CN108762084A - Irrigation system of rice field based on fuzzy control decision and method - Google Patents
Irrigation system of rice field based on fuzzy control decision and method Download PDFInfo
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- CN108762084A CN108762084A CN201810611566.6A CN201810611566A CN108762084A CN 108762084 A CN108762084 A CN 108762084A CN 201810611566 A CN201810611566 A CN 201810611566A CN 108762084 A CN108762084 A CN 108762084A
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- 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
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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Abstract
The invention discloses a kind of irrigation system of rice field and method based on fuzzy control decision, including:Monitoring modular includes weather monitoring system, soil moisture content monitoring system；Several paddy field monitoring sections, the paddy field monitoring section are set in paddy field, and the soil moisture content monitoring system is set to；Neural network prediction module, the neural network prediction module are connect with the monitoring modular, and the neural network prediction module is used to predict the transpiration quantity of crop；Fuzzy control decision-making module, described to be connect with the neural network prediction module, the fuzzy control decision-making module uses fuzzy-adaptation PID control mode；Module is irrigated, the irrigation module is connect with the paddy field and fuzzy control decision-making module.
Description
Technical field
The invention belongs to irrigation technique field more particularly to a kind of irrigation system of rice field and side based on fuzzy control decision
Method.
Background technology
With the increasing and industrialized development of population, water resources shortage problem is more and more significant.This requires me
Change the agricultural Model of traditional low moisture utilization rate, carry out wisdom agriculture management pattern.With crop each period to the need of moisture
The amount of asking is that foundation is timely and appropriately irrigated using the efficiency of water application for improving crop as target, and crop is made to be grown in always most
Good soil moisture state, final high-quality, high yield, the high benefit for realizing agricultural production.
Wisdom agricultural is that a kind of agricultural Model of production management is carried out with new and high technology on the basis of traditional agriculture.Intelligence
Compared with traditional agriculture, its advance is mainly applied global positioning system, geographical information technology, Computer Control Technology to intelligent agricultural
Etc. advanced technologies, realize to the positioning of agricultural production, timing, quantitative management, accomplish to cultivate intensely.Precision Irrigation is exactly in intelligence
Grow up on the basis of intelligent agricultural.Since agricultural management point more disperses, data acquisition and information are carried out with conventional method
The shortcomings that transmission is that low precision, speed are slow.Therefore, electronic technology, microelectric technique and the communication technology are combined closely, is adopted
Carrying out automatically-monitored and management with modernism is very important.
Therefore, domestic and foreign scholars have carried out a large amount of research work for irrigating the control method of Predicting and Policy-Making, they
Think, takes effective control method such as fuzzy logic inference, expertise, neural network etc. to predict that the irrigation of crops is used
Water, and constantly timely being adjusted according to environmental parameter, it is most to close to meet the growth of the crop under varying environment with water
Reason and promising method, relevant control method research is more, can be divided into modern scientist strategy, Model Predictive Control and without mould
The intelligent control method three categories of type.
Key issues of being controlled for Water Demand Prediction urgently to be resolved hurrily in crop Precision Irrigation and intelligent decision, carries out reason
By analysis, numerical simulation, innovation optimization and experimental study, and using rice be experimental subjects progress experimental verification, ultimately form with
The control theory and method of crop irrigation based on neural network prediction and fuzzy control decision push Precision Irrigation to control skill
The Precision Irrigation of timely and appropriate discovery is accomplished in the development of art.The patent of invention of Patent No. CN201310690175.5 also uses BP god
Through neural network forecast computational methods.
Invention content
In order to achieve the above objectives, the technical solution adopted by the present invention is：A kind of water paddy irrigation based on fuzzy control decision
System and method, including：Monitoring modular includes weather monitoring system, soil moisture content monitoring system；Several paddy field monitoring sections, it is described
Paddy field monitoring section is set to paddy field, and the soil moisture content monitoring system is set to；Neural network prediction module, the neural network prediction
Module is connect with the monitoring modular, and the neural network prediction module is used to predict the transpiration quantity of crop；Fuzzy control decision
Module, described to be connect with the neural network prediction module, the fuzzy control decision-making module uses fuzzy-adaptation PID control mode；
Module is irrigated, the irrigation module is connect with the paddy field and fuzzy control decision-making module.
