CN107678410A - It is a kind of towards the intelligent control method of greenhouse, system and controller - Google Patents
It is a kind of towards the intelligent control method of greenhouse, system and controller 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
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication
- G05B19/4186—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication by protocol, e.g. MAP, TOP
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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
-
- 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
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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- 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
Present invention offer is a kind of to be included towards the intelligent control method of greenhouse, system and controller, methods described：S1, using single-factor control mode, multiple environmental variance data in control targe greenhouse, and each environmental variance real data of corresponding record and control implementation procedure data respectively, until record number reaches setting demand；S2, initial neural network model is obtained, and using each environmental variance real data and the control implementation procedure data, train the initial neural network model, obtain control neural network model；S3, based on multiple-factor control process, accordingly multiple environmental variance real data it is expected environmental data with given, utilize the control neural network model, the control data-oriented of the multiple-factor control process is obtained, and the respective environment variable data in the target greenhouse is correspondingly adjusted based on the control data-oriented.The present invention is simple to operate, can effectively reduce resource consumption and improves intelligent level, so as to effectively improve economic benefit.
Description
Technical field
The present invention relates to industrialized agriculture information-oriented production management technical field, more particularly, to one kind towards greenhouse ring
Intelligent control method, system and the controller in border.
Background technology
Greenhouse is the space being isolated from the outside built and formed using materials such as glass, films, can make full use of and work as
The natural environmental condition on ground.This industrialized agriculture is the effective work for the every monitor and control facility being had by inside, reaches improvement
Crop growth environment or the purpose for createing more preferably room climate.Greenhouse is the important component of industrialized agriculture, is existing
Most active part in important carrier during generationization agricultural development, and China's agricultural modernization evolution.
At present, green house control mainly has two ways.It is a kind of controlled with single-factor based on, ignore the coupling between envirment factor
Conjunction relation, common regulation and control object are temperature or illumination, by temperature either illumination control in constant scope or controller
Executing agency is directly controlled to control some envirment factor.The core of this quasi-controller is usually single-chip microcomputer or PLC, control mode
Simply, generally require continuous action or keep 24 hours openings, the consumption to the energy is very big, and the control method is applied to
It is higher to environmental requirement, and expensive crop.
Another kind is to carry out environmental Kuznets Curves by establishing intelligence system, and executing agency and PC ends are remotely connected,
The data that sensor collects are sent to PC ends in real time, and field personnel is judged, the action of control executing agency.Compare
In single-factor controller, the control system effectively reduces energy consumption, while realizes the control to multiple executing agencies,
But the system can not accomplish intelligentized control method, and not help reducing human cost.In addition, when the communication in PC ends and greenhouse
When being out of order, control system can not carry out auto-control.
To sum up, existing Technique for Controlling Greenhouse Environment, intelligent level is relatively low, and resource consumption is big, and overall economic benefit is not
It is high.
The content of the invention
In order to overcome above mentioned problem or solve the above problems at least in part, the present invention provides a kind of towards greenhouse
Intelligent control method, system and controller, reaching effective streamline operation, reducing resource consumption and improving intelligent water
It is flat, so as to effectively improve the purpose of economic benefit.
In a first aspect, the present invention provides a kind of intelligent control method towards greenhouse, including：S1, using single-factor
Control mode, respectively multiple environmental variance data in control targe greenhouse, and each environmental variance real data of corresponding record and control
Implementation procedure data processed, until record number reaches setting demand, the single-factor control mode refers to respectively with single environment
Variable is control targe, individually controls each environmental variance data；S2, initial neural network model is obtained, and utilize each institute
Environmental variance real data and the control implementation procedure data are stated, trains the initial neural network model, obtains control god
Through network model；S3, based on multiple-factor control process, accordingly multiple environmental variance real data it is expected environmental data, profit with given
With the control neural network model, obtain the control data-oriented of the multiple-factor control process, and based on it is described control to
Fixed number is according to the corresponding respective environment variable data for adjusting the target greenhouse.
Wherein, each environmental variance real data and the control implementation procedure data, instruction are utilized described in step S2
The step of practicing the initial neural network model further comprises：Using each environmental variance real data as input, correspond to respectively
It is described to control implementation procedure data to train the initial neural network model for output.
Wherein, the multiple environmental variance data further comprise gas concentration lwevel, intensity of illumination, temperature and humidity；
The control implementation procedure data further comprise, when each environmental variance real data reaches preset value, corresponding environment becomes
The duration of amount control implementation procedure.
