CN112868435A - NSGA-II-based blueberry greenhouse light and temperature coordination optimization method - Google Patents

NSGA-II-based blueberry greenhouse light and temperature coordination optimization method Download PDF

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CN112868435A
CN112868435A CN202110048899.4A CN202110048899A CN112868435A CN 112868435 A CN112868435 A CN 112868435A CN 202110048899 A CN202110048899 A CN 202110048899A CN 112868435 A CN112868435 A CN 112868435A
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CN112868435B (en
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徐立鸿
徐赫
蔚瑞华
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Tongji University
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
    • A01G9/20—Forcing-frames; Lights, i.e. glass panels covering the forcing-frames
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • A01G7/04—Electric or magnetic or acoustic treatment of plants for promoting growth
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/12—Timing analysis or timing optimisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
    • Y02P60/14—Measures for saving energy, e.g. in green houses

Abstract

The invention relates to a NSGA-II-based blueberry greenhouse light and temperature coordination optimization method, which comprises the following steps: 1) dividing regulation and control time periods of the blueberry greenhouse needing light supplement and temperature control; 2) acquiring the average value of the environmental factors five minutes before the unit regulation time period; 3) constructing a greenhouse energy consumption prediction model and a blueberry net photosynthetic rate prediction model; 4) establishing a greenhouse light-temperature coordination optimization model by taking the maximum net photosynthetic rate of the blueberry crops and the minimum greenhouse energy consumption in a unit regulation time period as optimization targets; 5) solving a greenhouse light-temperature coordination optimization model by adopting an NSGA-II multi-target genetic algorithm; 6) and selecting an optimal light-temperature coordination scheme from the non-inferior solution set by adopting a decision method based on optimal benefit improvement. Compared with the prior art, the blueberry greenhouse light supplementing and temperature controlling method has the advantages of reasonable division of light supplementing and temperature controlling time periods, improvement of economic benefits, universality and expansibility of blueberry greenhouse production and the like, provides theoretical guidance and decision support for improvement of blueberry greenhouse production benefits, and ensures high yield and energy conservation of blueberry planting in the greenhouse.

Description

NSGA-II-based blueberry greenhouse light and temperature coordination optimization method
Technical Field
The invention relates to the field of greenhouse light and temperature optimization control, in particular to a blueberry greenhouse light and temperature coordination optimization method based on NSGA-II.
Background
Blueberry, also known as cowberry, is a fresh fruit crop rich in various nutrient elements such as anthocyanin and anthocyanin, has high nutritional value and medical value, and is evaluated as one of 5 major health foods by the Food and Agriculture Organization (FAO) of the United nations. In recent years, the market of blueberries is continuously expanded, and the economic benefit is considerable. Blueberries are annual growth crops, the growth period of flowers and fruits of the blueberries is usually 2-7 months, and proper temperature and sufficient illumination are required in the growth period. Multiple studies show that temperature and photon flux density have great influence on blueberry photosynthesis, and the photosynthesis provides the most basic carbohydrate for crop growth and can improve the quality and yield of fruits. Therefore, in the actual greenhouse production, it is necessary to establish a light-temperature environment suitable for blueberry growth through regulation and control, and the premise of reasonable regulation and control is to obtain the illumination and temperature target values capable of effectively improving the photosynthesis efficiency of the blueberries, so that a basis is provided for the greenhouse regulation and control.
At present, the target value setting of the light and temperature regulation and control of greenhouse production is still generally based on expert suggestions or traditional planting experiences, on one hand, the method is easily influenced by artificial subjective factors, and sufficient theoretical support cannot be provided for optimization; on the other hand, the mode cannot fully consider the synergistic influence of the illumination temperature on the photosynthesis of the crops, and cannot comprehensively consider the energy consumption of greenhouse production and the requirements of crop growth. Therefore, the light-temperature target value capable of ensuring the production benefit is obtained by constructing a blueberry greenhouse light-temperature coordination optimization method, and is a crucial link for greenhouse production regulation and control.
In recent years, many scholars have made relevant researches in the field of coordination and optimization of light and temperature of greenhouse crops, and the main methods can be summarized into two methods:
(1) and (3) taking the photon flux density as input, taking the net photosynthetic rate of the crops as output, obtaining a prediction model of the net photosynthetic rate of the crops through modeling, and directly using the maximum net photosynthetic rate or a photosynthetic saturation point as a light supplement threshold.
