CN117494861A - Water resource optimal allocation method for coordinating city and county two-stage water resource utilization targets - Google Patents
Water resource optimal allocation method for coordinating city and county two-stage water resource utilization targets Download PDFInfo
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Abstract
The invention discloses a water resource optimization configuration method for coordinating the utilization targets of city and county two-stage water resources, which comprises the following steps: drawing a water resource allocation network diagram; determining targets of the city and county two-stage water resource optimization configuration, and constructing a city and county two-stage water resource optimization configuration model; providing a hybrid strategy whale algorithm HSWOA to solve a city and county two-stage water resource optimization configuration model to form an initial game scheme set; screening non-dominant solutions in the initial game scheme set by adopting a rapid non-dominant sorting method to form a final game scheme set; and (3) introducing a back discussion price counter-price method, sequencing the schemes in the final game scheme set by the city level and the county level according to the optimal water resource utilization target, and finally obtaining the water resource optimal allocation scheme acceptable by both city and county levels through multiple rounds of negotiations. The invention balances the two-stage water resource utilization targets of the city and county, and finally realizes Nash balance by adopting the scheme, thereby having reference significance for guiding the development and utilization planning of water resources in the municipal administration area.
Description
Technical Field
The invention belongs to the water resource management technology, and particularly relates to a water resource optimal allocation method for coordinating the utilization targets of city and county two-stage water resources.
Background
The traditional water resource optimization configuration method assumes that only one authoritative water resource manager exists, and the authoritative water resource manager proposes a non-inferior solution or a group of pareto solution sets from the point of maximizing the overall water resource utilization target of the area, and then selects a final water resource configuration scheme according to self decision preference. In actual water resource management, multi-stage water resource managers often exist, the goal of the superior water resource manager is to coordinate water supply contradiction between subareas, and the secondary water resource manager is more focused on the water quantity available by the secondary water resource manager. In the multi-objective decision process, the participation of the secondary water resource manager is low, the requirements of the secondary water resource manager are not fully expressed, and the situation of low satisfaction of the water resource allocation scheme possibly exists, so that the execution of the scheme is influenced. Therefore, it is highly desirable to propose a water resource optimizing configuration method that comprehensively considers different levels of water resource utilization targets so as to better coordinate individual targets with overall targets.
The efficient optimization algorithm is an important tool for selecting a water resource allocation game scheme. The whale optimization algorithm (whale optimization algorithm, WOA) is a meta-heuristic optimization algorithm proposed by Mirjallii in 2016, and has the advantages of unique search mechanism, few parameters and easy understanding. The WOA is applied to the fields of water demand prediction, water resource optimal allocation, water-wind-light multi-energy complementary scheduling and the like by a plurality of scholars, and the result shows that the WOA has good performance in the process of the research problems. However, similar to other intelligent optimization algorithms, WOA has the disadvantage of slow convergence speed and susceptibility to local optima. Therefore, various strategies are needed to improve the WOA comprehensively so as to improve the convergence speed and optimizing precision.
Disclosure of Invention
The invention aims to: the invention aims to provide a water resource optimal allocation method for coordinating the water resource utilization targets of the city and county levels, aiming at solving the problem of target conflict in the water resource optimal allocation of the city and county levels.
The technical scheme is as follows: the invention relates to a water resource optimal allocation method for coordinating the utilization targets of city and county two-stage water resources, which comprises the following steps:
collecting basic data of water projects, river canal systems and administrative regions in the market domain, dividing computing units, analyzing supply and demand water topology relations between various water sources and water users in the computing units, and drawing a water resource allocation network diagram;
determining targets of the city and county two-stage water resource optimization configuration, providing an objective function and constraint conditions of an optimization configuration model, and constructing the city and county two-stage water resource optimization configuration model;
providing a hybrid strategy whale algorithm HSWOA to solve a city and county two-stage water resource optimization configuration model to form an initial game scheme set; the method comprises the steps that a hybrid strategy whale algorithm HSWOA is improved based on a whale optimization algorithm WOA, a Sobol sequence is used for replacing random search to initialize a population, nonlinear adjustment is carried out on a convergence factor a, a self-adaptive weight omega is added in the process of updating the whale individual position, a detection whale mechanism is introduced into the WOA, and when whale individuals with highest fitness remain unchanged in a certain iteration number, detection whale search is triggered;
screening non-dominant solutions in the initial game scheme set by adopting a rapid non-dominant sorting method to form a final game scheme set;
and (3) introducing a back discussion price counter-offer method, sequencing the schemes in the game scheme set by the city level and the county level according to the optimal water resource utilization target, and finally obtaining the water resource optimal allocation scheme acceptable by both city and county levels through multiple rounds of negotiations.