Further, the neural network prediction module predicts crop evapotranspiration by monitoring modular.
Further, the neural network prediction module uses Penman-Monteith formula and BP neural network to making
Object water requirement is modeled and is predicted.
Further, the neural network prediction module uses BP neural network ET0 prediction models.
Further, the irrigation method based on neural network prediction：
The data transmission that S1, monitoring modular collect weather monitoring system, soil moisture content monitoring system is pre- to neural network
Survey module；
The data prediction water requirements of crops that S2, neural network prediction module analysis are collected into, and by the data after analysis
It is input to fuzzy control decision-making module；
The extraneous factors such as S3, fuzzy control decision-making module combination soil moisture content, weather information fill each growth phase of crop
The influence for the amount of irrigating analyzes the variation tendency of the water demand of crop, and analysis data is transported to irrigation module；
S4, module is irrigated according to water requirement variation tendency to crop progress Precision Irrigation.
Further, paddy field monitoring section number is determined according to crops planting area size.
Further, the soil moisture content monitoring system includes air temperature sensor, relative air humidity sensor, soil
Earth temperature sensor, solar radiation, wind direction, wind speed, precipitation, atmospheric pressure, illuminance, dew point, sunshine, photosynthetic effective spoke
Penetrate, ultraviolet radioactive, evaporation, carbon dioxide, blade humidity sensor.
Further, the weather monitoring system can be weather monitoring instrument, meteorological sensor or meteorological satellite.
The invention solves the defect existing in the background technology, and the present invention has following advantageous effect：
1 present invention uses the advanced technologies such as neural network prediction and fuzzy control decision, then coupled computer control technology,
It realizes to the positioning of agricultural production, timing, quantitative automatically-monitored and management.
2 are modeled and are predicted to the water demand of crop using Penman-Monteith formula and BP neural network, before utilization
The meteorological data and the water demand of crop in one stage predicts the latter half water requirement, to disclose meteorological data and the water demand of crop
Between complex relationship, to Development of Water-Saving Irrigation theory, carrying out effective irrigate has important theory significance and practical value.
3 since irrigation system has the characteristics that big inertia, non-linear and pure time delay, what proposition was predicted based on BP neural network
Fuzzy PID carries out on-line optimization design, to realize efficient, reliable and accurate control.
Paddy field monitoring section number is arranged according to paddy field size in 4 present invention, and the whole of paddy field is predicted under the loss of very little
Body situation, to realize Precision Irrigation.
Description of the drawings
Present invention will be further explained below with reference to the attached drawings and examples；
Fig. 1 is the schematic diagram of the preferred embodiment of the present invention；
Fig. 2 is paddy field schematic diagram；
In figure：1, monitoring modular；2, weather monitoring system；3, soil moisture content monitors system；4, paddy field monitoring section；5, neural
Neural network forecast module；6, fuzzy control decision-making module；7, module is irrigated.
Specific implementation mode
Presently in connection with drawings and examples, the present invention is described in further detail, these attached drawings are simplified signal
Figure, technical solution in the embodiment of the present invention carry out clear, complete description, it is clear that described embodiment is only of the invention
A part of the embodiment, instead of all the embodiments.The basic structure of the invention will be illustrated schematically only, therefore it is only aobvious
Show composition related to the present invention.
The realization of the present invention is described in detail below in conjunction with specific implementation mode.
