CN106600025B - Multi-level urban sewage regeneration and reuse configuration data dynamic processing method based on multi-target hybrid genetic algorithm - Google Patents
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Abstract
The invention discloses a multi-level urban sewage regeneration and reuse configuration data dynamic processing method based on a multi-target hybrid genetic algorithm, which comprises the following steps: 1. acquiring basic information data of a multi-level water resource relationship, and establishing a multi-level data configuration relationship between a city centralized sewage treatment plant and a centralized reclaimed water plant; 2. acquiring data information such as basic information data of a city centralized sewage treatment plant and a centralized water reclamation plant; 3. establishing a multi-target data configuration model under the constraint conditions of considering water balance, water supply capacity, water delivery capacity, water demand capacity and risk according to the basic information data, wherein the target function is a multi-target function established by maximizing the total water resource utilization amount, maximizing the water resource regeneration and reuse benefit and minimizing the per-son water shortage of a subarea; 4. and solving to obtain data configuration schemes under different scenes by adopting a multi-objective hybrid genetic algorithm-based multi-level urban sewage recycling configuration data dynamic processing method. The invention meets the configuration requirement of urban multi-target multi-level sewage regeneration and reuse data.
Description
Technical Field
The invention relates to the field of urban centralized sewage regeneration and reuse, relates to a multilevel urban sewage data dynamic processing method, and particularly relates to a multilevel urban sewage regeneration and reuse configuration data dynamic processing method based on a multi-target hybrid genetic algorithm.
Technical Field
The urban centralized sewage regeneration and reuse relates to a complex data processing problem relating to a plurality of spatially dispersed water sources, a plurality of water resource types and a plurality of reuse ways, firstly, the tail water of the water outlet after the treatment of the urban centralized sewage plant can be directly used as the water source of the centralized sewage plant, a long-distance allocation and reuse way also exists in cities with water resource shortage, and then, the water outlet of the urban centralized sewage plant can be reused in the categories of urban miscellaneous use, industry, landscape, agriculture and the like, so that the urban centralized sewage regeneration and reuse relates to multilevel allocation data and the relation among the data, and the reuse way and allocation quantity decision problem can be finally generalized to the control problem of a multi-constraint multistage dynamic, long-term and complex linear system. Especially considering the particularity of the nature of the sewage source, the benefit decision between the ecological environment, the social and economic investment influence possibly caused in the recycling process of the sewage source further increases the complexity of the optimization solution of the sewage source. The existing centralized sewage recycling data configuration method has less research, mostly focuses on reservoir water resource data processing and configuration problems, the target solved by the problem is transited from single factor to multi-factor collaborative optimization, the solution of the multi-target optimization scheduling problem has a traditional algorithm and various bionic intelligent algorithms, the traditional algorithm can be solved by endowing each target with weight to form a single target when solving the multi-target problem, and only one solution is finally generated, so that the method is more suitable for the final decision problem; researchers use the multi-application intelligent algorithm to solve the collaborative optimization of the multi-target problem through the bionic principle, the multi-application intelligent algorithm typically comprises a genetic algorithm, an ant colony algorithm, a particle swarm algorithm and the like, and the intelligent algorithm has the characteristic of good overall optimization effect when solving the multi-target optimization problem.
A Genetic Algorithm (Genetic Algorithm) is proposed by John H.Holland in 1975, is an Algorithm based on natural selection and Genetic theory, simulates the biological evolution process in the nature, adopts an artificial evolution mode to carry out random optimization search on a target space, regards a feasible solution in a problem domain as an individual or a chromosome of a population, codes each individual in a symbol string form, simulates the Genetic selection of Darwin and the biological evolution process of natural elimination, and repeatedly carries out operations (heredity, cross and variation) based on genetics on the population, thereby achieving the purpose of efficient global optimization. The improved genetic algorithm developed gradually improves the problems of unsatisfactory global optimization and the like of the original algorithm in solving the multi-target problem by applying a dynamic archiving method, and is a hotspot algorithm applied to the field of water resource multi-target optimization configuration. However, the genetic algorithm still has the problems of slow global convergence and possible non-global optimal solution when solving the multi-target multi-constraint problem. And because the genetic algorithm can generate more than one solution, the decision maker is not favorable for providing a specific scheme for urban centralized sewage regeneration and reuse in the planning year. However, no relevant documents exist at present in the aspects that a genetic algorithm solves the linear programming problem of multi-target multi-constraint urban centralized sewage recycling configuration and reduces the final solution set range to provide references for decision making.