Further, the city and county two-stage water resource optimization configuration model is as follows:
min F(x)＝{f 1 (x),f 2 (x)}
wherein F (x) is the set of objective functions; f (f) 1 (x) A coefficient of water supply satisfaction is expressed; f (f) 2 (x) Indicating the water deficiency; i is a water supply source, i=1, 2,3, …, I; j is the number of users in the area, j=1, 2,3, …, J; k is the number of water resource allocation calculation units in the area, and k=1, 2,3, … and K;the water quantity is distributed to the jth user of the kth computing unit for the ith water source; w (W) i k The water supply amount of the ith water source is the kth computing unit; />Maximum water supply capacity for the kth computing unit of the ith water source;the water demand of the jth user is calculated for the kth unit.
Further, the method for solving the city and county two-stage water resource optimization configuration model by adopting the hybrid strategy whale algorithm HSWOA comprises the following steps:
(1) The water supply quantity of each water source to each user of different computing units is used as a decision variable, and a decision variable search interval [ ub, lb ] is determined]Setting an iteration number threshold limit when generating a detection whale, and determining a population scale N and a maximum iteration number t max ；
(2) Initializing population positions by adopting a Sobol sequence, calculating fitness of various populations, and recording the population position with highest fitness, wherein the iteration times t=0
(3) If the optimal whale position is kept unchanged after the limit iterations, triggering the detection whale, and carrying out cauchy Gaussian variation on the detection whale; the calculation formula is as follows:
wherein,the solution vector is the solution vector when the iteration times reach limit; />A solution vector for the (t+1) th search; cauchy (0, 1) is a random number obeying the Cauchy distribution; gauss (0, 1) is a random number subject to gaussian distribution;
(4) If the optimal whale position changes in limit iterations, nonlinear linearization is performed on the convergence factor a:updating the adaptive weight omega: />
(5) Parameters a and B are calculated, a=2aε -a, b=2ε, and the population location is updated:
wherein epsilon is a random number, epsilon is 0,1]；A solution vector of whale individuals randomly selected from the current whale population in the t-th search; />Searching for distances of prey for other whale random walks; />The solution vector for the t-th search; a and B are parameters;
(6) As the number of iterations t increases, the value of the I A is gradually reduced to 1, and the HSWOA enters a hunting stage; at the moment, the position of the whale individual with the optimal fitness in the whale group is the position of a prey or the position closest to the prey, and the rest whales are gathered to the position of the whale individual with the optimal fitness;
(7) When a whale swarm attacks a prey, two behaviors of surrounding the prey and spiral air bubble network hunting exist simultaneously, and the optimal whale position is updated by setting random probability; setting random probability p E [0,1] in each iteration process, and when the probability p is more than or equal to 0.5, performing spiral bubble net hunting on whale shoves; when the probability p is less than 0.5, the whale group can surround the prey according to the position of the optimal whale individual; as the number of iterations increases, |a| decreases stepwise, HSWOA finds the optimal solution when |a|=0.
Furthermore, the specific calculation formula of the method for updating the individual position of whales in the surrounding hunting stage is as follows:
wherein,the solution vector is the whale individual solution vector with the highest fitness after the t-th iteration; />The distance of the whale individual is optimized for the distance of the remaining whales.
Further, the specific calculation formula of the whale hunting mechanism is as follows:
where c is a coefficient controlling the shape of the spiral, c=1; omega is a random number, omega e [ -1,1].
Further, the final game scheme set generation method comprises the following steps:
calculating optimal fitness of water supply satisfaction degree based on HSWOA and water deficiency amount in each iteration processAnd->Will->And->Merging to form an initial game scheme set G= { G 1 ,g 2 ,…,g w }；
From the first scenario G in the initial game scenario set G 1 Initially, g 1 And g is equal to 2 For comparison, if g 1 All objective function values in (a) are not inferior to g 2 And at least one objective function value is better than g 2 Then refer to g 1 Dominant g 2 And let g 2 Is increased by 1; traversing all schemes in G, selecting the scheme with dominant frequency of 0 to form a final gameScheme set s= { S 1 ,s 2 ,…,s q }。
Further, a back bargaining counter-price method is adopted to obtain a water resource optimal allocation scheme, which is specifically as follows:
the city level and the county level order the schemes in the final game scheme set S according to the optimal water resource utilization target;
the city level and the county level conduct negotiations according to the sorting result, in the first round of negotiations, if the schemes provided by the city level and the county level are consistent, the scheme is the final scheme, and if the provided schemes are inconsistent, the next round of negotiations is carried out;
after multiple rounds of negotiations, the city level agrees with the county level proposed solution, which is the final solution.
