CN105353656A - Intelligent greenhouse irrigation control device based on fuzzy inference - Google Patents
Intelligent greenhouse irrigation control device based on fuzzy inference Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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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
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
- A01G9/247—Watering arrangements
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0275—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
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- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
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- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/25—Greenhouse technology, e.g. cooling systems therefor
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Abstract
The invention discloses an intelligent greenhouse irrigation control device based on fuzzy inference. The intelligent greenhouse irrigation control device comprises a soil humidity detection module, an evapotranspiration detection module, an intelligent control module based on fuzzy inference and an execution control module, wherein the soil humidity detection module is used for detecting moisture content in soil; the evapotranspiration detection module is used for acquiring greenhouse evapotranspiration according to average temperature of the environment, relative air humidity and illumination intensity; the intelligent control module is used for regarding soil humidity and evapotranspiration as input variables and irrigation quantity of a greenhouse as an output variable, fuzzifying the input variables and the output variable, selecting a triangular membership function, establishing a fuzzy rule, adopting a minimal operation method for fuzzy inference, and converting the inferred fuzzy quantity into accurate quantity for output; and the execution control module is used for outputting the accurate quantity as an irrigation quantity of the greenhouse to an irrigation execution mechanism. The intelligent greenhouse irrigation control device based on fuzzy inference provided by the invention is high in control precision, high in working efficiency, good in applicability and low in irrigation cost.
Description
Technical field
The present invention relates to greenhouse intelligent control field, especially a kind of greenhouse irrigation control device.
Background technology
In greenhouse irrigation system, irrigation water management is a very complicated task, this is because the water demand of crop is not only by the impact in crop growth period, also by the impact of the multiple environmental baselines such as temperature, humidity, precipitation, transpiration.A large amount of irrigation tests data shows: biological characteristics (leaf area index, crop root water-intake capacity etc.), the agrotechnique and irrigation and drainage measure etc. of the size of crop irrigation quantity and edaphic condition (comprising the soil texture, soil moisture content, structure and underground water table etc.), meteorological condition (comprising solar radiation, sunshine, temperature, wind speed and humidity etc.) and crop are relevant.More representational in numerous analogy models is at present that agricultural science and technology switch decision supports (DSSAT), Wageningen model, farm output system simulation (APSIM) and CROPWAT model etc.Especially CROPWAT model, function ratio is comparatively comprehensive, can carry out the calculating of standard to tatol evapotranspiration and irrigation requirement, can also assess different irrigation strategies and insufficient irrigation to the impact of crop yield.But these systems are too numerous and diverse, need all may use weather information, environmental information and the cost etc. with water, not only need quantity of information to be processed too huge, the configuration of requirement system is higher, but also the reaction time of control system can be reduced, make control system be in top load operating condition, reduce the performance and used life of whole system, its high cost makes it to be difficult to be applied in general irrigation system.
Summary of the invention
In order to overcome the deficiency that control accuracy is lower, work efficiency is lower, applicability is poor, irrigation cost is higher that existing greenhouse irrigation controls to exist, the invention provides the intelligent greenhouse control device for irrigating based on fuzzy reasoning that a kind of control accuracy is higher, work efficiency is higher, applicability is good and irrigation cost is lower.
The technical solution adopted for the present invention to solve the technical problems is:
Based on an intelligent greenhouse control device for irrigating for fuzzy reasoning, comprising:
For detecting the soil moisture detection module of soil moisture content;
For obtaining the transpiration quantity detection module of greenhouse transpiration quantity according to environment medial temperature, relative air humidity and intensity of illumination;
For with soil moisture and transpiration quantity for input variable, the irrigation volume in greenhouse is output variable, by input variable and output variable obfuscation, select Triangleshape grade of membership function, and set up fuzzy rule, adopt minimum operation method to carry out fuzzy reasoning, and the fuzzy quantity after reasoning is converted to the intelligent control module based on fuzzy reasoning of precise volume output;
For precise volume is outputted to the execution control module of irrigating topworks as the irrigation volume in greenhouse.