Further, before the step of S3, methods described also includes：Obtained by remote control terminal described given
It is expected environmental data.
Wherein, the remote control terminal further comprises：Industrial computer, cloud server or user's intelligent terminal.
Second aspect, the present invention provide a kind of intelligence control system towards greenhouse, including：It is greenhouse controller, more
The environmental data sensor of multiple environmental variance real data, described in variable environment adjustment actuating mechanism and measurement target greenhouse
Greenhouse controller communicates to connect between each environmental data sensor and each environment adjustment actuating mechanism respectively；It is described
Greenhouse controller is used for：Using single-factor control mode, the multiple environmental variance data, and each ring of corresponding record are controlled respectively
Border variable real data and control implementation procedure data, until record number reaches setting demand, the single-factor control mode
Refer to respectively using single environmental variance as control targe, individually control each environmental variance data；Build initial neutral net
Model, and using each environmental variance real data and the control implementation procedure data, train the initial neutral net
Model, obtain control neural network model；And based on multiple-factor control process respective environment variable real data and to regular
Environmental data is hoped, using the control neural network model, obtains the control data-oriented of the multiple-factor control process, and base
The respective environment variable number in the target greenhouse is adjusted in environment adjustment actuating mechanism corresponding to the control data-oriented control
According to.
Further, the system also includes：Connect the remote control terminal of the greenhouse controller, the remote control
Terminal is used to obtain and issues the given expectation environmental data to the greenhouse controller.
Wherein, the greenhouse controller further comprises wireless communication module and/or wire communication module, the long-range control
Terminal processed further comprises wireless communication module and/or wire communication module, the greenhouse controller and remote control end
Communicated between end using udp protocol；The remote control terminal issues time interval to the greenhouse controller by setting
The given expectation environmental data is issued, the given expectation environmental data includes each environmental variance optimal value and each environmental variance
Value range；The greenhouse controller uploads the multiple-factor to the remote control terminal by setting uplink time interval and controlled
Journey respective environment variable real data.
Wherein, the greenhouse controller is further additionally operable to：When judging with the remote control terminal communicating interrupt, press
The given expectation environmental data that last time receives, automatically controls the environmental variance data in the target greenhouse, and by institute
State multiple-factor control process respective environment variable real data and be stored in the data memory module, until whole with the remote control
The communication at end recovers, and switches to multiple-factor control process.
The third aspect, the present invention provide a kind of intelligent controller towards greenhouse, including：Central processing unit and with
Data memory module, communication module, analog-to-digital converting module and the relay mould that the central processing unit communicates to connect respectively
Block；The environmental data sensing of multiple environmental variance real data in the analog-to-digital converting module connection measurement target greenhouse
The output end of device, the relay module connect the input of each environment adjustment actuating mechanism in the target greenhouse；The number
It is used to store towards the intelligent control algorithm of greenhouse, environmental variance real data, control implementation procedure number according to memory module
According to, multiple-factor control process respective environment variable real data, given it is expected environmental data and control data-oriented；The center
Processor obtains each environmental variance real data by the analog-to-digital converting module, and is used for：Using single-factor control
Mode processed, the multiple environmental variance data are controlled respectively, and each environmental variance real data of corresponding record and control performed
Number of passes evidence, until record number reaches setting demand, the single-factor control mode refers to respectively using single environmental variance as control
Target processed, individually control each environmental variance data；Initial neural network model is built, and it is real using each environmental variance
Border data and the control implementation procedure data, train the initial neural network model, obtain control neural network model；With
And based on the multiple-factor control process respective environment variable real data and the given expectation environmental data, using described
Control neural network model, the control data-oriented of the multiple-factor control process is obtained, and will by the relay module
The control data-oriented is issued to corresponding environment adjustment actuating mechanism, adjusts the respective environment variable number in the target greenhouse
According to.
It is provided by the invention a kind of towards the intelligent control method of greenhouse, system and controller, for greenhouse
The nonlinear characteristic of multiple-factor coupling, multiple-input and multiple-output Controlling model is built using based on neural network algorithm, by temperature
Indoor ambient parameter (temperature, humidity, CO2Concentration and intensity of illumination) carry out real time automatic detection, Intelligent adjustment greenhouse.