Based on the same inventive concept, the water resource optimal allocation system for coordinating the city and county two-stage water resource utilization targets comprises the following components:
the network diagram drawing module is used for collecting basic data of water projects, river canal systems and administrative regions in the range of the market domain, dividing the computing units, analyzing the supply and demand water topology relation between various water sources and water users in the computing units, and drawing a water resource configuration network diagram;
the initial game scheme set generation module is used for determining targets of the city and county levels in water resource optimization configuration, providing objective functions and constraint conditions of an optimization configuration model, constructing the city and county level water resource optimization configuration model, and providing a hybrid strategy whale algorithm HSWOA to solve the model to form an initial game scheme set;
the final game scheme set generation module is used for screening non-dominant solutions in the initial game scheme set by adopting a rapid non-dominant sorting method to form a final game scheme set;
and the back-off discussion price counter-price module is used for introducing a back-off discussion price counter-price method, and the city level and the county level order the schemes in the final game scheme set according to the optimal water resource utilization target of the city level and the county level, and finally obtain the water resource optimal allocation scheme acceptable by both city and county levels through multiple rounds of negotiations.
Based on the same inventive concept, an electronic device of the present invention comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the water resource optimizing configuration method for coordinating the city and county two-stage water resource utilization targets.
Based on the same inventive concept, the computer readable storage medium of the present invention stores computer instructions for executing a water resource optimizing configuration method for coordinating the two-stage water resource utilization targets in city and county as described above when the computer instructions are called.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable technical effects:
(1) The convergence factor a is subjected to nonlinear adjustment, so that the convergence factor a has larger value and slower attenuation speed at the initial stage of iteration, and the global searching capability of the algorithm is improved; in the later iteration stage, the convergence factor a is rapidly attenuated to a smaller value, so that the local searching capability of an algorithm is improved, and population convergence is accelerated;
(2) Adding an adaptive weight omega in the process of updating the position of the whale individual, weakening the influence of the whale individual with the highest fitness on the current whale group position updating in the initial iteration stage, and further improving the global searching capability of the algorithm in the early stage; when the iteration times are large, gradually improving the influence degree of whale individuals with the highest fitness on the whale population position, and improving the convergence rate of the algorithm;
(3) Introducing a detection whale mechanism into WOA, triggering detection whale search when whale individuals with highest fitness remain unchanged in a certain iteration number, and enhancing the ability of jumping out of a local optimal solution by generating larger-amplitude position update;
(4) Respectively taking a city level water administration department and a county level water administration department as decision bodies, constructing a set of city and county level water resource optimization configuration model, and fully reflecting the difference of the two-level water resource utilization targets of the city and county;
(5) The rapid non-dominant ordering method is introduced to optimize the schemes in the initial game scheme set generated by the HSWOA, so that the advancement of the optimized scheme can be ensured, and the workload of a city and county two-stage back-off counter-price method can be obviously reduced.
(6) By introducing a back-off discussion counter-price method, the importance degree of the county-level water resource utilization target in the water resource optimal allocation decision is improved, and the city-county-level water resource utilization target is considered. Compared with the traditional water resource optimal allocation research which only considers the market-level water resource utilization target, the obtained final scheme is easier to accept and implement for the two levels of the city and county.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a network diagram of a water resource configuration;
FIG. 3 is a set of 2025 Handan city different level year final gaming scenarios;
FIG. 4 is a graph of 2025 different horizontal year game scenario set benefit values;
fig. 5 shows 2025 results of different horizontal annual water resource allocation.
Detailed Description
The present invention will be further described in detail with reference to the accompanying drawings and specific embodiments, which are to be understood as illustrative only and not limiting to the scope of the invention, which is to be given the full breadth of the claims appended hereto and any and all equivalents thereof, which are intended to be resorted to by those skilled in the art.
Aiming at the conflict of the water resource utilization targets of the city level and the county level in the water resource allocation, the invention provides a water resource optimization allocation method for coordinating the water resource utilization targets of the city level and the county level. The method simultaneously considers the two-stage water resource utilization requirements of the city and county from the angles of ecology, society and economic benefit, firstly, collects the data of hydraulic engineering, river canal system, administrative division and the like of the city domain, clarifies the supply and demand water topology relation between each water source and each water user in a space level, and draws a water resource allocation network diagram; secondly, determining objective functions and constraint conditions of the utilization of the water resources of the city and county levels, constructing a water resource optimizing configuration model of the city and county levels, and providing a whale algorithm (Hybrid Strategy Whale Optimization Algorithm, HSWOA) of a hybrid strategy to solve the model to generate an initial game scheme set of water resource optimizing configuration; thirdly, screening representative schemes by a rapid non-dominant sorting method to form a final game scheme set; and finally, introducing a back bargaining counter-price method, sorting game schemes according to the water resource utilization target optimization of the game schemes from the city level and the county level respectively, and starting a negotiation link until a compromised scheme set is not empty for the first time, wherein the scheme is a final scheme. The invention balances the two-stage water resource utilization targets of the city and county, and finally realizes Nash balance by adopting the scheme, thereby having reference significance for guiding the development and utilization planning of water resources in the municipal administration area.
