CN115659641B - Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method - Google Patents
Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method Download PDFInfo
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Abstract
The invention provides a water engineering multi-objective optimization scheduling method for water bloom prevention and control, which comprises the following steps: firstly, constructing a multi-objective function oriented to ecological, water quantity and power generation requirements, and using reservoir delivery flow of each period as a decision variable to orient a water bloom prevention and control water engineering joint scheduling model; simulating water bloom water quality diffusion migration by using a one-dimensional convection diffusion equation so as to evaluate ecological, water quantity and power generation scheduling objective functions; solving a scheduling model by adopting a multi-objective ant-lion optimization algorithm, obtaining a pareto solution set which can cooperatively optimize water bloom prevention and control, water quantity and power generation scheduling objectives, and providing a water engineering multi-objective optimization scheduling scheme; and step four, arranging a water body denitrification and dephosphorization device in a downstream water body eutrophication area, and providing engineering measures for water bloom prevention and control.
Description
Technical Field
The invention belongs to the technical field of reservoir dispatching, and particularly relates to a water engineering multi-objective optimization dispatching method for water bloom prevention and control.
Background
Bloom is a natural ecological phenomenon of mass propagation of algae in fresh water, is a characteristic of eutrophication of water, is commonly found in water with low flow rates such as lakes, reservoirs and the like, and has serious adverse effects on ecosystems, social and economic development and human health. In recent years, important water areas such as the middle and downstream of Han river, the Taihu lake, the Yunnan pond and the like in China have multiple times of water bloom, and many scholars develop research work around the aspects of water bloom cause, control and prevention and control and the like. However, water bloom prevention and control researches are mostly carried out in lakes at present, the combination of water bloom prevention and control and water engineering scheduling is lacking, and meanwhile, the water engineering scheduling technology (non-engineering measures) and the denitrification and dephosphorization device (engineering measures) are too little in fusion, so that the water bloom prevention and control effect is difficult to reach the expected target. The technical difficulties and challenges of water engineering scheduling include: (1) the difficulty of realizing the cooperative scheduling of water bloom prevention and control, water quantity allocation and power generation is high, and the comprehensive benefits of the cooperative scheduling of water engineering are difficult to fully develop; (2) the water engineering dispatching technology and the denitrification and dephosphorization device are lack to be fused.
Disclosure of Invention
The invention aims to solve the problems, and aims to provide a water engineering multi-objective optimization scheduling method and a water bloom treatment device for water bloom prevention and control, which are used for providing a water engineering scheduling method capable of cooperatively optimizing water bloom prevention and control, water volume allocation and power generation scheduling targets by deep fusion of water bloom water quality diffusion migration simulation and reservoir scheduling by applying theoretical methods such as hydrology, bionic evolution algorithm and the like; by arranging the denitrification and dephosphorization device, the self-cleaning capability of the water body is improved, and organic pollutants are degraded. The invention can provide theoretical basis and technical support for scientific formulation of reservoir dispatching scheme related to water bloom prevention and control in watershed.
In order to achieve the above object, the present invention adopts the following scheme:
the invention provides a water engineering multi-objective optimization scheduling method for water bloom prevention and control, which comprises the following steps:
step one, constructing a scheduling model: constructing a multi-objective function oriented to ecological, water quantity and power generation requirements, and a water engineering joint scheduling model oriented to water bloom prevention and control by taking reservoir outlet flow of each period as a decision variable;
step two, water bloom water quality diffusion migration simulation: simulating water bloom water quality diffusion migration by using a one-dimensional convection diffusion equation so as to evaluate ecological, water quantity and power generation scheduling objective functions;
step three, generating a scheduling scheme: solving a scheduling model by adopting a multi-objective ant lion optimization algorithm, obtaining a pareto solution set which can cooperatively optimize water bloom prevention and control, water quantity and power generation scheduling objectives, and providing a water engineering multi-objective optimization scheduling scheme;
and step four, arranging a water body denitrification and dephosphorization device in a downstream water body eutrophication area, and providing engineering measures for water bloom prevention and control.