In addition, by setting remote control terminal, controlled available for greenhouse group system, the management and control to ten thousand greenhouses is realized, when single
During communicating interrupt between greenhouse controller and remote control terminal, greenhouse controller can carry out autonomous control to greenhouse.This
Invent it is simple to operate, stable and reliable in work, practical, can effectively reduce resource consumption and improve intelligent level, so as to
Effectively improve economic benefit.
Brief description of the drawings
Fig. 1 is a kind of flow chart of intelligent control method towards greenhouse of the embodiment of the present invention；
Fig. 2 is a kind of training managing process flow diagram flow chart of neutral net of the embodiment of the present invention；
Fig. 3 is a kind of overall architecture schematic diagram of intelligence control system towards greenhouse of the embodiment of the present invention；
Fig. 4 is a kind of control system overall architecture schematic diagram of the embodiment of the present invention；
Fig. 5 is a kind of greenhouse clustered control structure composed schematic diagram of the embodiment of the present invention；
Fig. 6 is a kind of network interface communicating circuit structural representation of the embodiment of the present invention；
Fig. 7 is a kind of structured flowchart of intelligent controller towards greenhouse of the embodiment of the present invention；
Fig. 8 is a kind of overall structure diagram of intelligent controller of the embodiment of the present invention；
Fig. 9 is a kind of AI interface circuit figures of intelligent controller of the embodiment of the present invention；
Figure 10 is a kind of plug type AI interface terminals subgraphs of intelligent controller of the embodiment of the present invention；
Figure 11 is a kind of pluggable DO interface circuit figures of intelligent controller of the embodiment of the present invention；
Figure 12 is that a kind of hand push button of intelligent controller of the embodiment of the present invention connects circuit diagram；
Figure 13 is that a kind of buzzer of intelligent controller of the embodiment of the present invention connects circuit diagram；
Figure 14 is a kind of LCD interface circuit figures of intelligent controller of the embodiment of the present invention；
Figure 15 is a kind of runnable interface screenshotss schematic diagram of Intelligent Greenhouse Controller of the embodiment of the present invention.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached in the embodiment of the present invention
Figure, the technical scheme in the present invention is clearly and completely described, it is clear that described embodiment is one of the present invention
Divide embodiment, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not making
The every other embodiment obtained on the premise of creative work, belongs to the scope of protection of the invention.
As the one side of the embodiment of the present invention, the present embodiment provides a kind of intelligent control side towards greenhouse
Method, it is a kind of flow chart of intelligent control method towards greenhouse of the embodiment of the present invention with reference to figure 1, including：
S1, using single-factor control mode, respectively multiple environmental variance data in control targe greenhouse, and corresponding record is each
Environmental variance real data and control implementation procedure data, until record number reaches setting demand, the single-factor controlling party
Formula refers to respectively using single environmental variance as control targe, individually controls each environmental variance data.
It is to be understood that running the starting stage in controller, the coupling of multiple environmental variances in target greenhouse is not considered first
Relation, respectively using each single environmental variance in target greenhouse as control targe, individually control each environmental variance data.In real time
Gather and record each environmental variance real data in control process, while corresponding record is issued to the control of control requirement in the data
The control implementation procedure data of process processed.The confining spectrum of controller operation starting stage is to be put into operation since controller,
Reach the period of setting demand to data acquisition number.
It is wherein optional, the multiple environmental variance data further comprise gas concentration lwevel, intensity of illumination, temperature and
Humidity；The control implementation procedure data further comprise, when each environmental variance real data reaches preset value, corresponding ring
The duration of border Variable Control implementation procedure.
It is to be understood that greenhouse is collectively constituted by multinomial environment parameter, coupling between each environment parameter be present
Relation, each environment parameter data are adjusted by associating, reach the greenhouse condition of needs.The present embodiment considers greenhouse
Gas concentration lwevel, intensity of illumination, temperature and humidity parameter, the numerical value that four environmental variances are adjusted by associating reach needs
Greenhouse condition.
Assuming that setting times of collection as N, the starting stage is run in controller, greenhouse controller is in a manner of single-factor controls
Work, when some amount or several amounts exceed preset range, during the action of each executing agency of greenhouse controller setting
Between ti=0, goal of regulation and control is the optimal value of corresponding amount, at the end of regulation and control, records regulation and control time, the time corresponding to each amount
As control implementation procedure data.
S2, initial neural network model is obtained, and performed using each environmental variance real data and the control
Number of passes evidence, the initial neural network model is trained, obtain control neural network model.