Further, described intelligent control module comprises:
Input, output quantity fuzzy subset and language domain level definition unit, for the irrigation quantity WD of soil moisture value EC, crop evapotranspiration EV and hothouse plants is divided into 5 linguistic variables, i.e. { honest, just little, zero, negative little, negative large }, referred to as PB, PS, ZO, NS, NB}, its domain is:
EC＝{-2、-1、0、1、2}
EV＝{-2、-1、0、1、2}
WD＝{-2、-1、0、1、2}
The fuzzier unit of input quantity and output quantity, rounding will be carried out to the result of Y for adopting formula Y=4 (X-(a+b)/2)/(b-a), thus obtain the fuzzy quantity of corresponding EC and EV, all Triangleshape grade of membership function is selected to the membership function of input and output variable;
Fuzzy rule and fuzzy reasoning table set up unit, for setting up fuzzy control rule table according to expertise:
Fuzzy reasoning and sharpening unit, for adopting the minimum operation method of Mamdani to carry out fuzzy reasoning, and be converted to precise volume by fuzzy quantity, and this precise volume is the control signal of irrigating topworks.
Further, in described transpiration quantity detection module, according to environment medial temperature T, intensity of illumination lx, relative air humidity RH, draw by multiple linear regression the relational expression that transpiration quantity ET is following: ET=8.170+0.212T-0.130RH+0.370lx.
Technical conceive of the present invention is: on the basis considering hothouse plants transpiration quantity and soil moisture, have employed fuzzy intelligence decision-making technic to obtain best irrigation quantity, carries out appropriate irrigation to greenhouse.
Moisture in soil is comprehensively determined by multiple sensors of diverse location in greenhouse.Soil humidity sensor adopts the EC-5 soil moisture sensor selecting Decagon company.The output of this sensor is simulating signal, its essence is a kind of capacitive transducer of high integration, has the advantages such as volume is little, voltage is low, low in energy consumption, precision is high, picking rate is fast.Its concrete technical indicator is as follows:
A, test duration: 10ms
B, precision: >=0.03m3/m3
C, resolution: 0.001m3/m3VWC
D, power supply: 2.5VDC ~ 3.6VDC10mA
E, output: 10 ~ 40% field voltages (250-1000mVat2500mVexcitation)
F, working temperature :-40 ~+60 DEG C
The earphone base interface of G, interface type: 3.5mm.
The transpiration quantity of hothouse plants refer to growth over a large area without disease and pest crop, when soil moisture and fertility are suitable for, the condition of high yield potential can be obtained in given growing environment under, the water yield of transpiration and soil evaporation.But in actual applications, because the moisture of composition plant body only accounts for a part (being usually less than 1%) very small in total water requirements, and the influence factor of this part is comparatively complicated, be difficult to accurate calculating, this part is all ignored by old friends, namely think that the water demand of crop numerically just equals transpiration amount (Transpiration) under high yield level conditions and ground evaporation between plants (Evaporation) sum, be called " tatol evapotranspiration " (Evapotranspiration), be called for short " transpiration quantity ".
Plant physiology factor, by the research to the rising physiological mechanism of plant moisture, is well introduced rising computing formula by Penman.This formula is derived by energy equilibrium under underlying surface surface (interface in atmospheric envelope and face, land and water) is for saturated condition, primarily of radiation term and aerodynamics item composition.Mainly relevant with wind speed u with medial temperature T, intensity of illumination lx, relative air humidity RH with reference to transpiration rate.Irrigation system primary study of the present invention is the irrigation system in greenhouse, because greenhouse is a small-sized weather with specific environment, therefore, can introduce the estimation of the plant transpiration evaporation capacity of simplification.Research shows, the reference transpiration rate of irrigation district and environmental air temperature, humidity, intensity of illumination are certain linear relationship, the data experimentally measured, after the correlativity analyzing transpiration rate and three envirment factors, can draw by multiple linear regression the relational expression that transpiration quantity is following:
ET＝8.170+0.212·T-0.130·RH+0.370·lx
Fuzzy reasoning is carried out to obtained environmental information, irrigation water capacity comparatively is accurately obtained by ambiguity solution, under the accurate and reliable prerequisite ensureing irrigation water capacity, by simplifying the parameter of fuzzy control, Optimization of Fuzzy control algolithm, make the adaptive capacity that whole greenhouse irrigation control system is stronger, fuzzy reasoning can be carried out more fast to obtained information, irrigation water capacity comparatively is accurately obtained by ambiguity solution, improve the reaction time of whole irrigation system, control system is operated all the time efficiently, improve service efficiency and the serviceable life of system, improve production efficiency, reduce the cost of irrigation system, improve the earning rate of whole Greenhouse System.