(2) The first maximum curvature point is obtained from the light response curve in a proper temperature interval, the point is a characteristic point of which the influence degree of photon flux density on net photosynthetic rate is from strong to weak, and the point is used as a light supplement threshold value in actual regulation.
The two methods have certain practicability, but the light-temperature coordination is not achieved, and the greenhouse production cost is not considered in the two methods, so that the energy consumption which is easy to increase in greenhouse production is not used for effectively improving the net photosynthetic rate of crops, the energy waste is caused, and the economic benefit of greenhouse production cannot be guaranteed.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a blueberry greenhouse light and temperature coordination optimization method based on NSGA-II.
The purpose of the invention can be realized by the following technical scheme:
a blueberry greenhouse light and temperature coordination optimization method based on NSGA-II comprises the following steps:
1) dividing regulation and control time intervals of a blueberry greenhouse needing supplementary lighting and temperature control by taking one hour as a unit;
2) for each unit regulation and control time interval, acquiring an average value of the environmental factors five minutes before the unit regulation and control time interval as initial input;
3) constructing a greenhouse energy consumption prediction model and a blueberry net photosynthetic rate prediction model;
4) taking the maximum net photosynthetic rate of the blueberry crops and the minimum greenhouse energy consumption in a unit regulation and control time period as optimization targets, determining constraint conditions according to the average value of the environmental factors of five minutes before the regulation and control time period, and establishing a greenhouse light-temperature coordination optimization model;
5) solving a greenhouse light-temperature coordination optimization model by adopting an NSGA-II multi-target genetic algorithm to obtain a group of non-inferior solution sets meeting the requirements;
6) and selecting a light-temperature coordination optimal scheme from the non-inferior solution set by adopting a decision method based on optimal benefit improvement, and carrying out actual regulation and control by using the light-temperature coordination optimal scheme.
In the step 2), the environmental factors specifically include the indoor temperature of the greenhouse, the outdoor temperature of the greenhouse and the indoor illumination intensity of the greenhouse.
In the step 3), the expression of the greenhouse energy consumption prediction model is as follows:
QControl＝QTemp+QLight
wherein Q isControlFor regulating total energy consumption in greenhouses, QTempFor controlling energy consumption, Q, of the greenhouse temperatureLightSupplementing light energy consumption for the greenhouse.
Controlling energy consumption Q for greenhouse temperatureTempAt the time of temperature rise, the expression is:
Figure BDA0002898249220000021
when the temperature is reduced, the expression is as follows:
Figure BDA0002898249220000031
wherein Q isradinThe energy added by solar radiation for a greenhouse, U is the heat loss coefficient including convection, conduction, heat exchange and long-wave radiation, TaimIs a target value of the indoor temperature of the greenhouse, ToutIs the outdoor temperature, [ t ]0,tf]Is a unit regulation time interval.
Energy consumption Q for greenhouse light supplementLightThe expression is as follows:
Figure BDA0002898249220000032
where ρ isPARAs a conversion factor of the energy of illumination, IaimFor the target value of indoor illumination in a greenhouse, IsunIs the average indoor illumination intensity of the first five minutes, [ t0,tf]Is a unit regulation time interval.
In the step 3), the expression of the blueberry net photosynthetic rate prediction model is as follows:
Figure BDA0002898249220000033
wherein, PnFor the clean photosynthesis of blueberriesThe action rate, T is the growth temperature of crops, I is the photon flux density, alpha is the slope of the response curve of photosynthesis of plants when I is 0, namely the initial quantum efficiency, RdIs the dark respiration rate of the crop, beta is a correction coefficient, gamma is the curvature of the curve, fCorrect(. cndot.) is a temperature correction function.