It is to be understood that this step realizes foundation and the training process of target control neural network model.It is determined that input,
Output variable information, and according to input, after output variable information establishes initial neural network model, initial neural network model
Certain primary data is needed to be trained, but greenhouse is relative complex, collection and the screening for carrying out data are relatively more tired
It is difficult.The present embodiment is trained the collection and training of data in the starting stage that controller is run.
It is wherein optional, each environmental variance real data and the control implementation procedure number are utilized described in step S2
According to, train the initial neural network model the step of further comprise：Using each environmental variance real data as input, it is right
Answer and each described control implementation procedure data to train the initial neural network model for output.
The input and output of neutral net can represent that input vector is designated as X=(x with two one-dimensional vectors1,x1,K,
xn), output vector is designated as T=(t1,t1,K,tm).Wherein x1、x2、...、xnN input of respectively neural network model becomes
Amount, t1、t2、...、tnRespectively m output variable of neural network model.
For example, for the gas concentration lwevel in above-mentioned steps, intensity of illumination, temperature and humidity parameter, if neutral net
Input variable include the actual gas concentration lwevel of greenhouse, intensity of illumination, temperature and humidity parameter, and setting dioxy
Change concentration of carbon, intensity of illumination and temperature value；Output variable includes the execution time of regulation of carbon dioxide concentration executing agency, regulation
Execution time, the execution time of regulation temperature actuator and the holding for regulation humidity executing agency of intensity of illumination executing agency
The row time.The input vector for then building neural network model is X=(x1,x1,K,x7), output vector is T=(t1,t1,K,t4)。
Regulation and control terminate, it is necessary to corresponding input and output are normalized, the present embodiment uses sigmoid every time
Function is normalized, and formula is as follows：
After training sample collection capacity reaches setting value, the flow according to Fig. 2 is carried out to initial neural network model
Training, Fig. 2 are a kind of training managing process flow diagram flow chart of neutral net of the embodiment of the present invention, including：To current group training sample
It is normalized, and the neural network algorithm meter of initial neural network model is carried out to the training sample after normalized
Calculate；Judge that all training samples whether using finishing, if so, then terminating training program, are otherwise chosen next group of training sample and entered
Row neural network algorithm calculates；Carry out forward calculation and whether error in judgement rate meets given threshold, if so, then judging all instructions
Whether practice sample using finishing, otherwise carry out backpropagation, update neural network weight and threshold value, continue forward calculation,
Until error rate reaches given threshold.
S3, based on multiple-factor control process, accordingly multiple environmental variance real data it is expected environmental data with given, utilize
The control neural network model, the control data-oriented of the multiple-factor control process is obtained, and it is given based on the control
Data correspondingly adjust the respective environment variable data in the target greenhouse.
It is to be understood that according to collection of the above-mentioned steps completion to training sample data and to initial neural network model
Training after, into multiple-factor control process.In multiple-factor control process, multiple-factor control is first gathered by environmental data sensor
The environmental variance real data of process processed, and the given expectation environmental data that rule of thumb algorithm is calculated is obtained, this is given
It is expected that environmental data can include greenhouse optimal value or term of reference etc..
By the environmental variance real data of multiple-factor control process and given expectation environmental data together as control nerve
The input variable of network, calculated through control neural network Model coupling, obtain the control data-oriented of control greenhouse data,
The data-oriented corresponds to multiple governor motions.Then environmental variance governor motion corresponding to control exports according to neural network model
Control data-oriented carry out greenhouse variable regulation.
A kind of intelligent control method towards greenhouse provided in an embodiment of the present invention, for greenhouse multiple-factor coupling
The nonlinear characteristic of conjunction, multiple-input and multiple-output Controlling model is built using based on neural network algorithm, by the ring in greenhouse
Border parameter carries out real time automatic detection, Intelligent adjustment greenhouse.Because neutral net can preferably simulate complex environment, no
With coupled relation complicated between the computing environment factor again；Executing agency need not be always maintained at opening, simple to operate, work
Make it is reliable and stable, can effectively reduce resource consumption and improve intelligent level, so as to effectively improve economic benefit.
Further, before the step of S3, methods described also includes：Obtained by remote control terminal described given
It is expected environmental data.
It is to be understood that in multiple-factor control process, greenhouse controller is needed according to actual environment data and to regular
Hope environmental data carry out neural network algorithm calculating, so as to adjusted greenhouse variable data controlled quentity controlled variable, that is, control to
Fixed number evidence.Acted by environmental variance governor motion corresponding to control by control data-oriented, the reality in adjustment target greenhouse
Environmental data, actual environment data value is set to tend to given environmental data.