Disclosure of Invention
The invention aims to solve the technical problem that aiming at the multi-target multi-constraint linear control problem of urban centralized sewage multi-level recycling, a multi-target function and a related constraint model are established, a hybrid genetic algorithm is introduced to solve the model and pre-screen a solution set, a multi-level urban sewage recycling configuration data dynamic processing method based on the multi-target hybrid genetic algorithm is provided, multi-level long-term optimization control of urban centralized sewage recycling is realized, and objective basis is provided for urban sewage recycling configuration.
Specifically, the technical problem to be solved by the present invention is further: the method takes multi-level multi-planning-year urban sewage reuse of the effluent tail water of the centralized sewage treatment plant (going to the centralized reclaimed water plant or further remotely allocated) and the effluent of the centralized reclaimed water plant (going to the urban area for each reclaimed reuse purpose) as a dynamic integral system to analyze water quantity data and analyze dynamic relation among data, and provides objective data basis for further decision of sewage reuse. Firstly, a multi-objective function and a related constraint linear model are established on the basis of water quantity balance and water resource cost, then a mixed improved genetic algorithm based on interior points is introduced to solve the established model, the Pareto solution set is further clustered and target values are pre-screened, and finally urban centralized sewage reuse scheme data are obtained, so that the problem that existing centralized sewage reuse is needed for supply and urban level reuse design lacks systematicness is solved.
In order to solve the technical problems, the invention provides a sewage regeneration and reuse data processing method based on a multi-target hybrid genetic algorithm, which comprises the following steps:
acquiring basic information data of a multi-level water resource relationship, and establishing a multi-level data configuration relationship between a city centralized sewage treatment plant and a centralized reclaimed water plant;
the method comprises the steps of obtaining basic information data of a city centralized sewage treatment plant and a centralized reclaimed water plant, obtaining supply and demand prediction data of recycling ways of tail water and reclaimed water, and obtaining water distribution and transportation data and city development data. Preferably, the following can be used:
acquiring the number data and the space of each centralized sewage treatment plant by taking each subarea as a unitPosition distribution data information, design processing capability data xiWater quality of effluent tail water and reuse purpose information data x of effluent tail waterwAcquiring the data information of the number and spatial position distribution of the centralized regeneration water plant and the data x of the design processing capacityi’Water inlet source information, water quality of outlet regenerated water and reuse purpose information data xjAcquiring municipal delivery and distribution capacity data and cost coefficients c, and acquiring information such as socioeconomic development data and bearing population data b of each subarea;
establishing a multi-target data configuration model under the constraint conditions of considering water balance, water supply capacity, water delivery capacity, water demand capacity and risk according to the information data, wherein the target function is a multi-target function established by maximizing the total water resource utilization amount, maximizing the water resource regeneration and reuse benefit and minimizing the per-son water shortage in a subarea;
and solving to obtain data configuration schemes under different scenes by adopting a multi-objective hybrid genetic algorithm-based multi-level urban sewage recycling configuration data dynamic processing method.
The implementation of the invention has the following beneficial effects:
(1) the requirement of configuring multi-level regeneration and reuse data of urban centralized sewage is met;
(2) the initial population of the genetic algorithm is generated by utilizing the traditional interior point algorithm, the traversability, randomness and diversity of the initial population can be improved, and the problem that the genetic algorithm falls into local optimization due to poor random initial population quality and concentration in certain local areas under the multi-constraint multi-target condition can be avoided.
(3) The genetic algorithm adopts an external archive set method of a dynamic update mechanism to further ensure non-inferior solution diversity and accelerate global convergence.
(4) Based on the dynamic continuous water quantity allocation principle, the clustering method and the target value screening method are adopted to carry out decision pre-screening treatment on the genetic algorithm solution set, so that the long-term allocated water quantity stability of a single water plant and the long-term running effectiveness of the water plant are ensured.