In the step 4), the expression of the greenhouse light and temperature coordination optimization model is as follows:
Figure BDA0002898249220000034
wherein f is1(X) is a first objective function, f2(X) is a second objective function, TminFor the lower limit of temperature control of the greenhouse, TmaxThe upper limit of the temperature control of the greenhouse, IsunMean indoor light intensity in the first five minutes, ImaxFor the upper limit of the illumination intensity of the light supplement lamp of the greenhouse, X is an optimization variable and is specifically expressed as follows:
X＝(Taim,Iaim)。
the step 5) specifically comprises the following steps:
501) substituting the initial value of the environmental factor in the step 2), and setting model information and algorithm parameters;
502) randomly generating an initial population, and recording the iteration number gen as 1;
503) solving an objective function value of the blueberry net photosynthetic rate and an objective function value of the greenhouse energy consumption corresponding to each individual in the population;
504) performing rapid non-dominated sorting and congestion degree calculation on the initial generation population;
505) selecting, crossing and mutating the parent population by an elite strategy to generate offspring, and calculating objective function values corresponding to the offspring individuals;
506) merging the parent population and the child population, and performing rapid non-dominated sorting and congestion degree calculation on the merged individuals;
507) screening the combined parent population and offspring population by adopting an elite strategy to obtain a new parent population;
508) judging whether the set maximum iteration times is reached, if so, stopping iteration executing step 509), otherwise, making gen be gen +1, and returning to step 505);
509) and taking the set with the minimum non-dominated solution order value in the current population as the non-inferior solution set output of the greenhouse light-temperature coordination optimization model.
The step 6) specifically comprises the following steps:
601) the following steps are provided for solving the benefit coefficient set S of the non-inferior solution set:
S＝{x|x＝ηk,k＝1,2,...,K}
wherein eta iskThe coefficient is the benefit coefficient corresponding to the kth non-inferior solution in the non-inferior solution set, and K is the total number of the non-inferior solutions in the non-inferior solution set;
602) and (4) carrying out numerical value sequencing on the benefit coefficient set S of the non-inferior solution set, and selecting the non-inferior solution with the maximum benefit coefficient as the optimal scheme for light-temperature coordination and outputting.
In the step 601), the benefit coefficient eta corresponding to the kth non-inferior solution in the non-inferior solution setkThe calculation formula of (2) is as follows:
Figure BDA0002898249220000041
Figure BDA0002898249220000042
where N is the number of objective functions and N is 2, corresponding to two objective functions, K is the number of non-inferior solutions,
Figure BDA0002898249220000043
for the single target benefit coefficient of the nth objective function corresponding to the kth solution,
Figure BDA0002898249220000044
the maximum of the nth objective function in the non-inferior solution set,
Figure BDA0002898249220000045
for the nth objective function value of the kth solution,
Figure BDA0002898249220000046
is the benefit reference value of the nth objective function.
Compared with the prior art, the invention has the following advantages:
the light supplementing and temperature controlling time period is divided reasonably: the division of the time periods of the blueberry greenhouse needing light supplement and temperature control is reasonable, the optimization process is reliable, the optimized light temperature target set value is more suitable for practical regulation and control application, if the unit regulation and control time period is too long, the model with too large environmental factor change cannot be predicted, if the unit regulation and control time period is too short, the greenhouse regulation and control cannot actually achieve the temperature control effect, and the optimization loses significance.
And secondly, improving the economic benefits of blueberry greenhouse production: the method is based on the actual physiological requirements of crops, can effectively solve the problem that the production cost expenditure is not matched with the photosynthesis efficiency of the blueberries in the flower and fruit period, improves the economic benefit of blueberry greenhouse production, and avoids the problem that the blueberry photosynthesis efficiency cannot be effectively improved along with the increase of the production cost of the greenhouse.
Thirdly, the algorithm is advanced: the method solves the problem of blueberry greenhouse light and temperature coordination optimization by using an NSGA-II multi-target genetic algorithm to obtain a group of non-inferior solution sets meeting requirements, decides a greenhouse light and temperature target value benefit optimal setting scheme from the non-inferior solution sets by using a decision method for improving the optimal benefit, provides guidance for setting of greenhouse regulation and control values, and has certain innovation and practicability.
Fourthly, the universality and the expansibility are strong: the method is not limited to the greenhouse cultivation of the blueberry crops, is suitable for any greenhouse production crops capable of establishing a net photosynthetic rate model, and has certain universality and expansibility.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a detailed flow chart of the present invention for solving a multi-objective optimization model for greenhouse light and temperature coordination based on NSGA-II algorithm.
Fig. 3 is a detailed flowchart of the method for improving decision-making based on optimal benefit to obtain the optimal solution of light-temperature coordination from the non-inferior solution set according to the present invention.