If Fig. 1 and Fig. 2 show, a kind of irrigation system of rice field and method based on fuzzy control decision, including：Monitoring modular 1
System 3 is monitored including weather monitoring system 2, soil moisture content；Several paddy field monitoring sections 4, the paddy field monitoring section are set to paddy field, institute
Soil moisture content monitoring system 3 is stated to be set to；Neural network prediction module 5, the neural network prediction module 5 and the monitoring modular
1 connection, the neural network prediction module 5 are used to predict the transpiration quantity of crop；Fuzzy control decision-making module 6, it is described with it is described
Neural network prediction module 5 connects, and the fuzzy control decision-making module 6 uses fuzzy-adaptation PID control mode；Module 7 is irrigated, it is described
Module 7 is irrigated to connect with the paddy field and fuzzy control decision-making module 6.
It is understood that neural network due to its MPP, fault-tolerance, self-organizing, adaptive ability and
The strong feature of association function, it has also become solve the problems, such as many powerfuls, to breaking through the bottleneck of existing science and technology, deeper into
It explores non-linear equal complicated phenomenons and plays significant role, it is widely used to many engineering fields.Artificial neuron is raw
The mathematical abstractions of object neuron behavior and function, a kind of meter that neural network is typically made of a large amount of simple neuron interconnections
Structure is calculated, it can simulate the course of work of biological nervous system to a certain extent, to have the energy of solving practical problems
Power.Optimum algorithm of multi-layer neural network is exactly using the collaboration computation capability of neuron in neural network come constitution optimization algorithm,
It is corresponding with the stable state of neural network by the optimization solution of practical problem, and the optimization process to practical problem is mapped as god
Evolutionary process through network system.
Fuzzy control theory, with the method for language Rule Expression, decision is carried out by fuzzy reasoning based on fuzzy mathematics
Control strategy.It belongs to the scope of intelligent control, and it is important to be developed so far one had become in artificial intelligence field
Branch.Fuzzy control theory have developed rapidly, and application field is extensive, and control effect is apparent, especially in recent years, fuzzy control with
The integrated control that other control strategies are constituted, and the fuzzy neural network etc. being combined with neural network have obtained rapid hair
Exhibition.
The neural network prediction module 5 predicts crop evapotranspiration by monitoring modular 1.
The neural network prediction module 5 is using Penman-Monteith formula and BP neural network to the water demand of crop
It is modeled and is predicted.It is understood that using Penman-Monteith formula and BP neural network to the water demand of crop into
Row modeling and prediction, predict the latter half water requirement, to disclose using the meteorological data and the water demand of crop of previous stage
Complex relationship between meteorological data and the water demand of crop carries out effective irrigate with important to Development of Water-Saving Irrigation theory
Theory significance and practical value.
The neural network prediction module 5 uses BP neural network ET0 prediction models.Using the BP nerve nets of innovatory algorithm
Network predicts the transpiration quantity of crop, relates generally to the neural network based on error backpropagation algorithm, BP algorithm has become at present
The Learning Algorithm being most widely used, the transmission function of the neuron use of BP networks is typically Sigmoid types can
Micro- function, it is possible to realize the arbitrary nonlinear mapping between outputting and inputting.
Irrigation method based on neural network prediction, comprises the steps of：
S1, monitoring modular 1 are by weather monitoring system 2, the soil moisture content data transmission collected of monitoring system 3 to neural network
Prediction module 5；
S2, neural network prediction module 5 analyze the data prediction water requirements of crops that is collected into, and by the data after analysis
It is input to fuzzy control decision-making module 6；
S3, fuzzy control decision-making module 6 combine the extraneous factors such as soil moisture content, weather information to fill each growth phase of crop
The influence for the amount of irrigating analyzes the variation tendency of the water demand of crop, and analysis data is transported to and irrigate module 7；
S4, module 7 is irrigated according to water requirement variation tendency to crop progress Precision Irrigation.
Described 4 numbers in paddy field monitoring section are determined according to crops planting area size.It is fitted it is understood that the present invention uses
Water can be arranged in representational region in farmland for farmland concentration zones and large-scale farm for the paddy field of all size
Field detection zone 4.