Given expectation environmental data therein is typically to be calculated to obtain according to factors such as agrotypes in greenhouse, or according to warp
Test the data of plant growth or optimal span in the most suitable target greenhouse of acquisition.Usual warm indoor crops
And plant growth conditions are not changeless, therefore the given expectation environmental data in greenhouse controller is to need real-time update
's.
Calculated and obtained by remote control terminal, or by inputting remote control terminal by manually rule of thumb setting, then
Sent by remote control terminal to greenhouse controller, target greenhouse variable data is correspondingly adjusted for greenhouse controller.
Meanwhile for each remote control terminal, control instruction can be sent to more greenhouse controllers, realized to temperature
The centralized management of room cluster.
In one embodiment, the remote control terminal further comprises：Industrial computer, cloud server or user's intelligence
Terminal.
It is to be understood that given it is expected that environmental data can either cloud server or user hold by neighbouring industrial computer
Some intelligent mobile terminals obtain and are sent to target greenhouse controller.
A kind of intelligent control method towards greenhouse provided in an embodiment of the present invention, by by remote control terminal not
Suitable environment parameter is given in disconnected renewal greenhouse, and greenhouse data can be made to be always held at the optimal model of suitable plant growth
Enclose, meet that chamber crop grows demand, produce higher economic benefit, and for greenhouse cluster, it is possible to achieve to more greenhouses
Unified intelligent control.
As the other side of the embodiment of the present invention, the present embodiment provides a kind of intelligent control system towards greenhouse
System, it is a kind of overall architecture schematic diagram of intelligence control system towards greenhouse of the embodiment of the present invention with reference to figure 3, including：
The environment of multiple environmental variance real data in greenhouse controller 1, multivariable environment adjustment actuating mechanism 2 and measurement target greenhouse
Data pick-up 3.
Wherein, greenhouse controller 1 communicates between each environmental data sensor 3 and each environment adjustment actuating mechanism 2 respectively
Connection.Greenhouse controller 1 is used for：Using single-factor control mode, the multiple environmental variance data, and corresponding note are controlled respectively
Each environmental variance real data and control implementation procedure data are recorded, until record number reaches setting demand, the single-factor control
Mode processed refers to respectively using single environmental variance as control targe, individually controls each environmental variance data；The initial god of structure
Through network model, and using each environmental variance real data and the control implementation procedure data, train the initial god
Through network model, control neural network model is obtained；And based on multiple-factor control process respective environment variable real data and
It is given it is expected environmental data, using the control neural network model, the control of the multiple-factor control process is obtained to fixed number
According to, and based on the respective rings for controlling environment adjustment actuating mechanism 2 corresponding to data-oriented control to adjust the target greenhouse
Border variable data.
It is to be understood that from the point of view of by system architecture, control system includes three parts, i.e. information gathering part, control
Device part and executing agency part processed, while controller part and information gathering part and executing agency part pass through communicate respectively
Realize communication connection in part.Control system integrated stand composition refers to.
Information gathering part is mainly made up of environmental data sensor 3, including carbon dioxide (CO2) sensor, temperature pass
Sensor, humidity sensor and optical sensor etc..The monitoring of greenhouse is completed by environmental data sensor 3, environmental data
Sensor 3 monitors greenhouse environment parameter in real time, and in the certain time of setting, environmental data sensor 3 carries out a data and sent.
The data of transmission are the data gathered recently, because there is multiple sensor nodes in greenhouse, the data of each node collection
It is different from, therefore, inside controller needs to handle data, calculates the numerical value of a determination.
Greenhouse area is larger, and monitoring to whole greenhouse can not be completed by relying solely on one group of environmental data sensor 3,
Therefore, it is necessary to which structure according to greenhouse, is divided into several pieces of regions, each piece of region sets identical sensor network by greenhouse
Network is monitored.
Executing agency part is made up of environment adjustment actuating mechanism 2, and executing agency is to the output according to greenhouse controller 1
Data-oriented is controlled to perform adjustment action.Environment adjustment actuating mechanism 2 connects greenhouse by passing sequentially through contactor with relay
Controller 1.Environment adjustment actuating mechanism 2 is driven by contactor, and driven object includes heater, blower fan, CO2Release,
Alarm, vent window, insulation quilt and illumination facilities etc., the DO mouths of greenhouse controller 1 are by the break-make of control relay and continue
Time controls the motor action of environment adjustment actuating mechanism 2.
