimal planning and resource inefficiency. Therefore, this method sets a constraint that if different scales of the same facility are scheduled to be constructed at the same location, the construction time of the larger scale facility should be later than the construction time of the smaller scale facility. That is, if the alternative project and alternative projects The type is the same as the construction location, and x f1,k1 The size is greater than x f2,k2 , then:

[0123] k1 <k2。

[0124] Furthermore, the embodiment of the present application uses a preset heuristic algorithm (such as the NSGA-II algorithm) to solve the obtained multi-objective optimization problem of gray-green facilities in the urban watershed. Among them, the designed optimization algorithm takes into account climate uncertainty and social development uncertainty. By running the optimization algorithm separately under different social development scenarios, gray-green facility construction strategies suitable for different development scenarios are obtained. When the optimization algorithm is run under a specific development scenario, the impact of climate uncertainty is reflected through the optimal value function. That is, using the NSGA-II algorithm, in the allowed strategy set, the optimal value function and the multi-stage, spatially heterogeneous construction strategy corresponding to the optimal value function are solved. The embodiment of the present application converts the obtained multiple gray-green facility construction strategies into a genotype individual, and converts the obtained index function into the fitness of a genotype individual. The NSGA-II algorithm expresses the multi-objective optimization problem as a survival of the fittest process of a certain genotype individual. Through genetic operations such as selection, crossover, and mutation according to its adaptability, chromosomes with low adaptability are eliminated and chromosomes with high adaptability are retained. This is repeated, and the total number of iterations reaches N. gen , it is advisable to stop the algorithm and select the Pareto optimal individuals in the population to form a Pareto set. The recommended values ​​of the control parameters of the NSGA-II algorithm are shown in Table 3.

[0125] Because genetic operations such as parent selection and crossover mutation in genetic algorithms are random, the results obtained from multiple optimizations of the same socioeconomic development scenario will be biased. In order to reduce this random error and make the final frontier more representative, each scenario is optimized three times, and the three optimization results are combined to take the Pareto optimal solution as the final optimization result, as shown in Figure 1. Figure 6 As shown in , the Pareto set generated in this step can provide a variety of gray-green facility construction plans, each of which contains the construction time, location and scale information of various gray-green facilities, such as Figure 7 As shown, Figure 7The figure shows the construction details of the gray-green facility construction plan corresponding to a special solution of urbanization scenario s1. C1-C28 represent the 28 sub-basins in the embodiment, W1-W4 represent the sewage treatment plants in the embodiment, and 0-30 represent the planning period, which is divided into six construction periods. The color of the color block in the figure represents different construction scales. Therefore, the above figure contains the construction location, time and scale information of 10 types of gray-green facilities.

[0126] Table 3

[0127] Control parameters meaning Recommended value range <![CDATA[N pop ]]> Population size, that is, the number of solutions to search for in each generation 100-200 <![CDATA[N gen ]]> Maximum number of iterations ≥200 α Crossover probability 0.5-0.9 β Mutation probability 0.001-0.1

[0128] In summary, the beneficial effects of the embodiments of the present application are:

[0129] (1) It can provide a variety of gray-green facility design schemes with different costs and water quality effects for urban watersheds, and each scheme contains information on four dimensions: type of gray-green facility, construction location, construction time, and construction scale;

[0130] (2) For the first time, the coordinated optimization design of multiple planning elements of the urban watershed gray-green facility system was realized. Specifically, the embodiment of the present application incorporates the four construction elements of gray-green facility type, location, scale and time into the optimization variables, so that the optimization process can reflect the complex spatial and temporal correlation of various facilities. Therefore, the construction plan optimized by the embodiment of the present application has a better cost-effectiveness ratio during the entire planning period.

[0131] (3) The embodiment of the present application realizes the optimization design of gray-green facilities with water quality as the optimization target for the first time. Specifically, the embodiment of the present application calculates the objective function by calling a comprehensive water quality model of the urban watershed water system, so that the nonlinear response relationship between pollution reduction and water quality improvement goals is included in the optimization process. Therefore, each construction plan obtained in the embodiment of the present application is the best plan for the water quality of the receiving water body in the basin at the current cost. Compared with the existing method, the risk of meeting the pollution load reduction standard but exceeding the water quality standard is lower.

[0132] (4) The embodiment of the present application comprehensively considers the two most important uncertainties faced by gray-green facilities. Specifically, in the multi-objective optimization method obtained in the embodiment of the present application, the water quality target is the average water quality of the receiving water body under different climate change scenarios to reflect the impact of climate change on the urban water system; the social development scenario is the optimization boundary, and multi-objective optimization is performed for each social scenario to reflect the impact of changes in factors such as population and land use on the effects of urban gray-green facilities. Therefore, the gray-green facility construction plan obtained in the embodiment of the present application has a certain adaptability to climate change and social development changes.