As shown in fig. 1, the water resource optimizing configuration method for coordinating the city and county two-stage water resource utilization targets of the invention comprises the following steps:
s1, basic data such as water projects, river canal systems, administrative regions and the like in the urban area are collected, computing units are divided, supply and demand water topology relations between various water sources and water users in the computing units are analyzed, and a water resource allocation network diagram is drawn.
S2, determining targets of the city and county levels in water resource optimization configuration, providing an objective function and constraint conditions of an optimization configuration model, constructing the city and county level water resource optimization configuration model, and providing a hybrid strategy whale algorithm HSWOA to solve the model to form an initial game scheme set; the method specifically comprises the following steps:
s2-1, the city and county two-stage water resource optimal allocation model is as follows:
minF(x)＝{f 1 (x),f 2 (x)}
wherein F (x) is the set of objective functions; f (f) 1 (x)、f 2 (x) Respectively representing the water supply satisfaction degree based on the coefficient and the water shortage; i is the water supply source (i=1, 2,3, …, I); j is the number of users in the area (j=1, 2,3, …, J); k is the number of the water resource allocation calculation units in the region (the calculation units are the parameters)Minimum area optimally configured with water resources, k=1, 2,3, …, K);the water quantity is distributed to the jth user of the kth computing unit for the ith water source; w (W) i k The water supply amount of the ith water source is the kth computing unit; />Maximum water supply capacity for the kth computing unit of the ith water source; />The water demand of the jth user is calculated for the kth unit.
f 1 (x) For water supply satisfaction, the coefficient of kunity is defined as the ratio of the area between the diagonal and the lorentz curve to the area between the entire triangle below the diagonal. Firstly, defining water supply satisfaction degree:
wherein S is l,k Water supply satisfaction for the kth computing unit of the ith sub-basin;the water demand of the jth user is calculated for the kth calculation unit; m is M l,k The satisfaction of the water supply for the kth computing unit of the first sub-basin is the proportion of the satisfaction of all computing units of the first sub-basin.
Will M l,k Arranging from small to large to generate a group of sequences M' l,k Calculating the cumulative frequency P of the sequence s,n ：
According to the definition of the coefficient of Kerning, the calculation method of the coefficient of Kerning is as follows:
wherein G is S,l The coefficient of kunit for water satisfaction in the first sub-basin.
Therefore, the water supply satisfaction coefficient f 1 (x) The calculation method comprises the following steps:
wherein L is the number of all sub-watershed;
f 2 (x) The water shortage amount is calculated as follows:
s2-2, according to the water resource allocation network diagram of the urban area, so as toFor decision variables, determining decision variable search intervals [ ub, lb ]]Setting an iteration number threshold limit when generating a detection whale, and determining a population scale N and a maximum iteration number t max 。
Let iteration number t=0, initialize population position by Sobol sequence, record initial solution vectorThe specific calculation formula is as follows:
wherein P is primitive polynomial;is a solution vector which is not processed by a Sobol sequence; d is the degree of freedom of the primitive polynomial; />A random number between 0 and 1; r is (r) 1 ，…，r d-1 R is the primitive polynomial coefficient d-1 ∈[0,1]；/> Is positive odd; h is a 1 ,h 2 ,...,h u Randomly taking a value of 0 or 1; />Is binary intermediate exclusive OR operation; />For the initial solution vector, ++>The water quantity is distributed to the 1 st water user of the 1 st computing unit for the 1 st water source; />The water amount is distributed for the 1 st water source to the 2 nd water user of the 1 st computing unit.
Will beSubstituting F (x), and respectively calculating initial fitness of water supply satisfaction degree based on coefficient and water deficiency amount>And->
And the Sobol sequence is adopted to replace random search for population initialization, so that the uniformity of WOA search space distribution and the diversity of the population are improved.
S2-5, triggering the detection whale if the optimal whale position is kept unchanged after the limit iteration, and carrying out cauchy Gaussian variation on the detection whale, wherein the specific calculation formula is as follows:
wherein,the solution vector is the solution vector when the iteration times reach limit; />A solution vector for the (t+1) th search; cauchy (0, 1) is a random number obeying the Cauchy distribution; gauss (0, 1) is a random number that follows a gaussian distribution.