Controller part is made up of greenhouse controller 1, is run the starting stage in greenhouse controller 1, is not first considered target temperature
The coupled relation of multiple environmental variances in room, greenhouse controller 1 is respectively using each single environmental variance in target greenhouse as control mesh
Mark, individually controls each environmental variance data.
Meanwhile greenhouse controller 1 gathers in real time and records each environmental variance real data in control process, and corresponding record
The control implementation procedure data for the control process that control requires are issued in the data.Greenhouse controller 1 runs the starting stage
Confining spectrum is to be put into operation since greenhouse controller 1, and the period of setting demand is reached to data acquisition number.
Greenhouse controller 1 also realizes foundation and the training process of target control neural network model.It is determined that input, output
Variable information, and according to input, after output variable information establishes initial neural network model, greenhouse controller 1 is needed to first
Beginning neural network model is trained with certain primary data, but greenhouse is relative complex, carries out the collection of data
It is relatively difficult with screening.The present embodiment is trained the collection and training of data in the starting stage that greenhouse controller 1 is run.When
After training sample collection capacity reaches setting value, greenhouse controller 1 is trained to initial neural network model, obtains the control of needs
Neural network model processed.
After neural network model trains, greenhouse controller 1 is transferred to the intelligent control stage.When the environment in greenhouse becomes
Change, during beyond default scope, neural network model is started working.Opening timing device, and timer is set to 0, now, foundation is prolonged
When function, the data are fitted into function, the execution time of the function, are exactly that result of calculation conversion respective execution mechanisms continue
Time.
Before timer execution, DO mouths put 1, and relay conducting, executing agency acts.Timer timing terminates, and DO mouths are put
0, now relay be no longer turned on.After execution, wait for a period of time, then the data that sensor collects judged,
Judge whether greenhouse meets preset requirement, if do not met, continue to adjust.Specific conducting flow with reference to figure 4,
For a kind of control system overall architecture schematic diagram of the embodiment of the present invention.
A kind of intelligence control system towards greenhouse provided in an embodiment of the present invention, for greenhouse multiple-factor coupling
The nonlinear characteristic of conjunction, by setting greenhouse controller 1 to build multiple-input and multiple-output control mould using based on neural network algorithm
Type, real time automatic detection, Intelligent adjustment greenhouse are carried out to the ambient parameter in greenhouse.Because neutral net can be preferable
Complex environment is simulated, without coupled relation complicated between the computing environment factor again；Executing agency need not be always maintained at opening
State, it is simple to operate, stable and reliable in work, can effectively reduce resource consumption and improve intelligent level, so as to effectively improve
Economic benefit.
Further, the system also includes：Connect the remote control terminal of the greenhouse controller, the remote control
Terminal is used to obtain and issues the given expectation environmental data to the greenhouse controller.
It is to be understood that the greenhouse controller 1 of the present embodiment is the controller towards remote control terminal, it is greenhouse cluster
The control towards every greenhouse layer of control system.Remote control terminal can use local industrial computer, long-range cloud server
Or user's hand-held intelligent terminal.Remote control terminal calculates according to factors such as agrotypes in greenhouse obtains given expectation environment
The data of plant growth or optimal span in data, or the most suitable target greenhouse rule of thumb obtained.It
Remote control terminal sends the regular prestige environmental data of acquisition to greenhouse controller 1 afterwards, for the corresponding regulation mesh of greenhouse controller 1
Mark greenhouse variable data.
In addition, a remote control terminal can control multiple greenhouse controllers 1, responsible pair of each greenhouse controller 1 simultaneously
The specific control in greenhouse is answered, the present embodiment remote control terminal realizes that the structure composed of greenhouse clustered control is this hair with reference to figure 5
A kind of bright greenhouse clustered control structure composed schematic diagram of embodiment.Remote control terminal issues control instruction, greenhouse controller 1
The ambient parameter in greenhouse is controlled according to control instruction, and the environmental data monitored in real time is sent to remote control terminal.
A kind of intelligence control system towards greenhouse provided in an embodiment of the present invention, by setting remote control whole
End, realize that constantly updating suitable environment parameter in greenhouse gives, and can make greenhouse data be always held at suitable crop life
Long optimum range, meet that chamber crop grows demand, produce higher economic benefit, and for greenhouse cluster, it is possible to achieve
To the unified intelligent control in more greenhouses.