Preferably, in the first step, the multi-objective function refers to a scheduling objective facing ecological, water quantity and power generation requirements; the minimized ecological objective function is as follows:
wherein F is 1 Representing ecological objectives, Y l,m (t) a value representing an mth index of the t period of the first section; y is Y l,m * The threshold value of the water bloom outbreak of the mth index of the first section is represented, L is the total section number, T is the total period number, and M is the total index number;representing a step function.
Preferably, the step function is defined as follows:
(1) for the forward index, namely, the higher the index value is, the lower the water bloom outbreak probability is, and the forward index mainly comprises the ecological section flow index:
wherein the ecological section flow is related to reservoir delivery flow in each period of water engineering, namely the ecological section flow is represented by (3):
in which Q l (t) represents the ecological section flow rate of the first section t period; q (Q) jin (t) represents the discharge flow of the adjacent nearest reservoir upstream of the first section in the period t; q j Representing the interval inflow flow of the jth tributary between the first section and the upstream adjacent nearest reservoir;
(2) for negative indexes, namely, the higher the index value is, the higher the water bloom outbreak probability is, and the indexes mainly comprise total nitrogen TN and total nitrogen TP:
preferably, the brix schedule objective functions of maximizing the water storage amount and maximizing the power generation amount are respectively as follows:
wherein F is 2 And F 3 Respectively storing water and generating power scheduling targets; v (V) i (t+1) is the storage capacity of the ith reservoir at the time t+1; v (V) i max The maximum reservoir capacity allowed in the ith reservoir dispatching period can be generally taken as a reservoir capacity value corresponding to the normal high water level of the reservoir; k (k) i Representing the ith reservoir output coefficient; q (Q) fd,i (t) is the power generation flow of the ith reservoir at the moment t; h i (t) is the power generation water head of the ith reservoir at the moment t; Δt is the calculated period length.
Preferably, in the first step, the reservoir water storage schedule needs to satisfy the following constraint conditions: reservoir water quantity balance constraint, water level amplitude constraint, reservoir water level boundary constraint, delivery flow constraint, output constraint, water taking node water quantity calculation, water using node water quantity calculation, backwater node water quantity calculation and avionics junction constraint.
Preferably, in the second step, a one-dimensional convection diffusion module is used to simulate the water bloom water quality diffusion and migration process so as to evaluate the ecological, water quantity and power generation scheduling objective function, wherein the one-dimensional convection diffusion equation is as follows:
wherein, C is the concentration of pollutants, mg/L; d is the diffusion coefficient of the pollutant; a is the cross-sectional water passing area, m 2 The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, m 3 The method comprises the steps of carrying out a first treatment on the surface of the K is degradation coefficient, s -1 ；C 2 Is the concentration of source and sink, mg/L; q is the point source flow of the pollutant, m 3 S; x is a space coordinate, m; t is the time step, s.
Preferably, in the second step, the following constraint needs to be satisfied:
initial concentration of water quality index
Wherein C is i,n (t) and C s,n (t) represents the nth water quality index concentration of the ith reservoir and the nth water area for water withdrawal respectively;and->The initial concentrations of the nth water quality indexes of the ith reservoir and the nth water area are respectively shown.