S2-6, if the optimal whale position changes in limit iterations, nonlinear linearization is carried out on the convergence factor a, and the self-adaptive weight omega is updated, wherein a specific calculation formula is as follows:
s2-5, calculating parameters A and B, and updating the population position, wherein the specific calculation formula is as follows:
A＝2aε-a
B＝2ε
wherein epsilon is a random number, and the value range is 0,1]；A solution vector of whale individuals randomly selected from the current whale population in the t-th search; />Searching for distances of prey for other whale random walks; />The solution vector for the t-th search; a and B are coefficients.
S2-6, with the increase of the iteration times t, the A gradually decreases to 1, and the mixing strategy whale algorithm HSWOA enters a hunting stage. The optimal whale position in the whale population is the position of or closest to the prey at this time, so that the remaining whales are gathered towards the position of the individual whales with optimal fitness. The specific calculation formula of the method for updating the position of whales in the surrounding prey stage is as follows:
wherein,the distance of the whale individual is optimized for the distance of the remaining whales.
S2-7, when a whale swarm attacks a prey, two behaviors of surrounding the prey and spiral air bubble network hunting exist simultaneously, so that the optimal whale position is updated by setting random probability. In each iteration process, setting random probability p E [0,1], when the probability p is more than or equal to 0.5, carrying out spiral bubble net hunting on whales, and when the probability p is less than 0.5, surrounding hunting objects by the whales according to the optimal whale individual positions. As the number of iterations increases, a decreases stepwise, and when a=0, the hybrid strategy whale algorithm HSWOA finds the optimal solution. The specific calculation formula of the whale hunting mechanism is as follows:
where c is a coefficient controlling the shape of the spiral, c=1; omega is a random number, omega e [ -1,1].
S2-8, calculating water supply in each iteration processOptimal fitness of satisfaction coefficient and water deficiencyAnd
s2-9, willAnd->Merging to form an initial game scheme set G= { G 1 ,g 2 ,…,g w }；
S3, screening non-dominant solutions in the initial game scheme set by adopting a rapid non-dominant sorting method to form a final game scheme set; the method specifically comprises the following steps:
from the first scenario G in the initial game scenario set G 1 Initially, g 1 And g is equal to 2 For comparison, if g 1 All objective function values in (a) are not inferior to g 2 And at least one objective function value is better than g 2 Then refer to g 1 Dominant g 2 And let g 2 Is increased by 1. Traversing all schemes in G, selecting the scheme with dominant times of 0 to form a final game scheme set S= { S 1 ,s 2 ,…,s q }。
And S4, introducing a back bargaining counter-price method, and sequencing a final game scheme set by the city level and the county level according to the optimal water resource utilization target of the city level and the county level, and finally obtaining a water resource optimal allocation scheme acceptable by both city and county levels through multiple rounds of negotiations.
The method specifically comprises the following steps:
s4-1, the city level and the county level order the schemes in the final game scheme set S according to the optimal water resource utilization target;
s4-2, negotiating between the city level and the county level according to the sequencing result, wherein in the first round of negotiating, if the schemes provided by the city level and the county level are consistent, the scheme is the final scheme, and if the provided schemes are inconsistent, the next round of negotiating is performed;
s4-3, after multiple rounds of negotiations, the municipal level agrees with the scheme proposed by the administrative department of the county level water, and the scheme is the final scheme.
Examples
The rationality and effectiveness of the inventive method are now described by taking the optimized allocation of the Handan municipal water resource as an example. The city of Handy is located in the south of Hebei province of China, the latitude of the land is 36 degrees 04 degrees to 37 degrees 01 degrees, the longitude of east is 113 degrees 28 degrees to 115 degrees 28 degrees, and the land area is 12047km 2 . The local area has 2 large reservoirs, namely Yueyeng reservoir and Dongwushi reservoir, and the total storage capacity is 14.615 hundred million m 3 The method comprises the steps of carrying out a first treatment on the surface of the The medium-sized reservoir is 5 seats, namely an on-site reservoir, a four-lining rock reservoir, a vehicle valley reservoir and a green tower reservoir, and the total reservoir capacity is 12316 ten thousand meters 3 .6 water diversion openings are arranged in the south-to-north-water-transfer line project co-located in the Handa city, and 3.52 hundred million m water is supplied to the Handa city each year 3 . The water supply range of the yellow lead project relates to 8 counties (cities) such as a large-name county, a Wei county, a Feixiang district, a Qiu county, a Liu Zhu county, a qu Zhu county, a GuangPing county, a Jize county and the like, and the benefited population is 300 tens of thousands. The existing water taking well 122579 holes in Handa city, and the total underground water exploitation amount in whole city is 8.2332 hundred million m 3 . The total market has 50 sewage treatment plants, and the water supply amount of reclaimed water is 8843 ten thousand meters 3 . The water supply for the handan market in 2021 is shown in table 1. According to the transmission relation and hydraulic connection between each calculation unit and hydraulic engineering, a water resource allocation network diagram of the city water resource allocation network diagram is drawn, and the water resource allocation network diagram is shown in fig. 2.