Wherein optional, the greenhouse controller further comprises wireless communication module and/or wire communication module, described
Remote control terminal further comprises wireless communication module and/or wire communication module, the greenhouse controller with it is described long-range
Communicated between control terminal using udp protocol；The remote control terminal issues time interval to the greenhouse by setting
Controller issues the given expectation environmental data, and the given expectation environmental data includes each environmental variance optimal value and each ring
Border range of variables value；The greenhouse controller uploads the multiple-factor by setting uplink time interval to the remote control terminal
Control process respective environment variable real data.
It is to be understood that because in greenhouse clustered control, a remote control terminal is logical with multiple greenhouse controllers 1
Letter, in order to ensure communication efficiency and traffic rate, using udp protocol, greenhouse controller 1 and the communication modes of green house control terminal
There are two kinds of selections, wireless telecommunications or wire communication.Greenhouse controller 1 is provided with network interface, is entered by optical fiber and remote control terminal
Row communication.
Meanwhile the inside of greenhouse controller 1 is provided with WIFI module and 4G modules, remote control terminal is logical with greenhouse controller 1
4G/WIFI communications are crossed, i.e., greenhouse controller 1 can also be communicated wirelessly with remote control terminal, Neng Gouyou
The scope of application of effect extension greenhouse controller.
It is a kind of network interface communicating circuit structural representation of the embodiment of the present invention with reference to figure 6 by taking network interface communicating circuit as an example,
The core component of network interface communicating circuit is LAN8720A, and the information received is converted to rs 232 serial interface signal by network port circuit, with string
Degree of lip-rounding formula is sent in processor.
Week about, remote control terminal issues a control instruction to greenhouse controller 1, and the instruction is included in two aspects
Hold：The optimal value x_ of greenhouse parameterbestWith term of reference value (xmin,xmax), the target of greenhouse controller 1 is by greenhouse ring
Border is maintained in default scope.Uploaded in addition, greenhouse controller 1 every two hours carries out a data, upload the content of data
For the current environmental information in greenhouse.
A kind of intelligence control system towards greenhouse provided in an embodiment of the present invention, by remote control terminal with
The design of communication protocol and communication mode between greenhouse controller, can ensure communication efficiency and traffic rate.
Wherein optional, the greenhouse controller is further additionally operable to：In judging to communicate with the remote control terminal
When disconnected, the given expectation environmental data that is received by last time, the environmental variance data in the target greenhouse are automatically controlled,
And the multiple-factor control process respective environment variable real data is stored in the data memory module, until with it is described long-range
Control the communication of terminal to recover, switch to multiple-factor control process.
It is to be understood that greenhouse controller 1 and remote control terminal be by network communication, but greenhouse controller 1 and long-range
Communication between control terminal may be because that some reasons are interrupted.Under normal circumstances, greenhouse controller 1 is to remote control terminal
Data are sent, after remote control terminal receives data, signal is finished to the feedback reception of greenhouse controller 1.In greenhouse controller 1
After having sent data, wait receives signal and starts timing.
After beyond the default time, greenhouse controller 1 sends data again, if not receiving feedback, green house control also
Device 1 is determined as the communicating interrupt with remote control terminal.Now, greenhouse controller 1 carries out automatic control mode, and environment is maintained
In the range of newest setting, the data that sensor collects do not retransmit, and are stored temporarily in local database.
After the communication of greenhouse controller 1 and remote control terminal is normal, when remote control terminal issues extraction signal, temperature
Chamber controller 1 sends data, and wait receives signal, after receiving, empties the data of storage.Now, green house control
Device 1 is considered as communication normally, receives the control instruction of remote control terminal, and greenhouse is controlled according to control instruction.
A kind of intelligence control system towards greenhouse provided in an embodiment of the present invention, by setting greenhouse controller
It from control model, can ensure after the communication disruption with remote control terminal, continue to carry out greenhouse according to newest data-oriented
Environmental Kuznets Curves, system crash can be effectively avoided, brings economic loss.
As the another aspect of the embodiment of the present invention, the present embodiment provides a kind of intelligent control towards greenhouse
Device, it is a kind of structured flowchart of intelligent controller towards greenhouse of the embodiment of the present invention with reference to figure 7, including：Central processing
Device 101 and the data memory module 102 communicated to connect respectively with central processing unit 101, communication module 103, analog/digital turn
Change the mold block 104 and relay module 105.