[0133] According to the method for collaborative optimization design of gray-green facility systems in urban watersheds proposed in the embodiments of the present application, basic information of gray-green facility systems in urban watersheds to be planned is collected, and based on the basic information, various climate change scenarios and social development uncertainty scenarios of the gray-green facility systems in urban watersheds to be planned during the construction planning period are constructed; based on the water quality model of the urban watershed water system, a water environment state transfer equation is constructed that can take into account the pollution control effects, climate change effects and social development uncertainties of various gray-green facility systems; based on the water environment state transfer equation, the optimal value function is determined with water quality indicators and cost indicators as optimization objectives, and a preset heuristic algorithm is used to solve the optimal value and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system in the urban watershed to be planned corresponding to the optimal value. As a result, it solves the problems that the existing planning methods can only locally optimize factors such as construction scale and construction location, fail to take water quality as the optimization target, and lack comprehensive consideration of the uncertainties faced by system development. It enables various types of grey-green infrastructure, facility construction locations, construction time, and construction scale to be coordinated and optimized under the same optimization framework. By providing a response relationship between pollutant emission reduction brought about by the grey-green facility system and the water quality improvement of the receiving water body, optimization with water quality as the target becomes possible. The method of incorporating uncertainty into the optimization framework ensures the long-term effectiveness of the grey-green facility system, improves the system's adaptability to the future, and ensures that the cost-effectiveness of the grey-green facility system is optimal throughout the planning period.

[0134] Next, a device for collaborative optimization design of a gray-green facility system in an urban watershed proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0135] Figure 8 It is a block diagram of a device for collaborative optimization design of a gray-green facility system in an urban watershed according to an embodiment of the present application.

[0136] like Figure 8 As shown, the device 10 for collaborative optimization design of the urban watershed gray-green facility system includes: a first construction module 100, a second construction module 200 and an acquisition module 300.

[0137] Among them, the first construction module 100 is used to collect basic information of the gray-green facility system of the urban watershed to be planned, and construct various climate change scenarios and social development uncertainty scenarios of the gray-green facility system of the urban watershed to be planned during the construction planning period based on the basic information; the second construction module 200 is used to construct a water environment state transfer equation that can take into account the pollution control effects, climate change effects and social development uncertainties of various gray-green facility systems based on the water quality model of the urban watershed water system, wherein the water quality model of the urban watershed water system includes an urban module that simulates the urban treatment process by adopting a dynamic material flow analysis method, a river module constructed based on a series completely mixed reactor model CSTR, and a lake module composed of a lake mechanics model EFDC and an LSTM metamodel; the acquisition module 300 is used to determine the optimal value function based on the water environment state transfer equation, taking water quality indicators and cost indicators as optimization targets, and using a preset heuristic algorithm to solve and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system of the urban watershed to be planned corresponding to the optimal value;

[0138] The state equation of water environment state transfer is:

[0139]

[0140] Among them, s t For the social development uncertainty scenario, a t is the climate change scenario, T is the water quality model of the urban watershed water system, C t is the concentration of each pollutant at time t, is the different concentrations of different pollutants at time t in scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, p i Building strategies for grey-green facilities.

[0141] Optionally, in some embodiments, the second construction module 200 is further used to: generalize the construction stages of the gray-green facility system in the urban watershed to be planned; define the gray-green facility system of a specific location, scale and type that needs to be constructed in each construction stage as an alternative project, and generalize the construction status of the alternative project in each construction stage into a decision variable, and obtain the allowable decision set for all construction stages based on the decision variables of each construction stage; generalize the sequence composed of the allowable decision sets of all construction stages into multiple gray-green facility construction strategies.

[0142] Optionally, in some embodiments, the optimal value function is:

[0143]

[0144] Among them, f i (p i ) is to adopt the strategy of gray-green facility construction iThe obtained indicator function value, F quality is the water quality index function, F cost is the cost indicator function,

[0145] Among them, the water quality index function F quality It is the average exceeding standard of multiple types of pollutants under various climate scenarios. The calculation method is:

[0146]

[0147] Among them, c j,t,a is the concentration of pollutant j at time t under climate change scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, n a is the total number of climate change scenarios for the urban watershed to be planned, STD j is the water quality standard of pollutant j, f quality,j is the average value of the excess proportion of pollutant index j under various climate change scenarios, w j is the weight of each pollutant, ∑w j =1,T f is the total length of the planning period;

[0148] Cost index function F cost for:

[0149] F cost =TCC+TMC;

[0150] TCC=∑ t CC(p i , t) / (1+r) t ;

[0151] TMC=∑ t MC(p i , t) / (1+r) t ;

[0152] Among them, TCC is the total construction cost of various types of gray-green infrastructure during the construction planning period, TMC is the total maintenance cost of various types of gray-green infrastructure during the construction planning period, CC(p i ,t) is the construction cost function that returns the total construction cost of the gray-green facility constructed in year t, MC(p i ,t) is the maintenance cost function that returns the total maintenance cost of all built gray-green facilities in year t, r is the discount rate, p i Building strategies for multiple grey-green facilities.