Table 1 Handan Water supply and consumption in 2021
The invention takes water supply quantity of each water source in 2025 years to each water user of each calculation unit as decision variable, takes the minimum coefficient of water supply satisfaction degree as the utilization target of city-level water resource, takes the minimum water shortage as the utilization target of county-level water resource, takes water supply balance, water supply capacity, water demand, non-negative variable and the like as constraint conditions to construct the city-county two-level water resourceAnd (5) configuring a model, and solving the model by adopting HSWOA. The specific parameters of HSWOA are set as follows: population size n=200, iteration number threshold limit=10 when generating a detection whale, maximum iteration number t max =350. And respectively picking up the model year, generating an initial game scheme set by using the HSWOA, and then screening the initial game scheme set by adopting a rapid non-dominant sorting method so as to generate a final game scheme set. The final set of gaming schemes for different horizontal years for the 2025-year handan city is shown in fig. 3. As can be seen from fig. 3 (a) and (b), in the county level objective, the median and average number of water shortage in the dead water year are higher than those in the plain water year, mainly because the reservoir water and the lifting water in the dead water year can supply water in a reduced amount, and the agricultural water demand is increased, resulting in the water shortage being higher than that in the plain water year as a whole; in the aspect of the market-level objective, the median and average number of the water supply satisfaction coefficient of the dead water year are higher than those of the plain water year, mainly because the water supply capacity of the dead water year is reduced, the water demand is increased, and the water taking contradiction of each computing unit of the sub-river basin is aggravated, so that the water supply satisfaction coefficient of the dead water year is obviously higher than that of the plain water year.
The two-stage water resource optimization configuration process of city and county in different levels in 2025 is shown in table 2 and table 3. Table 2 shows the two-stage water resource optimizing configuration process of the city and county in the plain, and the two optimal water resource optimizing configuration process according to the respective targets provide an ideal scheme and an expected target, namely the ideal city level target is 0.1502, and the ideal county level target is 781.68 multiplied by 10 4 m 3 Obviously, the ideal proposal numbers proposed by the two are not unified, so that the back discussion counter-offer flow is started. In the first round of back-off counter-offer, both compromise the respective objective, but the objective at this time is still closer to the optimal objective. The comparison shows that a scheme capable of meeting the two targets at the same time does not exist, so that the next round of back-off discussion counter-price is carried out. Until the process is carried out to the 5 th round, the scheme 5 meets the targets of the two schemes at the same time, and Nash equilibrium is realized, so that the scheme is a final recommended scheme for the water resource allocation of the open water year. In table 3, the condition of the dead water year is similar to that of the plain water year, and the city and county two levels cannot agree, so that the back discussion counter-price flow is started. When the back discussion counter-price goes to the 6 th round, the 6 th scheme simultaneously satisfies the two-stage water resource utilization of city and countyThe target is used, so that scheme 6 is the final recommended scheme for the allocation of the water resources in the dry water. The results of the optimal allocation of the water resources in the open water and the dead water are shown in fig. 4 (a) and (b) and fig. 5 (a) and (b), respectively.
Table 2 Pingnian city and county two-stage water resource optimization configuration process
Table 3 two-stage water resource optimization configuration process in city and county in dead water year
Based on the same inventive concept, the water resource optimal allocation system for coordinating the city and county two-stage water resource utilization targets comprises the following components:
the network diagram drawing module is used for collecting basic data of water projects, river canal systems and administrative regions in the range of the market domain, dividing the computing units, analyzing the supply and demand water topology relation between various water sources and water users in the computing units, and drawing a water resource configuration network diagram;
the initial game scheme set generation module is used for determining targets of the city and county levels in water resource optimization configuration, providing objective functions and constraint conditions of an optimization configuration model, constructing the city and county level water resource optimization configuration model, and providing a hybrid strategy whale algorithm HSWOA to solve the model to form an initial game scheme set; the method comprises the steps that a hybrid strategy whale algorithm HSWOA is improved based on a whale optimization algorithm WOA, a Sobol sequence is used for replacing random search to initialize a population, nonlinear adjustment is carried out on a convergence factor a, a self-adaptive weight omega is added in the process of updating the whale individual position, a detection whale mechanism is introduced into the WOA, and when whale individuals with highest fitness remain unchanged in a certain iteration number, detection whale search is triggered;
the final game scheme set generation module is used for screening non-dominant solutions in the initial game scheme set by adopting a rapid non-dominant sorting method to form a final game scheme set;
and the back-off discussion price counter-price module is used for introducing a back-off discussion price counter-price method, sequencing the final game scheme according to the self water resource utilization target by the city level and the county level, and finally obtaining the water resource optimal configuration scheme acceptable by both city and county levels through multiple rounds of negotiations.