Detailed Description
The technical solutions in the technical embodiments of the present invention will be described in detail, clearly and completely with reference to the accompanying drawings in the embodiments of the present invention.
Examples
As shown in fig. 1, the invention provides a blueberry greenhouse light and temperature coordination optimization method based on NSGA-II, which comprises the following steps:
s1: dividing the time interval of the blueberry greenhouse needing light supplement and temperature control by taking one hour as a unit regulation time interval;
s2: acquiring an average value of the environmental factors of five minutes before a unit regulation time period as an initial input;
s3: establishing a greenhouse energy consumption prediction model and a blueberry net photosynthetic rate prediction model;
s4: taking the maximum net photosynthetic rate and the minimum greenhouse energy consumption of the blueberry crops in a unit regulation and control time period as optimization targets, determining constraint conditions according to the average value of the environmental factors of five minutes before the time period, and establishing a greenhouse light-temperature coordination optimization model;
s5: solving the greenhouse light-temperature coordination optimization model provided by the invention by adopting an NSGA-II multi-target genetic algorithm to obtain a group of non-inferior solution sets meeting the requirements;
s6: selecting a light-temperature coordination optimal scheme from the non-inferior solution set by using a decision method based on optimal benefit improvement, and substituting the light-temperature coordination optimal scheme into actual regulation and control;
s7: and in the time period of the blueberry greenhouse needing light supplement and temperature control, repeatedly executing the steps S2-S6 in each unit regulation and control time period.
The S2 environmental factors are the indoor temperature of the greenhouse, the outdoor temperature of the greenhouse and the indoor illumination intensity of the greenhouse.
In the step S3, a greenhouse energy consumption prediction model and a blueberry net photosynthetic rate prediction model are established, and the specific steps are as follows:
s301: establishing a greenhouse energy consumption prediction model, wherein the expression is as follows:
QControl＝QTemp+QLight
wherein Q isControlFor regulating total energy consumption in greenhouses, QTempControlling energy consumption for regulating and controlling the greenhouse temperature in unit time; qLightThe energy consumption of the greenhouse light supplement is regulated and controlled in unit time.
Controlling energy consumption Q for greenhouse temperatureTempThe specific development steps for constructing the greenhouse temperature control energy consumption model are as follows:
firstly, based on the energy conservation principle, a greenhouse air temperature dynamic model is established:
ΔQ＝Qradin(t)+QHeat(t)-QCool(t)-Qexch(t)-Qtran(t)-Qsoil(t)
wherein: Δ Q is the energy change in the greenhouse; qradinIs the solar radiation power entering the greenhouse; qTempThe heat power caused by heating or cooling the greenhouse; qtranThe transpiration heat absorption power of the crops; qexchThe power loss caused by the heat exchange between the air in the greenhouse and the outdoor air through the covering material; qsoilIs the heat exchange power between the air and the soil in the greenhouse.
For convenience and practicality, the model can be simplified as follows:
when the temperature rises, the greenhouse temperature controls the energy consumption model:
Figure BDA0002898249220000061
when cooling, the greenhouse temperature controls the energy consumption model:
Figure BDA0002898249220000071
wherein Q isradinThe energy of the greenhouse increased by solar radiation is regulated and controlled in unit time; u is all heat loss coefficients including convection, conduction, heat exchange, long-wave radiation and the like; t isaimIs a target value of the indoor temperature of the greenhouse; t isoutIs the outdoor temperature; [ t ] of0,tf]Is a unit regulation time interval.
Energy consumption Q for greenhouse light supplementLightThe expression is specifically as follows:
Figure BDA0002898249220000072
where ρ isPARThe conversion coefficient of illumination energy; i isaimThe target value of indoor illumination of the greenhouse is obtained; i issunThe average indoor illumination intensity of the first five minutes; [ t ] of0,tf]Is a unit regulation time interval.