Beneficial effect of the present invention is mainly manifested in: this device has stronger adaptive capacity, fuzzy reasoning can be carried out more fast to obtained environmental information, irrigation water capacity comparatively is accurately obtained by ambiguity solution, improve the reaction time of whole irrigation system, irrigation system is operated all the time efficiently, improve service efficiency and the serviceable life of system, reduce the cost of irrigation system.
Accompanying drawing explanation
Fig. 1 is the theory diagram of intelligent greenhouse control device for irrigating.
Fig. 2 is the illustraton of model of Fuzzy inferential decision.
Fig. 3 is the schematic diagram of the membership function of soil moisture.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.
With reference to Fig. 1 ~ Fig. 3, a kind of intelligent greenhouse control device for irrigating based on fuzzy reasoning, comprising:
For detecting the soil moisture detection module of soil moisture content;
For obtaining the transpiration quantity detection module of greenhouse transpiration quantity according to environment medial temperature, relative air humidity and intensity of illumination;
For with soil moisture and transpiration quantity for input variable, the irrigation volume in greenhouse is output variable, by input variable and output variable obfuscation, select Triangleshape grade of membership function, and set up fuzzy rule, adopt minimum operation method to carry out fuzzy reasoning, and the fuzzy quantity after reasoning is converted to the intelligent control module based on fuzzy reasoning of precise volume output;
For precise volume is outputted to the execution control module of irrigating topworks as the irrigation volume in greenhouse.
Further, described intelligent control module comprises:
Input, output quantity fuzzy subset and language domain level definition unit, for the irrigation quantity WD of soil moisture value EC, crop evapotranspiration EV and hothouse plants is divided into 5 linguistic variables, i.e. { honest, just little, zero, negative little, negative large }, referred to as PB, PS, ZO, NS, NB}, its domain is:
EC＝{-2、-1、0、1、2}
EV＝{-2、-1、0、1、2}
WD＝{-2、-1、0、1、2}
The fuzzier unit of input quantity and output quantity, rounding will be carried out to the result of Y for adopting formula Y=4 (X-(a+b)/2)/(b-a), thus obtain the fuzzy quantity of corresponding EC and EV, all Triangleshape grade of membership function is selected to the membership function of input and output variable;
Fuzzy rule and fuzzy reasoning table set up unit, for setting up fuzzy control rule table according to expertise:
Fuzzy reasoning and sharpening unit, for adopting the minimum operation method of Mamdani to carry out fuzzy reasoning, and be converted to precise volume by fuzzy quantity, and this precise volume is the control signal of irrigating topworks.
Described soil moisture detection module is all connected with described intelligent control module with transpiration quantity detection module, and described intelligent control module is connected with described execution control module.
The energy greenhouse irrigation control device of the present embodiment, in conjunction with greenhouse irrigation system, the concrete steps based on the greenhouse irrigation method of fuzzy reasoning are as follows:
1) definition input, output quantity fuzzy subset and language domain grade
System adopts the algorithm of look-up table as fuzzy control of compositional rule of inference.First service station intelligent management platform utilizes the greenhouse environment information collected to calculate transpiration quantity (EV) and the soil moisture value (EC) of plant.Then obfuscation is carried out to transpiration quantity EV, the soil moisture value EC of input variable-plant and the irrigation quantity WD in output variable-greenhouse.Consider and irrigate physical condition and control accuracy, the irrigation quantity WD of soil moisture value EC, crop evapotranspiration EV and hothouse plants is divided into 5 linguistic variables, i.e. { honest, just little, zero, negative little, negative large }, referred to as being { PB, PS, ZO, NS, NB}.