Preferably, in the third step, the solving of the scheduling model by using the multi-objective ant lion optimization algorithm is performed as follows:
(1) data initialization: initializing iteration times, population size and reservoir delivery flow in each period, wherein the reservoir delivery flow in each period corresponds to the position of an ant lion in an ant lion optimization algorithm;
(2) determining elite ant lion: randomly initializing the positions of ants and ant lions within the boundary range of a solution space, calculating the fitness value of all the populations, sequencing the ant lions in descending order, and naming the ant lion with the best fitness value in the ant lion population as elite ant lion;
(3) randomly matching each ant with an ant lion by roulette, updating upper and lower boundary values of a walk range according to the matched ant lion positions, enabling the ants to walk randomly near the selected ant lion and near elite ant lion respectively, and taking an average value as the initial position of the next generation of the ants;
(4) in each iteration, the ant and ant lion population is assigned an initial value again, the adaptability values of the ant and ant lion population are recalculated, the position 50% before the adaptability value is taken as the position of the new generation of ant lion, and the ant lion position with the best adaptability value is taken as the position of the new generation of elite ant lion;
(5) judging termination criteria: if the current iteration number is smaller than the maximum iteration number t max Repeating the step (3); otherwise, the calculation is terminated and the final result is output.
Preferably, in the fourth step, the water body denitrification and dephosphorization device is arranged in a downstream water body eutrophication area to provide engineering measures for water bloom prevention and control, and the treatment equipment comprises a mobile water quality purification device and a fixed denitrification and dephosphorization system.
Compared with the prior art, the application has the following beneficial effects:
1. from the perspective of water bloom prevention and control, a water engineering multi-objective optimization scheduling model for water bloom prevention and control is constructed and efficiently solved based on a water bloom water quality diffusion migration simulation method by applying theoretical methods such as hydrology and a bionic evolution algorithm, and the water bloom prevention and control, water yield allocation and power generation scheduling targets can be cooperatively optimized through deep fusion of the water bloom water quality diffusion migration simulation and reservoir scheduling.
2. From the perspective of water bloom treatment, when a water bloom event occurs, the self-cleaning capability of the water body is improved and organic pollutants are degraded by arranging a denitrification and dephosphorization device.
Drawings
Fig. 1 is a flowchart of a water engineering multi-objective optimization scheduling method for water bloom prevention and control according to an embodiment of the invention.
Fig. 2 is a flowchart for solving a water engineering multi-objective optimization scheduling model for water bloom prevention and control according to an embodiment of the present invention.
FIG. 3 is a diagram of a mobile water purification apparatus and a diagram of a stationary denitrification and dephosphorization treatment system according to an embodiment of the present invention.
Fig. 4 is a Pareto solution set distribution diagram of the average concentration of water quality and the lowest flow rate of the water bloom prevention and control section according to the embodiment of the invention.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the attached drawings, examples and comparative examples.
< example >
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be described below in conjunction with the embodiments of the present invention. Taking two sections of a river reservoir and a downstream as an example, a water engineering multi-objective optimization scheduling method facing water bloom prevention and control is adopted to realize multi-objective balanced optimization of water bloom prevention and control, water supply, water supplement and power generation. As shown in fig. 1, the method comprises the following steps:
step one, constructing a scheduling model: and constructing a water engineering joint scheduling model for water bloom prevention and control, which is oriented to multiple objective functions of ecology, water quantity and power generation requirements and takes the delivery flow of each period as a decision variable, as shown in (a) of fig. 1.
Step two, water bloom water quality diffusion migration simulation: and simulating water bloom water quality diffusion migration by using a one-dimensional convection diffusion module so as to evaluate ecological, water quantity and power generation scheduling objective functions.
Step three, generating a scheduling scheme: and solving a scheduling model by adopting a multi-objective ant lion optimization algorithm to obtain a pareto solution set which can cooperatively optimize water bloom prevention and control, water quantity and power generation scheduling objectives, and providing a water engineering multi-objective optimization scheduling scheme, as shown in (b) in fig. 1.
As shown in fig. 2, data initialization is performed according to (1): determining the initial population quantity of ants and ant lions; (2) determining elite ant lion; (3) updating the ant position; (4) updating the ant lion position and the elite ant lion position; (5) judging the basic flow of the termination criterion, solving a water bloom prevention and control oriented water engineering joint scheduling model, and acquiring a pareto solution set to generate a scheduling scheme to guide reservoir water storage scheduling and risk decision.