(5) The target value-based scenario decision pre-screening method can further select different optimal configuration schemes from a perennial Pareto optimal solution set based on dynamic continuous water allocation, and effectively improves the operation rate and the scheme execution efficiency of the data configuration schemes.
Drawings
FIG. 1 is a flow chart of a multi-level urban sewage recycling configuration dynamic treatment method based on a multi-objective hybrid genetic algorithm;
FIG. 2 is a flow chart of a multi-objective hybrid genetic algorithm;
FIG. 3 is a flow chart of decision pre-screening of a Pareto optimal solution set of a multi-objective hybrid genetic algorithm.
Detailed Description
The present invention will be further described with reference to the accompanying drawings. The described embodiments are only some embodiments of the invention, not all embodiments.
The invention provides a multi-target multi-constraint linear long-term data configuration problem of multi-level regeneration and reuse of urban centralized sewage, and provides a multi-level urban sewage reuse data dynamic processing method based on a multi-target hybrid genetic algorithm by establishing a multi-target function and a related constraint model.
FIG. 1 is a flow chart of a dynamic processing method for multi-level urban sewage recycling configuration data based on a multi-objective hybrid genetic algorithm, as shown in FIG. 1, comprising the steps of:
step 1, acquiring basic information data of a multi-level water resource relationship, and establishing a multi-level data configuration relationship between a city centralized sewage treatment plant and a centralized reclaimed water plant;
step 101, dividing a city into a plurality of sub-areas according to constraint conditions such as administrative divisions, water conservancy conditions, water resource utilization projects and the like;
102, taking each subarea as a unit, acquiring spatial position information of a centralized sewage treatment plant and a centralized regenerated water plant in each subarea, acquiring outlet tail water destination information of the centralized sewage treatment plant, inlet water source information of the centralized regenerated water plant and outlet water destination information of the centralized regenerated water plant;
103, based on the step 102, establishing a multi-level data configuration relationship based on the centralized sewage treatment plants and the centralized reclaimed water plants, establishing an association relationship between the tail water of the effluent of each centralized sewage treatment plant and the inlet water of the centralized reclaimed water plant, establishing an effluent tail water reuse departure association relationship of each centralized sewage treatment plant, and establishing an effluent reuse departure association relationship of the centralized reclaimed water plant;
and 2, acquiring basic information data of a municipal centralized sewage treatment plant and a centralized reclaimed water plant, acquiring supply and demand prediction data of reuse ways of tail water and reclaimed water, and acquiring water distribution and transportation data and urban development data.
Preferably, the number data, the spatial position distribution data information and the design processing capacity data x of each centralized sewage treatment plant are acquired by taking each subarea as a unitiWater quality of effluent tail water and reuse purpose information data x of effluent tail waterwAcquiring the data information of the number and spatial position distribution of the centralized regeneration water plant and the data x of the design processing capacityi”Water inlet source information, water quality of outlet regenerated water and reuse purpose information data xjAcquiring municipal delivery and distribution capacity data and a cost coefficient c, and acquiring socioeconomic development data and bearing population data b of each subarea;
considering that the recycled water source is sewage, the cost coefficient c comprehensively comprises municipal engineering construction and maintenance cost, road transportation cost, advanced treatment cost of a user according to the requirement of the user, environment pollution reduction treatment cost reaching the lake discharge standard, public acceptance intention risk and other weight factors generated in the process of recycling the water source.
Step 3, according to the information data, establishing a multi-target data configuration model under the constraint conditions of considering water quantity balance, water supply capacity, water delivery capacity, water demand capacity, risk and the like, wherein the target function is a multi-target function optimally established by using multi-type data; the multi-type data includes environmental data, benefit data, and pathway data. The information data at least comprises basic information data, supply and demand prediction data, water transmission and distribution data, city development data and the like; in one embodiment, the environmental data, the benefit data, and the path data are respectively the maximum total water resource utilization, the maximum water resource recycling benefit, and the minimum per-son water shortage, and a multi-objective function is established based thereon.