S302: establishing blueberry net photosynthetic rate prediction model
Figure BDA0002898249220000073
Wherein, PnThe net photosynthesis rate of the blueberries; t is the growth temperature of the crops; i is the photon flux density; alpha is the slope of the plant photosynthesis light response curve when I is 0, namely the initial quantum efficiency; rdThe dark respiration rate of the crop; beta is a correction coefficient; gamma is the curvature of the curve; f. ofCorrectIs a temperature correction function. In the present embodiment, it is preferred that,
Figure BDA0002898249220000074
α＝0.3386，b＝1.5455×10-4，γ＝0.0032，Rd＝1.0064。
therefore, in step S4, the expression of the greenhouse light and temperature coordination optimization model is:
Figure BDA0002898249220000075
in the formula (f)1(X) is greenhouse temperature control and light supplement energy consumption, namely a first objective function; f. of2(X) is the opposite number of the net photosynthetic rate of the blueberries, namely a second objective function; t isminFor the lower limit of temperature control of the greenhouse, TmaxThe upper limit of temperature control of the greenhouse; i issunMean indoor light intensity in the first five minutes, ImaxAnd the upper limit of the illumination intensity of the light supplement lamp for the greenhouse. X is an optimization variable, and is specifically expressed as:
X＝(Taim,Iaim)
in the formula: t isaimIs a target value of the temperature in the greenhouse, IaimIs the indoor illumination target value of the greenhouse.
In step S5, the multi-objective optimization model (greenhouse light-temperature coordination optimization model) is solved based on the multi-objective genetic algorithm NSGA-II. For one solution, if the constraint condition is satisfied, the solution is called a feasible solution, and if the constraint condition is not satisfied, the solution is called an infeasible solution. And for a feasible solution, defining a dominant relation in a solution set of the greenhouse light-temperature coordination optimization model according to the degree of approaching the target. And if the feasible solution a is better than or not worse than the feasible solution b in terms of the net photosynthetic rate of the blueberries and the greenhouse energy consumption target, the solution a is called to dominate the solution b. In NSGA-II, all solutions in the population that are not dominated by any other solution constitute a non-dominated solution, referred to as a non-dominated solution set.
Specifically, as shown in fig. 2, the solving step includes the following steps:
s501: substituting the initial value of the environmental factor in S2, and setting model information and algorithm parameters;
s502: randomly generating an initial population, and recording the iteration number gen as 1;
s503: solving a blueberry net photosynthetic rate objective function value and a greenhouse energy consumption objective function value corresponding to each individual in the population;
s504: performing rapid non-dominated sorting and congestion degree calculation on the initial generation population;
s505: selecting, crossing, mutating and the like the parent population through an elite strategy to generate offspring, and calculating objective function values corresponding to the offspring individuals;
s506: merging the parent generation and the child generation, and performing rapid non-dominated sorting and congestion degree calculation on the merged individuals;
s507: screening the combined parent and offspring by adopting an elite strategy to obtain a new parent;
s508: judging whether the set maximum iteration number is reached, if so, stopping iteration and entering S509, otherwise, enabling gen to be gen +1 and entering S505 again;
s509: and taking the set with the minimum non-dominated solution order value in the current population as the non-inferior solution set output of the greenhouse light-temperature coordination optimization model, and finishing the algorithm.
Finally, the solutions output in step S509 are both feasible solutions and approximate solutions, which provide guidance for the light-temperature coordination optimization within a unit regulation time period, and in order to further obtain an optimal light-temperature coordination optimization scheme, the invention adopts a decision method for improving optimal benefits, as shown in fig. 3, and the specific steps include:
s601: solving a benefit coefficient set S of a non-inferior solution set:
S＝{x|x＝ηk,k＝1,2,...,K}
in the formula etakThe benefit coefficient corresponding to the kth non-inferior solution in the non-inferior solution set; k is the number of non-inferior solutions in the set of non-inferior solutions.
The calculation formula of the benefit coefficient corresponding to the kth non-inferior solution in the non-inferior solution set is as follows:
Figure BDA0002898249220000091
in the formula, N is the number of objective functions;
Figure BDA0002898249220000092
and the single target benefit coefficient of the nth target function corresponding to the kth solution.
Wherein each target benefit coefficient
Figure BDA0002898249220000097
The calculation formula is as follows:
Figure BDA0002898249220000093
in the formula (I), the compound is shown in the specification,
Figure BDA0002898249220000094
the maximum value of the nth objective function in the non-inferior solution set;
Figure BDA0002898249220000095
an nth objective function value for the kth solution;
Figure BDA0002898249220000096
is the benefit reference value of the nth objective function.