And step four, when the water bloom event occurs, arranging a water body denitrification and dephosphorization device in the water area, wherein the device can be withdrawn from operation after the water quality is improved as shown in figure 3, and gradually forming an aquatic ecological balance system.
In this embodiment, in the first step, the multiple objective function refers to a scheduling objective for ecology, water quantity, and power generation requirements; the minimized ecological objective function is as follows:
wherein F is 1 Representing ecological objectives, Y l,m (t) a value representing an mth index of the t period of the first section; y is Y l,m * The threshold value of the water bloom outbreak of the mth index of the first section is represented, L is the total section number, T is the total period number, and M is the total index number;representing a step function.
Further, the step function is defined as follows:
(1) for the forward index, namely, the higher the index value is, the lower the water bloom outbreak probability is, and the forward index mainly comprises the ecological section flow index:
wherein the ecological section flow is related to reservoir delivery flow in each period of water engineering, namely the ecological section flow is represented by (3):
in which Q l (t) represents the ecological section flow rate of the first section t period; q (Q) jin (t) represents the discharge flow of the adjacent nearest reservoir upstream of the first section in the period t; q j Representing the interval inflow flow of the jth tributary between the first section and the upstream adjacent nearest reservoir;
(2) for negative indexes, namely, the higher the index value is, the higher the water bloom outbreak probability is, and the indexes mainly comprise total nitrogen TN and total nitrogen TP:
preferably, the brix schedule objective functions of maximizing the water storage amount and maximizing the power generation amount are respectively as follows:
wherein F is 2 And F 3 Respectively storing water and generating power scheduling targets; v (V) i (t+1) is the storage capacity of the ith reservoir at the time t+1; v (V) i max The maximum reservoir capacity allowed in the ith reservoir dispatching period can be generally taken as a reservoir capacity value corresponding to the normal high water level of the reservoir; k (k) i Representing the ith reservoir output coefficient; q (Q) fd,i (t) is the power generation flow of the ith reservoir at the moment t; h i (t) is the power generation water head of the ith reservoir at the moment t; Δt is the calculated period length.
In the first step, reservoir water storage scheduling needs to meet the following constraint conditions: reservoir water quantity balance constraint, water level amplitude constraint, reservoir water level boundary constraint, delivery flow constraint, output constraint, water taking node water quantity calculation, water using node water quantity calculation, backwater node water quantity calculation and avionics junction constraint.
Balance constraint of reservoir water quantity
V i (t+1)＝V i (t)+[I i (t)-O i (t)]Δt (7)
Wherein V is i (t+1) is the storage capacity of the ith reservoir at the time t+1; v (V) i (t) is the storage capacity of the ith reservoir at the time t; i i (t) represents the flow rate of the ith reservoir in the warehouse at the t-th period; o (O) i (t) represents the discharge flow rate of the ith reservoir in the t period.
Water level constraint
Wherein Z is i (t) represents the reservoir level at time t of the ith reservoir;and->The lower limit and the upper limit of the output of the ith reservoir are respectively taken as the original design water storage scheduling line and the stage flood limit water level.
Water level amplitude constraint
|Z i (t+1)-Z i (t)|≤δ i (9)
In delta i Is the water level amplitude threshold value of the ith reservoir at the adjacent moment.
Reservoir level boundary constraint
In the method, in the process of the invention,water for indicating the beginning of the reservoir schedule periodBit value.
Delivery flow constraints
In the method, in the process of the invention,and->The lower limit and the upper limit of the output of the ith reservoir are respectively determined by ecological requirements and navigation requirements, and the upper limit is determined by the drainage capacity.
Force constraint
P i min (t)≤P i (t)≤P i max (t) (12)
P i (t)＝k i Q fd,i (t)·H i (t) (13)
Wherein P is i (t) represents the output value of the ith reservoir hydropower station in the ith period, P i (t)＝k i Q fd,i (t)·H i (t)；P i min (t) represents a lower limit of the output of the ith reservoir hydropower station at the ith period; p (P) i max And (t) represents the upper limit of the output of the ith reservoir hydropower station in the ith period, and is determined by comprehensively considering the rated output, the blocked capacity, the peak regulation requirement and the like of the unit.