Step 301, determining the variable of the data configuration model as (x)i’j，xiw)；
Step 302, establishing a water resource utilization total amount objective function F1And a mathematical relation between the water source utilization amount and the multi-level water amount scheduling, wherein a water resource utilization amount target function is represented by the maximum water utilization amount data of a centralized sewage treatment plant;
MinF1(x)＝Min∑(xi-xi’j-xw)
wherein i is the serial number of the centralized sewage treatment plant, i' is the serial number of the centralized reclaimed water plant, j is the serial number of the recycling use of the centralized reclaimed water plant, w is the serial number of the recycling use of the centralized sewage treatment plant except the reclaimed water plant, xi’j、xiwThe amount of the effluent of each centralized type reclaimed water plant for different recycling purposes is respectively matched with the amount of the effluent of each centralized type sewage treatment plant for different recycling purposes except for supplying the effluent to the reclaimed water plant.
Step 303, establishing a water resource recycling benefit objective function F2And the mathematical relation between the multi-level water quantity scheduling, and the minimum multi-level water resource allocation benefit data is quantified;
MinF2(x)＝Min∑(xi’j×ci’j+xw×cw)
wherein, ci’j、cwThe cost coefficients of different purposes of the effluent of the centralized water reclamation plant and the effluent of the centralized sewage treatment plant except the supply of the effluent to the water reclamation plant in different space ranges are respectively.
Step 304, establishing a subzone per capita water shortage objective function F3And each levelThe number of the objective function depends on the number of the subareas, and the minimum water shortage data quantity of the supply and demand people of different recycling ways of each subarea is quantified;
MinF3(x)＝Min∑(xi’-xi’j)/b
step 305, determining the multi-objective function of the data configuration model as Min F (x) ═ F1(x)，F2(x)，F3(x)…Fn(x)}；
Step 306, establishing water quantity scheduling constraint conditions, including water supply capacity constraint, water delivery capacity constraint, water demand capacity constraint and other water quantity constraint conditions;
step 307, establishing a multi-level multi-target data configuration model based on the urban centralized sewage treatment plant and the urban centralized water reclamation plant according to the information data, wherein the data configuration model is as follows:
Min F(x)＝{F1(x)，F2(x)，F3(x)…Fn(x)}
in the formula, xi’jmin、xi’jmaxRespectively the lower limit and the upper limit, x, of the allocation data of the centralized type regeneration water plant i' in different recycling purposes jiwmin、xiwmaxRespectively the lower limit and the upper limit of the data allocated by the centralized sewage treatment plant i in other recycling purposes w except for supplying the reclaimed water plant.
And 4, solving by adopting a multi-level urban sewage recycling data dynamic processing method based on a multi-target hybrid genetic algorithm to obtain data configuration schemes under different scenes. In a specific embodiment, the following steps may be specifically adopted:
step 401, determining multi-year mold-entering parameters including multi-year time T, x according to the information data obtained in steps 1-3i、xi‘、xi’jmin、xi’jmax、xiwmin、xiwmax、ci’j、cw、b；
Step 402, setting parameters of a hybrid genetic algorithm: determining the number G of initial populations, the number SN of sub-populations, the number M of individuals of each sub-population and a genetic algebra GEN;
step 403, performing constraint processing on the objective function: when the objective function decision variable does not meet the constraint condition, taking the fitness function value of the objective function as a numerical value which is close to zero and is smaller than a preset threshold value, and determining that the output decision variable meets the constraint condition;
step 404, aligning x based on interior point methodi’j，xiwCarrying out population initialization;
step 405, representing variables by binary codes, and sorting by using a constraint non-dominated sorting method according to the size of individual fitness;
step 406, calculating each objective function value of each population individual, performing selection, crossing and variation operations, sequencing the individuals, performing random crossing and variation and comparing in pairs, sequentially classifying the non-inferior solutions in the current population into a first leading face and an Nth leading face according to the fitness comparison value, sequentially selecting representative individuals from each leading face according to a dynamic archiving method, and dynamically adjusting the population according to the number of screened individuals until the individuals are selected until the set value is reached;