Based on the same inventive concept, an electronic device of the present invention comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the water resource optimizing configuration method for coordinating the city and county two-stage water resource utilization targets.
The memory may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) and/or cache memory. The device may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, memory may be used to read from or write to non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"). A program/utility having a set (at least one) of program modules may be stored, for example, in a memory, such program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules typically carry out the functions and/or methods of the embodiments described herein.
The processor executes various functional applications and data processing by running programs stored in the memory, for example, the water resource optimizing configuration method for coordinating the city and county two-stage water resource utilization targets is realized.
Based on the same inventive concept, the computer readable storage medium of the present invention stores computer instructions for executing a water resource optimizing configuration method for coordinating the two-stage water resource utilization targets in city and county as described above when the computer instructions are called.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the above method operations, but may also perform the related operations in the method provided in any embodiment of the present invention.
The embodiment of the invention also discloses a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in a water resource optimizing configuration method for coordinating the two-stage water resource utilization targets in city county.
The system embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Claims (10)
1. A water resource optimal allocation method for coordinating the utilization targets of city and county two-stage water resources is characterized by comprising the following steps:
collecting basic data of water projects, river canal systems and administrative regions in the market domain, dividing computing units, analyzing supply and demand water topology relations between various water sources and water users in the computing units, and drawing a water resource allocation network diagram;
determining targets of the city and county two-stage water resource optimization configuration, providing an objective function and constraint conditions of an optimization configuration model, and constructing the city and county two-stage water resource optimization configuration model;
providing a hybrid strategy whale algorithm HSWOA to solve a city and county two-stage water resource optimization configuration model to form an initial game scheme set; the method comprises the steps that a hybrid strategy whale algorithm HSWOA is improved based on a whale optimization algorithm WOA, a Sobol sequence is used for replacing random search to initialize a population, nonlinear adjustment is carried out on a convergence factor a, a self-adaptive weight omega is added in the process of updating the whale individual position, a detection whale mechanism is introduced into the WOA, and when whale individuals with highest fitness remain unchanged in a certain iteration number, detection whale search is triggered;
screening non-dominant solutions in the initial game scheme set by adopting a rapid non-dominant sorting method to form a final game scheme set;
and (3) introducing a back discussion price counter-price method, sequencing the schemes in the final game scheme set by the city level and the county level according to the optimal water resource utilization target, and finally obtaining the water resource optimal allocation scheme acceptable by both city and county levels through multiple rounds of negotiations.
2. The water resource optimizing configuration method for coordinating the city and county two-stage water resource utilization targets according to claim 1, wherein the city and county two-stage water resource optimizing configuration model is as follows:
minF(x)＝{f 1 (x),f 2 (x)}
wherein F (x) is the set of objective functions; f (f) 1 (x) A coefficient of water supply satisfaction is expressed; f (f) 2 (x) Indicating the water deficiency; i is a water supply source, i=1, 2,3, …, I; j is the number of users in the area, j=1, 2,3, …, J; k is the number of water resource allocation calculation units in the area, and k=1, 2,3, … and K;the water quantity is distributed to the jth user of the kth computing unit for the ith water source; w (W) i k The water supply amount of the ith water source is the kth computing unit; />Maximum water supply capacity for the kth computing unit of the ith water source; />The water demand of the jth user is calculated for the kth unit.
