CN111008741A - Multi-target accurate fertilization control parameter optimization method for water and fertilizer all-in-one machine - Google Patents
Multi-target accurate fertilization control parameter optimization method for water and fertilizer all-in-one machine Download PDFInfo
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Abstract
The invention discloses a multi-target accurate fertilization control parameter optimization method for a water and fertilizer integrated machine, which comprises the steps of firstly establishing a multi-target accurate fertilization control parameter optimization mathematical model; and then solving the Pareto optimal solution set of the mathematical model established in the step 1, namely solving the balanced combination between the precision control and the energy consumption of the fertilization control of the water and fertilizer all-in-one machine, and providing the optimal solution set for selecting the design scheme suitable for different actual conditions. The method adopts a Pareto optimal distributed grayish wolf multi-objective optimization algorithm to solve the multi-objective precise fertilization control parameter optimization problem, so as to adapt to different design practical requirements and improve the applicability of the water and fertilizer all-in-one machine in practical application.
Description
Technical Field
The invention belongs to the technical field of agriculture and garden irrigation, and relates to a multi-target precise fertilization control parameter optimization method, in particular to a multi-target precise fertilization control parameter optimization method for a water and fertilizer integrated machine.
Background
Important equipment of modern facility agriculture of liquid manure all-in-one machine provides the basis for realizing fertilization accurate control, improving modern agriculture's planting and management level. The initial construction cost of the water and fertilizer integrated equipment is high, the fertilization precision of the water and fertilizer integrated equipment is related to the flow, the fertilization precision is high when the flow is large due to the limitation of the fertilizer suction capacity of the Venturi fertilizer suction device, but the fertilization precision fluctuation is large when the flow is small. While the initial cost and the operation cost of the large-flow fertilizing equipment are higher. Therefore, the two indexes of high fertilization precision and low cost are contradictory to achieve simultaneously.
Therefore, the design parameters of the water and fertilizer integrated machine need to be adjusted by fully considering the actual factors when the water and fertilizer integrated machine is designed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-target precise fertilization control parameter optimization method for a water and fertilizer all-in-one machine.
The technical scheme adopted by the invention is as follows: a multi-target accurate fertilization control parameter optimization method for a water and fertilizer all-in-one machine is characterized by comprising the following steps:
step 1: establishing a multi-target accurate fertilization control parameter optimization mathematical model of the water and fertilizer integrated machine, and controlling the operation of a water pump by adjusting the flow and the lift of a fertilization pump of the water and fertilizer integrated machine;
wherein, according to the minimum control precision error and the minimum operation cost, the established multi-target mathematical model is as follows:
min F(x)＝(f1(x1，x2)，f2(x1，x2))
f1(x1，x2)＝min max|gt(x1，x2)-set|
f2(x1，x2)＝min(A·k(x1，x2)+B·p(x1，x2))
s.t：x＞0
wherein f is1(x1，x2) For the control accuracy error minimum function, f2(x1，x2) As a function of minimum running cost, gt(x1，x2) Is a fertilizer liquid concentration curve in the running process of the water and fertilizer integrated machine, wherein x1Is the flow of the liquid manure machine, x2Setting as a set concentration value for the water fertilizer machine lift; k (x)1，x2) Is a relation curve of the cost of the water and fertilizer integrated machine and the flow-lift, p (x)1，x2) Flow-lift operation cost curve, A, B are correction parameters;
step 2: solving the Pareto optimal solution set of the mathematical model established in the step 1 and performing multi-objective optimization, and obtaining a group of optimal solutions by solving the Pareto optimal solution set, wherein the group of optimal solutions comprises f1(x1，x2) Control accuracy error value and f2(x1，x2) The operation cost value is used for solving the balance combination between the precision control and the energy consumption of the fertilization control of the liquid manure machine, and an optimal solution set is provided for selecting a design scheme suitable for different practical conditions;
and step 3: and setting parameters of the water and fertilizer all-in-one machine according to the selected optimal solution.