step 407, judging whether a preset genetic algebra GEN is reached, if not, turning to step 405, continuing the next round of global search, otherwise, outputting a Pareto optimal solution set in the dynamic external archive set;
step 408, substituting the data in the multi-year step 401, and repeating the steps 402 to 409 to obtain a multi-year Pareto optimal solution set P1，P2，P3…PN；
Step 409, respectively carrying out perennial Pareto optimal solution sets P1，P2，P3…PNPerforming cluster screening to respectively obtain P with minimum dispersion1K、P2K、…PNKThe optimal solution set based on dynamic continuous planning for many years; in a specific embodiment, the step 409 can also be implemented in the following specific manner:
step 4091, setting different K values, respectively operating step 4, generating discreteness among clustering schemes by comparing the K values by taking the standard deviation of the data set as an index, determining the K value with the best clustering effect and using the K value for the next step;
step 4092, respectively carrying out perennial Pareto optimal solution sets P1，P2，P3…PNPerforming cluster screening to obtain P1、P2、…PNK cluster groups of (a);
step 4093, using standard deviation of data set as index, respectively for P1、P2、…PNPerforming data dispersion analysis on the K clustering groups to obtain P with minimum dispersion1K、P2K、…PNKThe optimal solution set based on dynamic continuous planning for many years;
step 410, according to the optimal solution set P based on dynamic continuous planning for many years1K、P2K、…PNKAnd further setting different target constraint condition scenes and screening target values to obtain an optimal dynamic solution set based on the model target. In a specific embodiment, the step 410 can also be implemented in the following specific manner:
step 4101, setting average scenes based on the maximum total water resource utilization amount, the maximum water resource regeneration and reuse benefit, the minimum per sub-district water shortage and the above constraint conditions according to the environment data, benefit data and path data targets set up by the model;
step 4102, obtaining P according to step 4091K、P2K、…PNKThe model target value is used as a screening condition to respectively obtain a data configuration scheme of a decision scenario under the scenario condition set in the step 4101.
Now, the effectiveness and rationality of the method of the invention are illustrated by taking an example of the optimized configuration of the Kunming city centralized sewage recycling data. The Kunming city has 27 centralized sewage treatment plants, wherein the tail water outlet part of 25 centralized sewage treatment plants is recycled as the water source of corresponding 25 centralized regenerated water plants, and the tail water outlet part of 15 sewage treatment plants in the 25 centralized sewage treatment plants is used as the raw water of industrial supply water of cities downstream of the Dian-Chi basin through city water diversion engineering. The water outlet of 25 centralized water reclamation plants can realize the regeneration and reuse of multiple purposes such as industry, landscape (park landscape water and river water replenishing), urban miscellaneous use (municipal greening, road sprinkling, toilet flushing and car washing) and the like in the region according to administrative divisions. The design treatment capacity of each centralized sewage treatment plant and each centralized regeneration treatment plant in 2020 and 2030, the upper and lower limit values of the demand of various regeneration and reuse approaches in the administrative region where each plant is located, the maximum water regulating capacity of the urban water regulating project, water delivery parameters and other economic and social parameters are obtained. Determining three objective functions, namely determining water balance constraint and establishing a mathematical model, wherein the three objective functions are respectively the maximum centralized sewage reuse amount, the minimum economic cost and the minimum per-capita water shortage. And optimally configuring by adopting a hybrid genetic algorithm to realize three-target optimization. Through repeated test calculation, the optimal parameters for solving the multi-target optimization configuration problem are determined as the initial population number G is 180, the sub-population number SN is 3, the individual number M of each sub-population is 60, and the genetic algebra GEN is 100;
based on the obtained multiple solution sets of the genetic algorithm, clustering the solution sets by using a K-means clustering algorithm, wherein the K value is determined to be 7, 7 clustering schemes in 2020 and 2030 are obtained respectively, continuous clustering scheme sets in 2020 and 2030 are obtained according to the continuity of discrete data, and three scenario decision recommendation schemes based on the maximum total water resource utilization amount, the maximum water resource regeneration and recycling benefit and the minimum sub-district per-capita water shortage are obtained according to a target value screening method.