3. The water resource optimization configuration method for coordinating the city and county two-stage water resource utilization targets according to claim 1, wherein the method for solving the city and county two-stage water resource optimization configuration model by adopting a hybrid strategy whale algorithm HSWOA is as follows:
(1) The water supply quantity of each water source to each user of different computing units is used as a decision variable, and a decision variable search interval [ ub, lb ] is determined]Setting an iteration number threshold limit when generating a detection whale, and determining a population scale N and a maximum iteration number t max ；
(2) Initializing population positions by adopting a Sobol sequence, calculating fitness of various populations, and recording the population position with highest fitness, wherein the iteration times t=0
(3) If the optimal whale position is kept unchanged after the limit iterations, triggering the detection whale, and carrying out cauchy Gaussian variation on the detection whale; the calculation formula is as follows:
wherein,the solution vector is the solution vector when the iteration times reach limit; />A solution vector for the (t+1) th search; cauchy (0, 1) is a random number obeying the Cauchy distribution; gauss (0, 1) is a random number subject to gaussian distribution;
(4) If the optimal whale position changes in limit iterations, nonlinear linearization is performed on the convergence factor a:updating the adaptive weight omega: />
(5) Parameters a and B are calculated, a=2aε -a, b=2ε, and the population location is updated:
wherein epsilon is a random number, epsilon is 0,1]；A solution vector of whale individuals randomly selected from the current whale population in the t-th search; />Searching for distances of prey for other whale random walks; />The solution vector for the t-th search; a and B are parameters;
(6) As the number of iterations t increases, the value of the I A is gradually reduced to 1, and the HSWOA enters a hunting stage; at the moment, the position of the whale individual with the optimal fitness in the whale group is the position of a prey or the position closest to the prey, and the rest whales are gathered to the position of the whale individual with the optimal fitness;
(7) When a whale swarm attacks a prey, two behaviors of surrounding the prey and spiral air bubble network hunting exist simultaneously, and the optimal whale position is updated by setting random probability; setting random probability p E [0,1] in each iteration process, and when the probability p is more than or equal to 0.5, performing spiral bubble net hunting on whale shoves; when the probability p is less than 0.5, the whale group can surround the prey according to the position of the optimal whale individual; as the number of iterations increases, |a decreases progressively, and when |a=0, HSWOA finds the optimal solution.
4. The water resource optimizing configuration method for coordinating city and county two-stage water resource utilization targets according to claim 3, wherein the specific calculation formula of the individual whale position updating method in the surrounding prey stage is as follows:
wherein,the solution vector is the whale individual solution vector with the highest fitness after the t-th iteration; />The distance of the whale individual is optimized for the distance of the remaining whales.
5. The water resource optimizing configuration method for coordinating city and county two-stage water resource utilization targets according to claim 3, wherein the specific calculation formula of the whale hunting mechanism is as follows:
wherein c is a coefficient controlling the shape of the spiral line; omega is a random number, omega e [ -1,1].
6. The water resource optimizing configuration method for coordinating city and county two-stage water resource utilization targets according to claim 1, wherein the final game scheme set generating method is as follows:
calculating optimal fitness of water supply satisfaction degree based on HSWOA and water deficiency amount in each iteration processAndwill->And->Merging to form an initial game scheme set G= { G 1 ,g 2 ,…,g w }；
From the first scenario G in the initial game scenario set G 1 Initially, g 1 And g is equal to 2 For comparison, if g 1 All objective function values in (a) are not inferior to g 2 And at least one objective function value is better than g 2 Then refer to g 1 Dominant g 2 And let g 2 Is increased by 1; traversing all schemes in G, selecting the scheme with dominant times of 0 to form a final game scheme set S= { S 1 ,s 2 ,…,s q }。
7. The water resource optimal allocation method for coordinating the two-stage water resource utilization targets in city and county according to claim 1, wherein a back discussion counter-price method is adopted to obtain a water resource optimal allocation scheme, specifically:
the city level and the county level order the schemes in the final game scheme set S according to the optimal water resource utilization target;
the city level and the county level conduct negotiations according to the sorting result, in the first round of negotiations, if the schemes provided by the city level and the county level are consistent, the scheme is the final scheme, and if the provided schemes are inconsistent, the next round of negotiations is carried out;
after multiple rounds of negotiations, the city level agrees with the county level proposed solution, which is the final solution.
8. A water resource optimal allocation system for coordinating the utilization targets of city and county two-stage water resources is characterized by comprising the following steps:
the network diagram drawing module is used for collecting basic data of water projects, river canal systems and administrative regions in the range of the market domain, dividing the computing units, analyzing the supply and demand water topology relation between various water sources and water users in the computing units, and drawing a water resource configuration network diagram;
the initial game scheme set generation module is used for determining targets of the city and county levels in water resource optimization configuration, providing objective functions and constraint conditions of an optimization configuration model, constructing the city and county level water resource optimization configuration model, and providing a hybrid strategy whale algorithm HSWOA to solve the model to form an initial game scheme set;
the final game scheme set generation module is used for screening non-dominant solutions in the initial game scheme set by adopting a rapid non-dominant sorting method to form a final game scheme set;
and the back-off discussion price counter-price module is used for introducing a back-off discussion price counter-price method, and the city level and the county level order the schemes in the final game scheme set according to the optimal water resource utilization target of the city level and the county level, and finally obtain the water resource optimal allocation scheme acceptable by both city and county levels through multiple rounds of negotiations.
9. An electronic device, the device comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform a water resource optimization configuration method for coordinating city and county two-stage water resource utilization targets as claimed in any one of claims 1-7.
10. A computer readable storage medium storing computer instructions which, when invoked, are operable to perform a water resource optimizing configuration method for coordinating a city and county two-stage water resource utilization target according to any one of claims 1-7.
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