In the prior art, the influence of flow and pressure on fertilization precision is not usually considered when designing the water and fertilizer integrated machine, and only the cost of the water and fertilizer integrated machine is usually considered. In the modern precise fertilization technology, the requirement on the fertilization precision of the water and fertilizer integrated machine is improved, and the more precise water and fertilizer integrated machine is needed to realize the precision of agricultural production. Therefore, when the integrated water and fertilizer machine of the irrigation system is designed, the fertilization precision and the cost of the water and fertilizer machine are considered at the same time, a multi-objective optimization model is established, a Pareto-based optimal multi-objective distribution gray wolf optimization algorithm is provided, a parallel clustering evolution mechanism is designed to solve the optimization model according to the fact that the control precision error and the cost are the minimum in the model, the problem that the model is nonlinear and high-dimensional and difficult to solve is solved well, a Pareto optimal solution set is obtained, namely a plurality of groups of optimal fertilization precision and cost combinations and corresponding flow and pressure parameters are obtained, and accurate and economic selection is provided for designers to design a head water and fertilizer integrated system.
According to the method, the fertilization precision and the cost of the water and fertilizer integrated machine are fully considered, the model is established, the model is solved through a Pareto optimal multi-objective distribution wolf optimization algorithm, the precision and the economy of the water and fertilizer integrated system are improved, support is provided for designers to design the head water and fertilizer integrated system, and the design level of the designers is improved.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
Referring to fig. 1, the method for optimizing the multi-target precise fertilization control parameter of the water and fertilizer integrated machine provided by the invention comprises the following steps:
step 1: establishing a multi-target accurate fertilization control parameter optimization mathematical model of the water and fertilizer integrated machine, namely controlling the operation of a water pump and the cost of the water pump by adjusting the flow and the lift of a fertilization pump of the water and fertilizer integrated machine;
wherein, according to the minimum control precision error and the minimum operation cost, the established multi-target mathematical model is as follows:
min F(x)＝(f1(x1，x2)，f2(x1，x2))
f1(x1，x2)＝min max|gt(x1，x2)-set|
f2(x1，x2)＝min(A·k(x1，x2)+B·p(x1，x2))
s.t：x＞0
wherein f is1(x1，x2) For the control accuracy error minimum function, f2(x1，x2) As a function of minimum running cost, f1(x1，x2) The method is related to the design structure of the water and fertilizer integrated machine, the relation between different pressures and flows under certain initial stock solution concentration and the concentration of fertilizer application controlled by the water and fertilizer integrated machine can be measured through an experimental method, a flow-pressure-concentration envelope line is formed, and the fertilizer application concentration under certain flow-pressure can be inquired through an interpolation method; gt(x1，x2) Is a fertilizer liquid concentration curve in the running process of the water and fertilizer integrated machine, wherein x1Is the flow of the liquid manure machine, x2Setting as a set concentration value for the water fertilizer machine lift; k (x)1，x2) Is a relation curve of the cost of the water and fertilizer integrated machine and the flow-lift, p (x)1，x2) Flow-lift operation cost curve, A, B are correction parameters;
step 2: solving the Pareto optimal solution set of the mathematical model established in the step 1 and performing multi-objective optimization, and obtaining a group of optimal solutions by solving the Pareto optimal solution set, wherein each optimal solution corresponds to one pair, and f1(x1，x2) Control accuracy error value and f2(x1，x2) The operation cost value can be selected by a designer through acceptable cost and corresponding control precision, and corresponding x is obtained1，x2Parameters, namely the flow and the pressure of the fertilizer applicator, namely the balanced combination between the precision control and the energy consumption of the fertilization control of the water fertilizer applicator is obtained and used as the basis of further pipe network design, and the type selection of the water pump can be carried out through the flow and pressure parameters, so that an optimal solution set is provided for selecting design schemes suitable for different actual conditions;