Water intake node water quantity calculation
R s (t)＝Q sy,s (t)+Q qj,s (t)-W qu,s (t) (14)
W qu,s (t)≤W xu,s (t) (15)
Wherein R is j (t) represents the flow in the river after taking water in the t period of the s water area; q (Q) sy,s (t) and Q qj,s (t) represents the inflow of water and interval upstream of the t-th period of the s-th water-using zone, respectively; w (W) qu,s (t) and W xu,s (t) represents the water intake and water demand, respectively, of the s-th water zone in the t-th period.
Water consumption calculation at water node
W tui,s (t)＝(1-α)W qu,s (t) (16)
In which W is tui,s (t) represents the water withdrawal amount of the water zone for the s-th time period; alpha represents the average water consumption rate of the calculated basin.
Water quantity calculation for backwater node
Q h,s (t)＝R s (t)+W tui,s (t) (17)
In which Q h,s And (t) represents the catchment node flow of the ith period of the ith water area.
Air armature new york bundle
Generally, a navigation electricity hub is built at the downstream of the reservoir, the functions of shipping and electricity generation are mainly exerted, the regulating capacity is small, and for such engineering constraint, simulation is carried out according to the conventional scheduling regulation, as follows:
O hd,k (t)＝ψ hd,k [I hd,k (t),Z hd,k (t)] (18)
wherein O is hd,k (t)，I hd,k (t) and Z hd,k (t) represents outflow, inflow and water level, respectively, of the kth aeronautical armature button during the t-th period; psi phi type hd,k The normal scheduling procedure for the kth avionics hub is indicated.
In the second step, a one-dimensional convection diffusion module is used for simulating the water bloom water quality diffusion migration process so as to evaluate the ecological, water quantity and power generation scheduling objective function, wherein the one-dimensional convection diffusion equation is as follows:
wherein, C is the concentration of pollutants, mg/L; d is the diffusion coefficient of the pollutant; a is the cross-sectional water passing area, m 2 The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, m 3 The method comprises the steps of carrying out a first treatment on the surface of the K is degradation coefficient, s -1 ；C 2 Is the concentration of source and sink, mg/L; q is the point source flow of the pollutant, m 3 S; x is a space coordinate, m; t is the time step, s.
Preferably, in the second step, the following constraint needs to be satisfied:
initial concentration of water quality index
Wherein C is i,n (t) and C s,n (t) represents the nth water quality index concentration of the ith reservoir and the nth water area for water withdrawal respectively;and->The initial concentrations of the nth water quality indexes of the ith reservoir and the nth water area are respectively shown.
In the third step, the multi-objective ant lion optimization algorithm is used for solving the scheduling model, and the method comprises the following steps:
(1) data initialization: initializing iteration times, population size and reservoir delivery flow in each period, wherein the reservoir delivery flow in each period corresponds to the position of an ant lion in an ant lion optimization algorithm;
(2) determining elite ant lion: randomly initializing the positions of ants and ant lions within the boundary ranges of solution spaces (7) - (18), (20) and (21), calculating the fitness values of all the species groups according to formulas (1) - (6) and (19), sequencing the species groups according to descending order, and naming the ant lion with the best fitness value in the ant lion species group as elite ant lion;
(3) randomly matching each ant with an ant lion by roulette, updating upper and lower boundary values of a walk range according to the matched ant lion positions, enabling the ants to walk randomly near the selected ant lion and near elite ant lion respectively, and taking an average value as the initial position of the next generation of the ants;
(4) in each iteration, the ant and ant lion population is assigned an initial value again, the adaptability values of the ant and ant lion population are recalculated, the position 50% before the adaptability value is taken as the position of the new generation of ant lion, and the ant lion position with the best adaptability value is taken as the position of the new generation of elite ant lion;
(5) judging termination criteria: if the current iteration number is smaller than the maximum iteration number t max Repeating the step (3); otherwise, the calculation is terminated and the final result is output.