[0153] It should be noted that the aforementioned explanation of the method embodiment of the collaborative optimization design of the gray-green facility system in an urban watershed is also applicable to the device for collaborative optimization design of the gray-green facility system in an urban watershed of this embodiment, and will not be repeated here.

[0154] According to the device for collaborative optimization design of gray-green facility systems in urban watersheds proposed in the embodiment of the present application, basic information of gray-green facility systems in urban watersheds to be planned is collected, and based on the basic information, various climate change scenarios and social development uncertainty scenarios of the gray-green facility systems in urban watersheds to be planned during the construction planning period are constructed. Based on the water quality model of the urban watershed water system, a water environment state transfer equation is constructed that can consider the pollution control effects, climate change effects and social development uncertainties of various gray-green facility systems. According to the water environment state transfer equation, the optimal value function is determined with water quality indicators and cost indicators as optimization objectives, and a preset heuristic algorithm is used to solve the optimal value and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system in the urban watershed to be planned corresponding to the optimal value. As a result, it solves the problems that the existing planning methods can only locally optimize factors such as construction scale and construction location, fail to take water quality as the optimization target, and lack comprehensive consideration of the uncertainties faced by system development. It enables various types of grey-green infrastructure, facility construction locations, construction time, and construction scale to be coordinated and optimized under the same optimization framework. By providing a response relationship between pollutant emission reduction brought about by the grey-green facility system and the water quality improvement of the receiving water body, optimization with water quality as the target becomes possible. The method of incorporating uncertainty into the optimization framework ensures the long-term effectiveness of the grey-green facility system, improves the system's adaptability to the future, and ensures that the cost-effectiveness of the grey-green facility system is optimal throughout the planning period.

[0155] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0156] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0157] When the processor 902 executes the program, the method for collaborative optimization design of the urban watershed gray-green facility system provided in the above embodiment is implemented.

[0158] Furthermore, the electronic device further comprises:

[0159] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0160] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0161] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0162] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0163] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0164] The processor 902 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0165] An embodiment of the present application also provides a computer program product having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for collaborative optimization design of gray-green facility systems in urban watersheds.

[0166] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0167] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0168] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0169] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer program product for use with an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer program product" can be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer program products (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). Furthermore, the computer program product may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example by optically scanning the paper or other medium and then editing, interpreting or, if necessary, processing it in another suitable manner, and then storing it in a computer memory.

[0170] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0171] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer program product, which, when executed, includes one or a combination of the steps of the method embodiment.

[0172] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer program product.

[0173] The computer program product mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for collaborative optimization design of gray-green facility systems in urban watersheds, characterized in that: The following steps are involved: Collect basic information of the gray-green facility system in the urban watershed to be planned, and construct multiple climate change scenarios and social development uncertainty scenarios of the gray-green facility system in the urban watershed to be planned during the construction planning period based on the basic information; Based on the water quality model of the urban watershed water system, a water environment state transfer equation is constructed that can take into account the pollution control effects of various gray-green facility systems, climate change effects and social development uncertainties. The water quality model of the urban watershed water system includes an urban module that simulates the urban treatment process by adopting a dynamic material flow analysis method, a river module constructed based on a series complete mixing reactor model CSTR, and a lake module composed of a lake mechanics model EFDC and a LSTM metamodel. According to the water environment state transfer equation, the optimal value function is determined with water quality indicators and cost indicators as optimization targets, and a preset heuristic algorithm is used to solve the optimal value and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system of the urban watershed to be planned corresponding to the optimal value; The water environment state transfer state equation is: Among them, s t For the social development uncertainty scenario, a t is the climate change scenario, T is the water quality model of the urban watershed water system, C t is the concentration of each pollutant at time t, is the different concentrations of different pollutants at time t in scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, p i Building strategies for grey-green facilities.

2. The method according to claim 1, characterized in that The optimal value function is: Among them, f i (p i ) is to adopt the strategy of gray-green facility construction i The obtained indicator function value, F quality is the water quality index function, F cost is the cost indicator function, Among them, the water quality index function F quality It is the average exceeding standard of various types of pollutants under various climate scenarios, and its calculation method is: Among them, C j，t，a is the concentration of pollutant j at time t under climate change scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, n a is the total number of climate change scenarios for the urban watershed to be planned, STD j is the water quality standard of pollutant j, f quality,j is the average value of the excess proportion of pollutant index j under various climate change scenarios, w j is the weight of each pollutant, ∑w j =1,T f is the total length of the planning period; The cost indicator function F cost for: F cost =TCC+TMC; TCC=∑ t CC(p i ,t) / (1+r) t ; TMC=∑ t MC(p i ,t) / (1+r) t ; Among them, TCC is the total construction cost of various types of gray-green infrastructure during the construction planning period, TMC is the total maintenance cost of various types of gray-green infrastructure during the construction planning period, CC(p i , t) is the construction cost function that returns the total construction cost of the gray-green facility constructed in year t, MC(p i , t) is the maintenance cost function that returns the total maintenance cost of all built gray-green facilities in year t, r is the discount rate, p i Building strategies for multiple grey-green facilities.