The above description is only a preferred embodiment of the present invention, and it should be noted that all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
Claims (5)
1. The multi-level urban sewage regeneration and reuse configuration data dynamic processing method based on the multi-target hybrid genetic algorithm is characterized by comprising the following steps of:
step 1, acquiring basic information data of a multi-level water resource relationship, and establishing a multi-level data configuration relationship between a city centralized sewage treatment plant and a centralized reclaimed water plant;
step 2, acquiring basic information data of a city centralized sewage treatment plant and a centralized regeneration water plant, acquiring supply and demand prediction data of reuse ways of tail water and reclaimed water, and acquiring water distribution and transportation data and city development data;
step 3, establishing a multi-target data configuration model according to the basic information data, wherein the specific mode is as follows:
step 301, determining the variable of the data configuration model as (x)i’j，xiw)；
Step 302, establishing a water resource utilization total amount objective function F1And a mathematical relation between the water resource utilization amount and multi-level water quantity scheduling, wherein a water resource utilization amount target function is characterized by the minimum water utilization amount data of a centralized sewage treatment plant:
MinF1(x)＝Min∑(xi-xi’j-xw)
wherein i is the serial number of the centralized sewage treatment plant, i' is the serial number of the centralized reclaimed water plant, j is the serial number of the recycling use of the centralized reclaimed water plant, w is the serial number of the recycling use of the centralized sewage treatment plant except the reclaimed water plant, xi’jThe amount of the discharged water of each centralized type reclaimed water plant is adjusted for different recycling purposes, xiwIs mixed with the effluent of various centralized sewage treatment plants for different recycling purposes except for supplying the effluent to a regeneration water plantiIs the design processing capacity data of a centralized sewage treatment plant, xwIs the information data of the reuse use of the tail water of the effluent;
step 303, establishing a water resource recycling benefit objective function F2And the mathematical relation between the multi-level water quantity scheduling, and the minimum multi-level water resource allocation benefit data is used as quantification:
MinF2(x)＝Min∑(xi’j×ci’j+xw×cw)
wherein, ci’jIs the cost of different purposes of the effluent of the centralized water reclamation plant in different space rangesCoefficient, cwThe cost coefficient of different purposes of effluent of a centralized sewage treatment plant except for supply to a reclaimed water plant in different space ranges is determined;
step 304, establishing a subzone per capita water shortage objective function F3And a mathematical relation between the target function and each level of water quantity scheduling, wherein the number of the target function depends on the number of the subareas, and the minimum water shortage data quantity of the supply and demand persons of different recycling ways of each subarea is taken as quantification:
MinF3(x)＝Min∑(xi’-xi’j)/b，
where b is bearer population data, xi’Is the design and processing capacity data of the centralized regeneration water plant;
step 305, determining the multi-objective function of the data configuration model as Min F (x) ═ F1(x)，F2(x)，F3(x)}；
Step 306, establishing water quantity scheduling constraint conditions, including water supply capacity constraint, water delivery capacity constraint and water demand capacity constraint water quantity constraint conditions;
step 307, establishing a multi-level multi-target data configuration model based on the urban centralized sewage treatment plant and the urban centralized reclaimed water plant according to the basic information data, wherein the data configuration model is as follows:
Min F(x)＝{F1(x)，F2(x)，F3(x)}
in the formula, xi’jmin、xi’jmaxRespectively the lower limit and the upper limit, x, of the allocation data of the centralized type regeneration water plant i' in different recycling purposes jiwmin、xiwmaxRespectively the lower limit and the upper limit of the data prepared by the centralized sewage treatment plant i in other recycling purposes w except for supplying to a reclaimed water plant;
step 4, acquiring a data configuration scheme based on the multi-target hybrid genetic algorithm, wherein the specific method comprises the following steps:
step 401, obtaining according to step 1-step 3Taking information data, determining multi-year mold-entering parameters including multi-year time T, xi、xi‘、xi’jmin、xi’jmax、xiwmin、xiwmax、ci’j、cw、b；
Step 402, setting parameters of a hybrid genetic algorithm: determining the number G of initial populations, the number SN of sub-populations, the number M of individuals of each sub-population and a genetic algebra GEN;
step 403, performing constraint processing on the objective function: when the objective function decision variable does not meet the constraint condition, taking the fitness function value of the objective function as a numerical value which is close to zero and is smaller than a preset threshold value, and determining that the output decision variable meets the constraint condition;