Its domain is:
EC＝{-2、-1、0、1、2}
EV＝{-2、-1、0、1、2}
WD＝{-2、-1、0、1、2}
2) fuzzification process of input quantity and output quantity
Precise volume EC, EV are converted into fuzzy quantity.Be [a if general by actual change scope, b] precise volume X, be converted to [-2,2] the fuzzy change Y of interval change, formula Y=4 (X-(a+b)/2)/(b-a) should be adopted will rounding to be carried out to the result of Y, thus obtain the fuzzy quantity of corresponding EC and EV, be so just converted into corresponding fuzzy set accurately collecting.Have passed through from basic domain to after the conversion of fuzzy domain, the membership function of fuzzy variable will be determined.The impact of selection on fuzzy control performance of membership function is larger.In general, the shape of membership function is steeper, and Systematical control sensitivity is higher; Otherwise membership function change is slower, then the stability of system is better.Consider simplicity and the practicality of the programming of this irrigation system, the membership function of the design to input and output variable all selects Triangleshape grade of membership function, as shown in Figure 3.
As follows according to the membership function table of membership function figure, input variable EC, EV and output variable WD:
What table 1 was soil moisture EC is subordinate to angle value assignment table:
Table 1
What table 2 was transpiration quantity EV is subordinate to angle value assignment table:
Table 2
Table 3 is the degree of membership assignment table of irrigation volume WD:
Table 3
3) foundation of fuzzy rule and fuzzy reasoning table
Known by expertise: when soil moisture EC is very low, even if transpiration quantity is less, still need to carry out maximum irrigation to orchard; When soil moisture EC is very high, even if transpiration quantity EV is very large, also need to pour water on a small quantity.Therefore design consideration expertise sums up 25 fuzzy condition statements altogether.Their summary is become fuzzy control rule table such as table 4 show.
Table 4
The conditional statement of " IF-THEN " form of use is described below:
IfEC＝NBandEV＝NBthenWD＝PB；
IfEC＝NSandEV＝NBthenWD＝PS；
......
IfEC＝PBandEV＝PBthenWD＝NS；
4) fuzzy reasoning and sharpening
In fuzzy controller, a fuzzy subset of control variable will be gone out through the decision-making of fuzzy reasoning ability to the fuzzy control rule set up, it is a fuzzy quantity and directly can not controls controlled device, also need to take rational method that fuzzy quantity is converted to precise volume, to have given play to the decision-making results of fuzzy reasoning result best.This process that fuzzy quantity is converted to precise volume is called sharpening.For above-mentioned rule, fuzzy reasoning will adopt the minimum operation method of Mamdani the most frequently used in Fuzzy control system.
The scheme of the present embodiment is under the accurate and reliable prerequisite ensureing irrigation water capacity, by simplifying the parameter of fuzzy control, Optimization of Fuzzy control algolithm, make the adaptive capacity that whole greenhouse irrigation control system is stronger, fuzzy reasoning can be carried out more fast to obtained information, irrigation water capacity comparatively is accurately obtained by ambiguity solution, improve the reaction time of whole irrigation system, control system is operated all the time efficiently, improve service efficiency and the serviceable life of system, improve production efficiency, reduce the cost of irrigation system, improve the earning rate of whole Greenhouse System.
Claims (3)
1. based on an intelligent greenhouse control device for irrigating for fuzzy reasoning, it is characterized in that: described intelligent greenhouse control device for irrigating comprises:
For detecting the soil moisture detection module of soil moisture content;
For obtaining the transpiration quantity detection module of greenhouse transpiration quantity according to environment medial temperature, relative air humidity and intensity of illumination;
For with soil moisture and transpiration quantity for input variable, the irrigation volume in greenhouse is output variable, by input variable and output variable obfuscation, select Triangleshape grade of membership function, and set up fuzzy rule, adopt minimum operation method to carry out fuzzy reasoning, and the fuzzy quantity after reasoning is converted to the intelligent control module based on fuzzy reasoning of precise volume output;
For precise volume is outputted to the execution control module of irrigating topworks as the irrigation volume in greenhouse.