In the fourth step, a water body denitrification and dephosphorization device is arranged in a downstream water body eutrophication area to provide engineering measures for water bloom prevention and control, and the treatment equipment comprises a mobile water quality purification device and a fixed denitrification and dephosphorization system.
As shown in fig. 4, as the average concentration of the sections TP and TP is reduced, the power generation amount of the hydropower station is increased, and the residual water amount of the reservoir volume is reduced; the minimum flow of the section and the generated energy show a cooperative increasing relation, and the increase of the minimum flow and the generated energy can lead to the decrease of the residual water quantity of the reservoir capacity. In addition, with the conventional scheduling result as a dividing line, the Pareto solution set may be divided into A, B two areas. Obviously, the generating capacity of the Pareto solution set in the area A, the average concentration and the minimum flow of the sections TP and TP are inferior to those of the conventional dispatching solution, but the benefit of the residual reservoir capacity of the reservoir is better; and the section TP, TP average concentration, minimum flow, hydropower station power generation amount and reservoir capacity remaining benefits of the Pareto solution set of the zone B are all superior to those of the conventional scheduling solution. The multi-target Pareto solution set obtained by the embodiment can aim at ecological, water quantity and power generation targets, and the comprehensive benefits of water engineering joint scheduling are improved.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions in a similar manner without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (5)
1. The water bloom prevention and control oriented water engineering multi-objective optimization scheduling method is characterized by comprising the following steps of:
step one, constructing a scheduling model: constructing a multi-objective function oriented to ecological, water quantity and power generation requirements, and a water engineering joint scheduling model oriented to water bloom prevention and control by taking reservoir outlet flow of each period as a decision variable;
in the first step, the multi-objective function refers to a scheduling objective facing ecological, water quantity and power generation requirements; the minimized ecological objective function is as follows:
wherein F is 1 Representing ecological objectives, Y l,m (t) a value representing an mth index of the t period of the first section; y is Y l,m * The threshold value of the water bloom outbreak of the mth index of the first section is represented, L is the total section number, T is the total period number, and M is the total index number;representing a step function;
the step function is defined as follows:
(1) for a forward index, namely, the higher the index value is, the lower the water bloom outbreak probability is, and the forward index comprises an ecological section flow index:
the ecological section flow is related to reservoir delivery flow in each period of water engineering, namely the ecological section flow is expressed as:
in which Q l (t) represents the ecological section flow rate of the first section t period; q (Q) jin (t) represents the discharge flow of the adjacent nearest reservoir upstream of the first section in the period t; q j The inflow flow rate of the section of the jth branch between the first section and the upstream adjacent nearest reservoir is represented, and J is the total number of branches;
(2) for negative indexes, namely, the higher the index value is, the higher the water bloom outbreak probability is, and the indexes comprise total nitrogen TN and total nitrogen TP:
the brix scheduling objective functions of maximizing the water storage amount and maximizing the power generation amount are respectively as follows:
wherein F is 2 And F 3 Respectively storing water and generating power scheduling targets; v (V) i (t+1) is the storage capacity of the ith reservoir at the time t+1;the maximum reservoir capacity allowed in the ith reservoir dispatching period can be taken as a reservoir capacity value corresponding to the normal high water level of the reservoir; k (k) i Representing the ith reservoir output coefficient; q (Q) fd,i (t) is the power generation flow of the ith reservoir at the moment t; h i (t) is the power generation water head of the ith reservoir at the moment t; delta T is calculated time period length, N is reservoir number, and T is time;
step two, water bloom water quality diffusion migration simulation: simulating water bloom water quality diffusion migration by using a one-dimensional convection diffusion equation so as to evaluate ecological, water quantity and power generation scheduling objective functions;