It includes air temperature sensor, relative air humidity sensor, soil moisture sensing that soil moisture content, which monitors system 3,
Device, solar radiation, wind direction, wind speed, precipitation, atmospheric pressure, illuminance, dew point, sunshine, photosynthetically active radiation, ultraviolet radioactive,
Evaporation, carbon dioxide, blade humidity sensor.It is understood that the sensor in soil moisture content monitoring system 3 is not limited to
This can be selected in actual mechanical process according to actual demand.
The weather monitoring system 2 can be weather monitoring instrument, meteorological sensor or meteorological satellite.It is understood that
Weather monitoring system 2 and soil moisture content monitor system 3 according to different Crop Planting Structures and water requirement situation, in conjunction with surface water
Resource distribution, reasonable distribution water resource realize Precision Irrigation.
Based on the above description of the preferred embodiments of the present invention, through the above description, related personnel completely can be with
Without departing from the scope of the technological thought of the present invention', various changes and amendments are carried out.The technical scope of this invention
It is not limited to the contents of the specification, it is necessary to determine the technical scope according to the scope of the claims.
Claims (8)
1. a kind of irrigation system of rice field based on fuzzy control decision, which is characterized in that including：
Monitoring modular includes weather monitoring system, soil moisture content monitoring system；
Several paddy field monitoring sections, the paddy field monitoring section are set to paddy field, and the soil moisture content monitoring system is set to；
Neural network prediction module, the neural network prediction module are connect with the monitoring modular, the neural network prediction
Module is used to predict the transpiration quantity of crop；
Fuzzy control decision-making module, described to be connect with the neural network prediction module, the fuzzy control decision-making module uses
Fuzzy-adaptation PID control mode；
Module is irrigated, the irrigation module is connect with the paddy field and fuzzy control decision-making module.
2. the irrigation system of rice field according to claim 1 based on fuzzy control decision, it is characterised in that：The nerve net
Network prediction module predicts crop evapotranspiration by monitoring modular.
3. the irrigation system of rice field according to claim 2 based on fuzzy control decision, it is characterised in that：The nerve net
Network prediction module is modeled and is predicted to the water demand of crop using Penman-Monteith formula and BP neural network.
4. the irrigation system of rice field according to claim 3 based on fuzzy control decision, it is characterised in that：The nerve net
Network prediction module uses BP neural network ET0 prediction models.
5. the irrigation method that the irrigation system in a kind of 1-4 using claim described in any claim carries out, feature exist
In comprising the steps of：
The data transmission that S1, monitoring modular collect weather monitoring system, soil moisture content monitoring system is to neural network prediction mould
Block；
The data prediction water requirements of crops that S2, neural network prediction module analysis are collected into, and the data after analysis are inputted
To fuzzy control decision-making module；
The extraneous factors such as S3, fuzzy control decision-making module combination soil moisture content, weather information are to each growth phase irrigation volume of crop
Influence, analyze the variation tendency of the water demand of crop, and analysis data are transported to irrigation module；
S4, module is irrigated according to water requirement variation tendency to crop progress Precision Irrigation.
6. irrigation method according to claim 5, it is characterised in that：The paddy field prison is determined according to crops planting area size
Survey area's number.
7. the irrigation system of rice field according to claim 1 based on fuzzy control decision, it is characterised in that：The soil moisture in the soil
Feelings monitoring system include air temperature sensor, relative air humidity sensor, soil temperature sensor, solar radiation, wind direction,
Wind speed, precipitation, atmospheric pressure, illuminance, dew point, sunshine, photosynthetically active radiation, ultraviolet radioactive, evaporation, carbon dioxide, leaf
Piece humidity sensor.
8. the irrigation system of rice field according to claim 1 based on fuzzy control decision, it is characterised in that：The meteorological prison
Examining system can be weather monitoring instrument, meteorological sensor or meteorological satellite.
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| CN114442705A (en) * | 2021-12-31 | 2022-05-06 | 浙江优控云科技有限公司 | Intelligent agricultural system based on Internet of things and control method |
| CN114600751A (en) * | 2022-03-23 | 2022-06-10 | 西安建筑科技大学 | Irrigation system and method based on computer vision monitoring |
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