S602: and (4) carrying out numerical value sequencing on the benefit coefficient set S of the non-inferior solution set, and taking the non-inferior solution with the maximum benefit coefficient, wherein the non-inferior solution is the optimal scheme for light-temperature coordination of the invention and is output.

Claims (10)

1. A NSGA-II-based blueberry greenhouse light and temperature coordination optimization method is characterized by comprising the following steps:
1) dividing regulation and control time intervals of a blueberry greenhouse needing supplementary lighting and temperature control by taking one hour as a unit;
2) for each unit regulation and control time interval, acquiring an average value of the environmental factors five minutes before the unit regulation and control time interval as initial input;
3) constructing a greenhouse energy consumption prediction model and a blueberry net photosynthetic rate prediction model;
4) taking the maximum net photosynthetic rate of the blueberry crops and the minimum greenhouse energy consumption in a unit regulation and control time period as optimization targets, determining constraint conditions according to the average value of the environmental factors of five minutes before the regulation and control time period, and establishing a greenhouse light-temperature coordination optimization model;
5) solving a greenhouse light-temperature coordination optimization model by adopting an NSGA-II multi-target genetic algorithm to obtain a group of non-inferior solution sets meeting the requirements;
6) and selecting a light-temperature coordination optimal scheme from the non-inferior solution set by adopting a decision method based on optimal benefit improvement, and carrying out actual regulation and control by using the light-temperature coordination optimal scheme.
2. The NSGA-II-based blueberry greenhouse light and temperature coordination optimization method according to claim 1, wherein in the step 2), the environmental factors specifically comprise greenhouse indoor temperature, greenhouse outdoor temperature and greenhouse indoor illumination intensity.
3. The NSGA-II-based blueberry greenhouse light and temperature coordination optimization method according to claim 1, wherein in the step 3), the expression of the greenhouse energy consumption prediction model is as follows:
QControl＝QTemp+QLight
wherein Q isControlFor regulating total energy consumption in greenhouses, QTempFor controlling energy consumption, Q, of the greenhouse temperatureLightSupplementing light energy consumption for the greenhouse.
4. The NSGA-II-based blueberry greenhouse light and temperature coordination optimization method is characterized in that energy consumption Q is controlled for greenhouse temperatureTempAt the time of temperature rise, the expression is:
Figure FDA0002898249210000011
when the temperature is reduced, the expression is as follows:
Figure FDA0002898249210000012
wherein Q isradinThe energy added by solar radiation for a greenhouse, U is the heat loss coefficient including convection, conduction, heat exchange and long-wave radiation, TaimIs a target value of the indoor temperature of the greenhouse, ToutIs the outdoor temperature, [ t ]0,tf]Is a unit regulation time interval.
5. The NSGA-II-based blueberry greenhouse light and temperature coordination optimization method according to claim 3, wherein energy consumption Q for greenhouse light supplement isLightThe expression is as follows:
Figure FDA0002898249210000021
where ρ isPARAs a conversion factor of the energy of illumination, IaimFor the target value of indoor illumination in a greenhouse, IsunIs the average indoor illumination intensity of the first five minutes, [ t0,tf]Is a unit regulation time interval.
6. The NSGA-II-based blueberry greenhouse light and temperature coordination optimization method as claimed in claim 3, wherein in the step 3), the expression of the blueberry net photosynthetic rate prediction model is as follows:
Figure FDA0002898249210000022
wherein, PnThe net photosynthesis rate of blueberries, T is the growth temperature of crops, I is the photon flux density, alpha is the slope of the light response curve of plant photosynthesis when I is 0, namely the initial quantum efficiency, RdIs the dark respiration rate of the crop, beta is a correction coefficient, gamma is the curvature of the curve, fCorrect(. cndot.) is a temperature correction function.