the specific implementation comprises the following substeps:
step 2.1: initializing flow parameter X ═ X1，i，x2，i，x3，i，x4，i..，xn，i](ii) a Wherein Xn，i(ii) a flow rate for the ith individual over the nth time period; initializing flow variables by adopting a random generation mode in a domain definition range;
step 2.2: calculating each individualAt f1(x1，x2) Target and f2(x1，x2) A function value on the target;
step 2.3: according to the definition of Pareto optimality, if the current evolution is the first generation evolution, analyzing the current population and finding out all Pareto optimal solutions; if not, carrying out first-generation evolution analysis on all individuals in the current population and the previous-generation Pareto optimal solution set, and finding out the Pareto optimal solution of the combined set;
step 2.4, clustering and grouping the current population, taking the solution in the Pareto solution set as the center, dividing the current population into M populations, respectively calculating the gray wolf parameters of α, β, delta and omega of each population by adopting a gray wolf optimization algorithm, and performing clustering and evolution to generate a next generation population;
the specific implementation comprises the following substeps:
step 2.4.1: clustering by using k-mean according to the Euclidean distance between individuals by taking the Pareto optimal solution in the Pareto optimal solution set as a center;
step 2.4.2, dividing a grouped wolf pack into 4 levels, wherein the wolf pack is respectively numbered α, β, delta and omega from top to bottom, the wolf pack is divided into four wolfs with different levels, wherein α is the optimal solution, β is the suboptimal solution, delta is the third solution, and other individuals are wolfs with omega levels, each wolf is an individual, and the mathematical expression of each wolf is as follows:
wherein, Xi(t) represents the ith wolf individual, N represents the dimension of the problem, t is the evolution algebra,
a value representing the ith wolf in the nth dimension;
step 2.4.3: designing and solving a multi-target accurate fertilization control parameter optimization algorithm of the water-fertilizer all-in-one machine by simulating a wolf flock hunting process, wherein the optimization algorithm comprises three steps of tracking and approaching a prey, tracking and surrounding the prey and attacking the prey; the concrete mathematical model of the approaching course of the wolf colony is as follows:
Xi(t+1)＝Xp(t)-D·|C·Xp(t)-Xi(t)|
wherein t is the current iteration number, Xp(t) is the position of the prey at time t, D.C.Xp(t)-Xi(t) | is the step length of hunting enclosure, D and C are coefficients for controlling the enclosure step length, respectively, and the coefficients are defined as follows:
D＝a·(2·r1-1)；
C＝2·r2；
where r1 and r2 are random numbers between [0,1], a is a control parameter, and decreases as the number of population iterations increases, i.e.:
in the formula tmaxIs the maximum iteration number;
step 2.4.4, updating the individuals at different levels of leadership in the wolf group, and updating the wolfs at other omega levels according to the positions of α, β and delta wolfs, wherein the specific formula is as follows:
wherein, Xi，α(t+1)、Xi，β(t+1)、Xi，δ(t+1)、Xi，ω(t +1) represent the positions of α, β, δ, ω wolfs at the t +1 th generation, respectively, the mathematical model describing the course of the wolf pack tracking around and attacking the prey;
step 2.5: merging all the groups, and according to the definition of the Pareto optimal solution, namely, if no other solution Pareto dominates the solution exists in any solution in the solution set, the solution is called the Pareto optimal solution, and the Pareto optimal solution set is updated according to the definition;
step 2.6: judging whether the program is finished, namely whether the set evolution algebra is reached(ii) a If yes, obtaining a balanced combination between precision control and energy consumption of fertilization control of the liquid manure fertilizing machine, obtaining a group of optimal solutions by solving a Pareto optimal solution set, wherein each optimal solution corresponds to one pair, and f1(x1，x2) Control accuracy error value and f2(x1，x2) The operation cost value can be balanced and combined by a designer through acceptable cost and corresponding control precision, and corresponding x is obtained1，x2If the parameters are the flow and the pressure of the fertilizer applicator, the process is ended; otherwise, jump to step 2.2.
And step 3: and setting parameters of the water and fertilizer all-in-one machine according to the optimal solution.