2., as claimed in claim 1 based on the intelligent greenhouse control device for irrigating of fuzzy reasoning, it is characterized in that: described intelligent control module comprises:
Input, output quantity fuzzy subset and language domain level definition unit, for the irrigation quantity WD of soil moisture value EC, crop evapotranspiration EV and hothouse plants is divided into 5 linguistic variables, i.e. { honest, just little, zero, negative little, negative large }, referred to as PB, PS, ZO, NS, NB}, its domain is:
EC＝{-2、-1、0、1、2}
EV＝{-2、-1、0、1、2}
WD＝{-2、-1、0、1、2}
The fuzzier unit of input quantity and output quantity, rounding will be carried out to the result of Y for adopting formula Y=4 (X-(a+b)/2)/(b-a), thus obtain the fuzzy quantity of corresponding EC and EV, all Triangleshape grade of membership function is selected to the membership function of input and output variable;
Fuzzy rule and fuzzy reasoning table set up unit, for setting up fuzzy control rule table according to expertise:
Fuzzy reasoning and sharpening unit, for adopting the minimum operation method of Mamdani to carry out fuzzy reasoning, and be converted to precise volume by fuzzy quantity, and this precise volume is the control signal of irrigating topworks.
3. as claimed in claim 1 or 2 based on the intelligent greenhouse control device for irrigating of fuzzy reasoning, it is characterized in that: in described transpiration quantity detection module, according to environment medial temperature T, intensity of illumination lx, relative air humidity RH, draw by multiple linear regression the relational expression that transpiration quantity ET is following: ET=8.170+0.212T-0.130RH+0.370lx.
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Cited By (15)
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| CN106962069A (en) * | 2017-05-09 | 2017-07-21 | 上海电机学院 | A kind of crop cultivation case based on daylight |
| CN107015480A (en) * | 2017-05-17 | 2017-08-04 | 江苏商贸职业学院 | A kind of intelligent greenhouse irrigation system based on generalized predictive control and Internet of Things |
| CN107121927A (en) * | 2017-05-17 | 2017-09-01 | 江苏商贸职业学院 | A kind of irrigation system based on generalized predictive control |
| CN110352832A (en) * | 2019-05-14 | 2019-10-22 | 青岛农业大学 | MLR model red Fuji apple tree Precision Irrigation method based on Spark |
| CN110432046A (en) * | 2019-09-17 | 2019-11-12 | 华北水利水电大学 | A kind of indoor Intelligent irrigation system of temperature |
| CN111257592A (en) * | 2020-03-05 | 2020-06-09 | 广东零偏科技有限公司 | Static discrimination method for detection device |
| CN111612331A (en) * | 2020-05-19 | 2020-09-01 | 河北工业大学 | Fuzzy intelligent plant growth monitoring system based on soil water potential and control target threshold value obtaining method |
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| CN106962069A (en) * | 2017-05-09 | 2017-07-21 | 上海电机学院 | A kind of crop cultivation case based on daylight |
| CN107015480A (en) * | 2017-05-17 | 2017-08-04 | 江苏商贸职业学院 | A kind of intelligent greenhouse irrigation system based on generalized predictive control and Internet of Things |
| CN107121927A (en) * | 2017-05-17 | 2017-09-01 | 江苏商贸职业学院 | A kind of irrigation system based on generalized predictive control |
| CN110352832A (en) * | 2019-05-14 | 2019-10-22 | 青岛农业大学 | MLR model red Fuji apple tree Precision Irrigation method based on Spark |
| CN110432046A (en) * | 2019-09-17 | 2019-11-12 | 华北水利水电大学 | A kind of indoor Intelligent irrigation system of temperature |
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