step three, generating a scheduling scheme: solving a scheduling model by adopting a multi-objective ant lion optimization algorithm, obtaining a pareto solution set which can cooperatively optimize water bloom prevention and control, water quantity and power generation scheduling objectives, and providing a water engineering multi-objective optimization scheduling scheme; in the third step, the multi-objective ant lion optimization algorithm is used for solving the scheduling model, and the method comprises the following steps:
(1) data initialization: initializing iteration times, population size and reservoir delivery flow in each period, wherein the reservoir delivery flow in each period corresponds to the position of an ant lion in an ant lion optimization algorithm;
(2) determining elite ant lion: randomly initializing the positions of ants and ant lions within the boundary range of a solution space, calculating the fitness value of all the populations, sequencing the ant lions in descending order, and naming the ant lion with the best fitness value in the ant lion population as elite ant lion;
(3) randomly matching each ant with an ant lion by roulette, updating upper and lower boundary values of a walk range according to the matched ant lion positions, enabling the ants to walk randomly near the selected ant lion and near elite ant lion respectively, and taking an average value as the initial position of the next generation of the ants;
(4) in each iteration, the ant and ant lion population is assigned an initial value again, the adaptability values of the ant and ant lion population are recalculated, the position 50% before the adaptability value is taken as the position of the new generation of ant lion, and the ant lion position with the best adaptability value is taken as the position of the new generation of elite ant lion;
(5) judging termination criteria: if the current iteration number is smaller than the maximum iteration number t max Repeating the step (3); otherwise, stopping calculation and outputting a final result;
and step four, arranging a water body denitrification and dephosphorization device in a downstream water body eutrophication area, and providing engineering measures for water bloom prevention and control.
2. The water bloom prevention and control oriented water engineering multi-objective optimization scheduling method as claimed in claim 1, wherein the method is characterized by comprising the following steps: in the first step, reservoir water storage scheduling needs to meet the following constraint conditions: reservoir water quantity balance constraint, water level amplitude constraint, reservoir water level boundary constraint, delivery flow constraint, output constraint, water taking node water quantity calculation, water using node water quantity calculation, backwater node water quantity calculation and avionics junction constraint.
3. The water bloom prevention and control oriented water engineering multi-objective optimization scheduling method as claimed in claim 1, wherein the method is characterized by comprising the following steps: in the second step, a one-dimensional convection diffusion module is used for simulating the water bloom water quality diffusion migration process so as to evaluate the ecological, water quantity and power generation scheduling objective function, wherein the one-dimensional convection diffusion equation is as follows:
wherein, C is the concentration of pollutants, mg/L; d is the diffusion coefficient of the pollutant; a is the cross-sectional water passing area, m 2 The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, m 3 The method comprises the steps of carrying out a first treatment on the surface of the K is degradation coefficient, s -1 ；C 2 Is the concentration of source and sink, mg/L; q is the point source flow of the pollutant, m 3 S; x is a space coordinate, m; t is the time step, s.
4. The water bloom prevention and control oriented water engineering multi-objective optimization scheduling method as claimed in claim 1, wherein the method is characterized by comprising the following steps: in the second step, the following constraint conditions are required to be satisfied:
initial concentration of water quality index
Wherein C is i,n (t) and C s,n (t) represents the nth water quality index concentration of the ith reservoir and the nth water area for water withdrawal respectively;and->The initial concentrations of the nth water quality indexes of the ith reservoir and the nth water area are respectively shown.
5. The water bloom prevention and control oriented water engineering multi-objective optimization scheduling method as claimed in claim 1, wherein the method is characterized by comprising the following steps:
in the fourth step, a water body denitrification and dephosphorization device is arranged in a downstream water body eutrophication area to provide engineering measures for water bloom prevention and control, and the treatment equipment comprises a mobile water quality purification device and a fixed denitrification and dephosphorization system.
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