The parameter design of the liquid manure all-in-one machine determines the system operation cost, the fertilization precision and other performances, and the economical efficiency and the accuracy of the liquid manure machine can be effectively improved through a multi-objective optimization mode, and meanwhile, the robustness of the actual situation adapting to different requirements is improved. According to the method, a multi-objective optimization model is established by establishing a fertilization error minimum objective and a cost minimum objective model, a group of Pareto most solution sets are obtained by solving a multi-objective problem, and design parameters suitable for the water-in-one fertilizer machine can be selected from the Pareto most solution sets to meet actual requirements.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (2)
1. A multi-target accurate fertilization control parameter optimization method for a water and fertilizer all-in-one machine is characterized by comprising the following steps:
step 1: establishing a multi-target accurate fertilization control parameter optimization mathematical model of the water and fertilizer integrated machine, and controlling the operation of a water pump by adjusting the flow and the lift of a fertilization pump of the water and fertilizer integrated machine;
wherein, according to the minimum control precision error and the minimum operation cost, the established multi-target mathematical model is as follows:
min F(x)＝(f1(x1，x2)，f2(x1，x2))
f1(x1，x2)＝min max|gt(x1，x2)-set|
f2(x1，x2)＝min(A·k(x1，x2)+B·p(x1，x2))
s.t：x＞0
wherein f is1(x1，x2) For the control accuracy error minimum function, f2(x1，x2) As a function of minimum running cost, gt(x1，x2) Is a fertilizer liquid concentration curve in the running process of the water and fertilizer integrated machine, wherein x1Is the flow of the liquid manure machine, x2Setting as a set concentration value for the water fertilizer machine lift; k (x)1，x2) Is a relation curve of the cost of the water and fertilizer integrated machine and the flow-lift, p (x)1，x2) Flow-lift operation cost curve, A, B are correction parameters;
step 2: solving the Pareto optimal solution set of the mathematical model established in the step 1 and performing multi-objective optimization, and obtaining a group of optimal solutions by solving the Pareto optimal solution set, wherein the group of optimal solutions comprises f1(x1，x2) Control accuracy error value and f2(x1，x2) The operation cost value is used for solving the balance combination between the precision control and the energy consumption of the fertilization control of the liquid manure machine, and an optimal solution set is provided for selecting a design scheme suitable for different practical conditions;
and step 3: and setting parameters of the water and fertilizer all-in-one machine according to the selected optimal solution.
2. The method for optimizing the multi-target precise fertilization control parameters of the water and fertilizer all-in-one machine according to claim 1, wherein the concrete implementation of the step 2 comprises the following substeps:
step 2.1: initializing flow parameter X ═ X1,i,x2,i,x3,i,x4,i..,xn,i](ii) a Wherein xn,i(ii) a flow rate for the ith individual over the nth time period; initializing flow variables by adopting a random generation mode in a domain definition range;
step 2.2: calculating each individual at f1(x1，x2) Target and f2(x1，x2) A function value on the target;
step 2.3: according to the definition of Pareto optimality, if the current evolution is the first generation evolution, analyzing the current population and finding out all Pareto optimal solutions; if not, carrying out first-generation evolution analysis on all individuals in the current population and the previous-generation Pareto optimal solution set, and finding out the Pareto optimal solution of the combined set;
step 2.4, clustering and grouping the current population, taking the solution in the Pareto solution set as the center, dividing the current population into M populations, respectively calculating the gray wolf parameters of α, β, delta and omega of each population by adopting a gray wolf optimization algorithm, and performing clustering and evolution to generate a next generation population;
the specific implementation comprises the following substeps:
step 2.4.1: clustering by using k-mean according to the Euclidean distance between individuals by taking the Pareto optimal solution in the Pareto optimal solution set as a center;
step 2.4.2, dividing a grouped wolf pack into 4 levels, wherein the wolf pack is respectively numbered α, β, delta and omega from top to bottom, the wolf pack is divided into four wolfs with different levels, wherein α is the optimal solution, β is the suboptimal solution, delta is the third solution, and other individuals are wolfs with omega levels, each wolf is an individual, and the mathematical expression of each wolf is as follows:
wherein, Xi(t) represents the ith wolf individual, N represents the dimension of the problem, t is the evolution algebra,
a value representing the ith wolf in the nth dimension;