step 404, aligning x based on interior point methodi’j，xiwCarrying out population initialization;
step 405, representing variables by binary codes, and sorting by using a constraint non-dominated sorting method according to the size of individual fitness;
step 406, calculating each objective function value of each population individual, performing selection, crossing and variation operations, sequencing the individuals, performing random crossing and variation and comparing in pairs, sequentially classifying the non-inferior solutions in the current population into a first leading face and an Nth leading face according to the fitness comparison value, sequentially selecting representative individuals from each leading face according to a dynamic archiving method, and dynamically adjusting the population according to the number of screened individuals until the individuals are selected until the set value is reached;
step 407, judging whether a preset genetic algebra GEN is reached, if not, turning to step 405, continuing the next round of global search, otherwise, outputting a Pareto optimal solution set in the dynamic external archive set;
step 408, substituting the data of the plurality of years in the step 401 respectively, and repeating the steps 402 to 407 to obtain a plurality of years Pareto optimal solution set P1，P2，P3…PN；
Step 409, respectively carrying out perennial Pareto optimal solution sets P1，P2，P3…PNPerforming cluster screening to respectively obtain P with minimum dispersion1K、P2K、…PNKThe optimal solution set based on dynamic continuous programming for many years, wherein K is the number of cluster groups;
step 410, according to the optimal solution set P based on dynamic continuous planning for many years1K、P2K、…PNKAnd further setting different target constraint condition scenes and screening target values to obtain a model-based target optimal dynamic solution set.
2. The method according to claim 1, wherein the step 409 specifically comprises:
step 4091, setting different K values, respectively operating the steps 401 to 407, generating discreteness among clustering schemes by comparing the K values by taking the standard deviation of the data set as an index, determining the K value with the best clustering effect and using the K value for the next step;
step 4092, respectively carrying out perennial Pareto optimal solution sets P1，P2，P3…PNPerforming cluster screening to obtain P1、P2、…PNK cluster groups of (a);
step 4093, using standard deviation of data set as index, respectively for P1、P2、…PNPerforming data dispersion analysis on the K clustering groups to obtain P with minimum dispersion1K、P2K、…PNKBased on the optimal solution set for dynamic continuous planning.
3. The method of claim 1, wherein the step 410 specifically comprises:
step 4101, setting average scenes based on the maximum total water resource utilization amount, the maximum water resource recycling benefit, the minimum per sub-district water shortage and the above constraint conditions according to the set target of the model;
step 4102, obtaining P according to step 4091K、P2K、…PNKThe model target value is used as a screening condition to respectively obtain a data configuration scheme of a decision scenario under the scenario condition set in the step 4101.
4. The method according to claim 1, wherein the step 2 specifically comprises:
acquiring the number data, the spatial position distribution data information and the design processing capacity data x of each centralized sewage treatment plant by taking each subarea as a unitiWater quality of effluent tail water and reuse purpose information data x of effluent tail waterwAcquiring the data information of the number and spatial position distribution of the centralized regeneration water plant and the data x of the design processing capacityi’Water inlet source information, water quality of outlet regenerated water and reuse purpose information data xjAnd acquiring municipal delivery and distribution capacity data and a cost coefficient c, and acquiring socioeconomic development data and bearing population data b of each subarea.
5. The method according to claim 1, wherein step 1 specifically comprises:
step 101, dividing a city into a plurality of sub-areas according to administrative divisions, water conservancy conditions and water resource utilization projects;
102, taking each subarea as a unit, acquiring spatial position information of a centralized sewage treatment plant and a centralized regenerated water plant in each subarea, acquiring outlet tail water destination information of the centralized sewage treatment plant, inlet water source information of the centralized regenerated water plant and outlet water destination information of the centralized regenerated water plant;
and 103, establishing a multilevel data configuration relationship based on the centralized sewage treatment plants and the centralized reclaimed water plants, establishing an association relationship between the tail water of the effluent of each centralized sewage treatment plant and the inlet water of the centralized reclaimed water plant, establishing an effluent tail water reuse direction-going association relationship of each centralized sewage treatment plant, and establishing an effluent reuse direction-going association relationship of the centralized reclaimed water plants based on the step 102.
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