7. The NSGA-II-based blueberry greenhouse light and temperature coordination optimization method according to claim 6, wherein in the step 4), the expression of the greenhouse light and temperature coordination optimization model is as follows:
Figure FDA0002898249210000023
wherein f is1(X) is a first objective function, f2(X) is a second objective function, TminFor the lower limit of temperature control of the greenhouse, TmaxThe upper limit of the temperature control of the greenhouse, IsunMean indoor light intensity in the first five minutes, ImaxFor the upper limit of the illumination intensity of the light supplement lamp of the greenhouse, X is an optimization variable and is specifically expressed as follows:
X＝(Taim,Iaim)。
8. the NSGA-II-based blueberry greenhouse light and temperature coordination optimization method according to claim 1, wherein the step 5) specifically comprises the following steps:
501) substituting the initial value of the environmental factor in the step 2), and setting model information and algorithm parameters;
502) randomly generating an initial population, and recording the iteration number gen as 1;
503) solving an objective function value of the blueberry net photosynthetic rate and an objective function value of the greenhouse energy consumption corresponding to each individual in the population;
504) performing rapid non-dominated sorting and congestion degree calculation on the initial generation population;
505) selecting, crossing and mutating the parent population by an elite strategy to generate offspring, and calculating objective function values corresponding to the offspring individuals;
506) merging the parent population and the child population, and performing rapid non-dominated sorting and congestion degree calculation on the merged individuals;
507) screening the combined parent population and offspring population by adopting an elite strategy to obtain a new parent population;
508) judging whether the set maximum iteration times is reached, if so, stopping iteration executing step 509), otherwise, making gen be gen +1, and returning to step 505);
509) and taking the set with the minimum non-dominated solution order value in the current population as the non-inferior solution set output of the greenhouse light-temperature coordination optimization model.
9. The NSGA-II-based blueberry greenhouse light and temperature coordination optimization method according to claim 1, wherein the step 6) specifically comprises the following steps:
601) the following steps are provided for solving the benefit coefficient set S of the non-inferior solution set:
S＝{x|x＝ηk,k＝1,2,...,K}
wherein eta iskThe coefficient is the benefit coefficient corresponding to the kth non-inferior solution in the non-inferior solution set, and K is the total number of the non-inferior solutions in the non-inferior solution set;
602) and (4) carrying out numerical value sequencing on the benefit coefficient set S of the non-inferior solution set, and selecting the non-inferior solution with the maximum benefit coefficient as the optimal scheme for light-temperature coordination and outputting.
10. The NSGA-II-based blueberry greenhouse light and temperature coordinated optimization method of claim 9, wherein in the step 601), the benefit coefficient eta corresponding to the kth non-inferior solution in the non-inferior solution setkThe calculation formula of (2) is as follows:
Figure FDA0002898249210000031
Figure FDA0002898249210000032
where N is the number of objective functions and N is 2, K is the number of non-inferior solutions,
Figure FDA0002898249210000041
for the single target benefit coefficient of the nth objective function corresponding to the kth solution,
Figure FDA0002898249210000042
the maximum of the nth objective function in the non-inferior solution set,
Figure FDA0002898249210000043
for the nth objective function value of the kth solution,
Figure FDA0002898249210000044
is the benefit reference value of the nth objective function.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113994829A (en) * 2021-11-19 2022-02-01 国网甘肃省电力公司电力科学研究院 LED light supplement lamp operation regulation and control method considering time shifting and cost factors
CN114371748A (en) * 2021-12-15 2022-04-19 同济大学 Greenhouse crop illumination carbon dioxide regulation and control method based on Gaussian curvature
CN114868542A (en) * 2022-04-11 2022-08-09 四维生态科技（杭州）有限公司 Environment processing method, device and system for strawberry planting
CN115226516A (en) * 2022-07-07 2022-10-25 西北农林科技大学 Facility light and carbon dioxide environment cooperative regulation and control method
CN115528689A (en) * 2022-11-28 2022-12-27 南京邮电大学 Agricultural greenhouse spare capacity assessment method considering light supplement requirement