step 2.4.3: designing and solving a multi-target accurate fertilization control parameter optimization algorithm of the water-fertilizer all-in-one machine by simulating a wolf flock hunting process, wherein the optimization algorithm comprises three steps of tracking and approaching a prey, tracking and surrounding the prey and attacking the prey; the concrete mathematical model of the approaching course of the wolf colony is as follows:
Xi(t+1)＝Xp(t)-D·|C·Xp(t)-Xi(t)|
wherein t is the current iteration number, Xp(t) is the position of the prey at time t, D.C.Xp(t)-Xi(t) | is the step length of hunting enclosure, D and C are coefficients for controlling the enclosure step length, respectively, and the coefficients are defined as follows:
D＝a·(2·r1-1)；
C＝2·r2；
where r1 and r2 are random numbers between [0,1], a is a control parameter, and decreases as the number of population iterations increases, i.e.:
in the formula tmaxIs the maximum iteration number;
step 2.4.4, updating the individuals at different levels of leadership in the wolf group, and updating the wolfs at other omega levels according to the positions of α, β and delta wolfs, wherein the specific formula is as follows:
wherein, Xi，α(t+1)、Xi，β(t+1)、Xi，δ(t+1)、Xi，ω(t +1) represent the positions of α, β, δ, ω wolfs at the t +1 th generation, respectively, the mathematical model describing the course of the wolf pack tracking around and attacking the prey;
step 2.5: merging all the groups, and according to the definition of the Pareto optimal solution, namely, if no other solution Pareto dominates the solution exists in any solution in the solution set, the solution is called the Pareto optimal solution, and the Pareto optimal solution set is updated according to the definition;
step 2.6: judging whether the program is finished, namely whether the set evolution algebra is reached; if so, obtaining the balanced combination between the precision control and the energy consumption of the fertilization control of the water and fertilizer machine to obtain the corresponding x1，x2If the parameters are the flow and the pressure of the fertilizer applicator, the process is ended; otherwise, jump to step 2.2.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117751742A (en) * | 2024-02-22 | 2024-03-26 | 浙江园博景观建设有限公司 | Intelligent garden water and fertilizer irrigation optimization method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106698642A (en) * | 2016-12-29 | 2017-05-24 | 北京工业大学 | Multi-objective real-time optimization control method for sewage treatment process |
| PL415481A1 (en) * | 2015-12-23 | 2017-07-03 | Anioł Kazimierz Przedsiębiorstwo Produkcyjno-Usługowo-Handlowe AKPIL | Agricultural unit for fertilizing and sowing |
| CN107360775A (en) * | 2017-07-11 | 2017-11-21 | 中工武大设计研究有限公司 | The fertilising accuracy control method and its control system of a kind of water-fertilizer integral equipment |
| CN109214028A (en) * | 2017-07-07 | 2019-01-15 | 河北工业大学 | A kind of reverse osmosis boron removal seawater desalination system Multipurpose Optimal Method for considering environment and influencing |
| CN109496520A (en) * | 2018-11-27 | 2019-03-22 | 湖北工业大学 | A kind of multiple target water-fertilizer integral system rotation flow partition method |
| CN109657283A (en) * | 2018-11-27 | 2019-04-19 | 湖北工业大学 | A kind of fertigation pipe network optimization method based on Estimation of Distribution Algorithm |
| CN110131460A (en) * | 2019-06-11 | 2019-08-16 | 中国农业科学院农田灌溉研究所 | A kind of piping flow control valve of automatic adapted soil soil moisture content |
-
2019
- 2019-12-09 CN CN201911247387.XA patent/CN111008741B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| PL415481A1 (en) * | 2015-12-23 | 2017-07-03 | Anioł Kazimierz Przedsiębiorstwo Produkcyjno-Usługowo-Handlowe AKPIL | Agricultural unit for fertilizing and sowing |
| CN106698642A (en) * | 2016-12-29 | 2017-05-24 | 北京工业大学 | Multi-objective real-time optimization control method for sewage treatment process |
| CN109214028A (en) * | 2017-07-07 | 2019-01-15 | 河北工业大学 | A kind of reverse osmosis boron removal seawater desalination system Multipurpose Optimal Method for considering environment and influencing |
| CN107360775A (en) * | 2017-07-11 | 2017-11-21 | 中工武大设计研究有限公司 | The fertilising accuracy control method and its control system of a kind of water-fertilizer integral equipment |
| CN109496520A (en) * | 2018-11-27 | 2019-03-22 | 湖北工业大学 | A kind of multiple target water-fertilizer integral system rotation flow partition method |
| CN109657283A (en) * | 2018-11-27 | 2019-04-19 | 湖北工业大学 | A kind of fertigation pipe network optimization method based on Estimation of Distribution Algorithm |
| CN110131460A (en) * | 2019-06-11 | 2019-08-16 | 中国农业科学院农田灌溉研究所 | A kind of piping flow control valve of automatic adapted soil soil moisture content |
Non-Patent Citations (1)
| Title |
|---|
| 吴艳春: "高效节水灌溉工程的优化设计要点探析", 《南方农业》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117751742A (en) * | 2024-02-22 | 2024-03-26 | 浙江园博景观建设有限公司 | Intelligent garden water and fertilizer irrigation optimization method and system |
| CN117751742B (en) * | 2024-02-22 | 2024-04-19 | 浙江园博景观建设有限公司 | Intelligent garden water and fertilizer irrigation optimization method and system |
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| CN111008741B (en) | 2022-06-07 |
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