3. The method according to claim 2, characterized in that The input of the water environment state transfer equation includes the gray-green facility construction strategy p i , the construction method of this strategy includes: Outline the construction phases of the grey-green facility system in the urban watershed to be planned; The gray-green facility systems that need to be built in specific locations, scales and types in each construction phase are defined as alternative projects, and the construction status of the alternative projects in each construction phase is generalized as decision variables. Based on the decision variables of each construction phase, the set of allowable decisions for all construction phases is obtained. The sequence of permissible decision sets of all construction phases is generalized into multiple gray-green facility construction strategies p i .

4. A device for collaborative optimization design of gray-green facility systems in urban watersheds, characterized in that: The device includes: The first construction module is used to collect basic information of the gray-green facility system in the urban watershed to be planned, and construct multiple climate change scenarios and social development uncertainty scenarios of the gray-green facility system in the urban watershed to be planned during the construction planning period based on the basic information; The second construction module is used to construct a water environment state transfer equation based on the water quality model of the urban watershed water system, which can take into account the pollution control effects of various gray-green facility systems, climate change effects and social development uncertainties. The water quality model of the urban watershed water system includes an urban module that simulates the urban treatment process by adopting a dynamic material flow analysis method, a river module constructed based on a series complete mixing reactor model CSTR, and a lake module composed of a lake mechanics model EFDC and a LSTM metamodel; An acquisition module is used to determine the optimal value function according to the water environment state transfer equation, taking the water quality index and the cost index as the optimization target, and using a preset heuristic algorithm to solve and the multi-stage, spatially heterogeneous construction strategy of the gray-green facility system of the urban watershed to be planned corresponding to the optimal value; The water environment state transfer state equation is: Among them, s t For the social development uncertainty scenario, a t is the climate change scenario, T is the water quality model of the urban watershed water system, C t is the concentration of each pollutant at time t, is the different concentrations of different pollutants at time t in scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, p i Building strategies for grey-green facilities.

5. The device according to claim 4, characterized in that The optimal value function is: Among them, f i (p i ) is to adopt the strategy of gray-green facility construction i The obtained indicator function value, F quality is the water quality index function, F cost is the cost indicator function, Among them, the water quality index function F quality It is the average exceeding standard of multiple types of pollutants under various climate scenarios. The calculation method is: Among them, c j，t，a is the concentration of pollutant j at time t under climate change scenario a, n j is the total number of pollutant types of concern in the urban watershed to be planned, n a is the total number of climate change scenarios for the urban watershed to be planned, STD j is the water quality standard of pollutant j, f quality，j is the average value of the excess proportion of pollutant index j under various climate change scenarios, w j is the weight of each pollutant, ∑w j =1,T f is the total length of the planning period; The cost indicator function F cost for: F cost =TCC+TMC; TCC=∑ t CC(p i ,t) / (1+r) t ; TMC=∑ t MC(p i ,t) / (1+r) t ; Among them, TCC is the total construction cost of various types of gray-green infrastructure during the construction planning period, TMC is the total maintenance cost of various types of gray-green infrastructure during the construction planning period, CC(p i , t) is the construction cost function that returns the total construction cost of the gray-green facility constructed in year t, MC(p i , t) is the maintenance cost function that returns the total maintenance cost of all built gray-green facilities in year t, r is the discount rate, p i Building strategies for multiple grey-green facilities.

6. The device according to claim 5, characterized in that The second building block is further used for: Outline the construction phases of the grey-green facility system in the urban watershed to be planned; The gray-green facility systems that need to be built in specific locations, scales and types in each construction phase are defined as alternative projects, and the construction status of the alternative projects in each construction phase is generalized as decision variables. Based on the decision variables of each construction phase, the set of allowable decisions for all construction phases is obtained. The sequences composed of the permutations of the allowed decision sets of all the construction phases are generalized into a plurality of gray-green facility construction strategies.

7. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for collaborative optimization design of gray-green facility systems in urban watersheds as described in any one of claims 1 to 3.

8. A computer program product, wherein the computer program product stores a computer program, characterized in that: When the program is executed by a processor, it implements the method for collaborative optimization design of urban watershed gray-green facility systems as described in any one of claims 1-3.

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