CN116976199A (en) * 2023-07-07 2023-10-31 同济大学 PCM-TCG photo-thermal performance optimization method based on non-dominant multi-objective genetic algorithm

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103237380A (en) * 2013-03-15 2013-08-07 西北农林科技大学 Method and system of intelligent light-environment controlling based on multi-factor coupling
CN105993720A (en) * 2016-06-13 2016-10-12 沈阳农业大学 Simulation calculation method for irrigation amount of solar-greenhouse matrix bag cultivation crops
CN106155144A (en) * 2016-08-17 2016-11-23 石家庄市农林科学研究院 A kind of environmental control of greenhouse method and device
CN106444378A (en) * 2016-10-10 2017-02-22 重庆科技学院 Plant culture method and system based on IoT (Internet of things) big data analysis
CN108633697A (en) * 2018-05-15 2018-10-12 重庆科技学院 A kind of foster culture method of the intelligent plant based on the daily data analysis of plant and cloud
CN109156196A (en) * 2018-10-08 2019-01-08 中国农业大学 A kind of plant light compensation control method and system for shining amount based on cumulative daylight
CN109324506A (en) * 2018-07-12 2019-02-12 同济大学 Consider energy conservation and the optimal greenhouse temperature setting value automatic obtaining method of production benefit
CN110175421A (en) * 2019-05-31 2019-08-27 杭州电子科技大学 A kind of new type water light complementation multi-target optimum operation method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103237380A (en) * 2013-03-15 2013-08-07 西北农林科技大学 Method and system of intelligent light-environment controlling based on multi-factor coupling
CN105993720A (en) * 2016-06-13 2016-10-12 沈阳农业大学 Simulation calculation method for irrigation amount of solar-greenhouse matrix bag cultivation crops
CN106155144A (en) * 2016-08-17 2016-11-23 石家庄市农林科学研究院 A kind of environmental control of greenhouse method and device
CN106444378A (en) * 2016-10-10 2017-02-22 重庆科技学院 Plant culture method and system based on IoT (Internet of things) big data analysis
CN108633697A (en) * 2018-05-15 2018-10-12 重庆科技学院 A kind of foster culture method of the intelligent plant based on the daily data analysis of plant and cloud
CN109324506A (en) * 2018-07-12 2019-02-12 同济大学 Consider energy conservation and the optimal greenhouse temperature setting value automatic obtaining method of production benefit
CN109156196A (en) * 2018-10-08 2019-01-08 中国农业大学 A kind of plant light compensation control method and system for shining amount based on cumulative daylight
CN110175421A (en) * 2019-05-31 2019-08-27 杭州电子科技大学 A kind of new type water light complementation multi-target optimum operation method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
HAIGEN HU等: "NSGA-II-based nonlinear PID controller tuning of greenhouse climate for reducing costs and improving performances", 《NEURAL COMPUTING AND APPLICATIONS》 *
LINA WANG: "Greenhouse Microclimate Control Optimization Based on Improved NSGA-II Algorithm", 《CHINESE CONTROL AND DECISION CONFERENCE》 *
张磊: "基于遗传算法的温室作物利润最优控制", 《内江科技》 *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113994829A (en) * 2021-11-19 2022-02-01 国网甘肃省电力公司电力科学研究院 LED light supplement lamp operation regulation and control method considering time shifting and cost factors
CN113994829B (en) * 2021-11-19 2022-09-20 国网甘肃省电力公司电力科学研究院 LED light supplement lamp operation regulation and control method considering time shifting and cost factors
CN114371748A (en) * 2021-12-15 2022-04-19 同济大学 Greenhouse crop illumination carbon dioxide regulation and control method based on Gaussian curvature
CN114371748B (en) * 2021-12-15 2022-09-20 同济大学 Greenhouse crop illumination carbon dioxide regulation and control method based on Gaussian curvature
CN114868542A (en) * 2022-04-11 2022-08-09 四维生态科技（杭州）有限公司 Environment processing method, device and system for strawberry planting
CN114868542B (en) * 2022-04-11 2024-01-09 四维生态科技（杭州）有限公司 Environment processing method, device and system for strawberry planting
CN115226516A (en) * 2022-07-07 2022-10-25 西北农林科技大学 Facility light and carbon dioxide environment cooperative regulation and control method
CN115226516B (en) * 2022-07-07 2023-09-26 西北农林科技大学 Cooperative regulation and control method for facility light and carbon dioxide environment
CN115528689A (en) * 2022-11-28 2022-12-27 南京邮电大学 Agricultural greenhouse spare capacity assessment method considering light supplement requirement
CN115528689B (en) * 2022-11-28 2023-02-17 南京邮电大学 Agricultural greenhouse spare capacity assessment method considering light supplement requirement
CN116976199A (en) * 2023-07-07 2023-10-31 同济大学 PCM-TCG photo-thermal performance optimization method based on non-dominant multi-objective genetic algorithm
CN116976199B (en) * 2023-07-07 2024-03-29 同济大学 PCM-TCG photo-thermal performance optimization method based on non-dominant multi-objective genetic algorithm

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