Wherein, the connection of analog-to-digital converting module 104 measures the environment of multiple environmental variance real data in target greenhouse
The output end of data pick-up, relay module 105 connect the input of each environment adjustment actuating mechanism in the target greenhouse；
Data memory module 102 is used to store to be performed towards the intelligent control algorithm of greenhouse, environmental variance real data, control
Number of passes evidence, multiple-factor control process environmental variance real data, given expectation environmental data and control data-oriented.
Central processing unit 101 obtains each environmental variance real data by analog-to-digital converting module 104, is used in combination
In：
Using single-factor control mode, the multiple environmental variance data, and each environmental variance of corresponding record are controlled respectively
Real data and control implementation procedure data, until record number reaches setting demand, the single-factor control mode refers to point
Not using single environmental variance as control targe, each environmental variance data are individually controlled；
Initial neural network model is built, and utilizes each environmental variance real data and the control implementation procedure number
According to, the training initial neural network model, acquisition control neural network model；
And based on the multiple-factor control process respective environment variable real data and the given expectation environment number
According to, using the control neural network model, obtain the control data-oriented of the multiple-factor control process, and by it is described after
The control data-oriented is issued to corresponding environment adjustment actuating mechanism by electrical appliance module, adjusts the corresponding of the target greenhouse
Environmental variance data.
It is to be understood that controller during green house control is performed, it is necessary to according to greenhouse actual environment data and give
Determine expected data calculating control to give, the given corresponding execution unit of needs of control of acquisition, which corresponds to, to be performed, while needs are to control
The data of process processed are stored.Therefore need to set corresponding functional unit in the controller.Specifically include：For obtaining ring
It is the analog-to-digital converting module 104 of border data, the central processing unit 101 calculated for control algolithm, fixed for passing control to
The relay module 105 of data, the data memory module 102 for storage control process data and for being communicated with relevant device
Communication module 103.
In one embodiment, central processing unit uses arm processor, sets 8 simulation inputs (AI) on the controller
Interface and 8 numeral output (DO) interfaces, with reference to figure 8, the overall structure for a kind of intelligent controller of the embodiment of the present invention is shown
It is intended to, AI interfaces are used for external sensor, and 2 RS485 serial ports and WIFI module are used for external other sensors or sensing
Device node, the data of AI interfaces collection are changed by 16 built-in A/D modular converters, and data are sent to central processing unit
GPIO port, the port is arranged to input.
The reading for carrying out sensing data in a program is set, and every 30s, the GPIO mouths port of 8 connection AI mouths is entered
Row single pass, read data.RS485 communication modes are serial communication, when in use, can by external RS485 expanders
With external multiple equipment, single RS485 serial ports could support up external 32 nodes.WIFI module can be used for and sensor section
Point or sensor are communicated, and WIFI module can be with real-time reception sensing data, but data is not preserved, in greenhouse control
Device processed just preserved during digital independent.
The Interface design circuit of AI interfaces is as shown in figure 9, Fig. 9 is a kind of AI interfaces of intelligent controller of the embodiment of the present invention
Circuit diagram.The A/D conversion elements that the present embodiment uses are AD7699, and the element has 8 input ports, and the value of input is adopted for sensor
The magnitude of voltage collected, magnitude of voltage and reference voltage compare, and real output valve is obtained according to this.
For the ease of repair and maintenance, AI interfaces use plug-in terminal, the design circuit knot of terminal in one embodiment
Structure is as shown in Figure 10, and Figure 10 is a kind of plug type AI interface terminals subgraphs of intelligent controller of the embodiment of the present invention.
The present embodiment sets 8 DO interfaces on the controller, and corresponding environment adjustment actuating mechanism is controlled with corresponding.DO ends
Mouthful voltage is 5V, is not directly disposed as external terminal, each external relay in DO ports, when DO puts 1 in port, relay
Device turns on, now the external terminal conducting of relay.
Environment adjustment actuating mechanism power is larger, and the external DC24 of terminal of relay turns AC220 relays, should by control
Relay turns on, and then the conducting of control contactor, final to drive executing agency's action.In addition to easy access is safeguarded,
In one embodiment, DO interfaces use plug type structure, circuit structure such as Figure 11 institutes of plug type structure DO terminal interfaces
Show, Figure 11 is a kind of plug type DO interface circuit figures of intelligent controller of the embodiment of the present invention.
In another embodiment, in order to prevent some contingencies from causing greenhouse controller cisco unity malfunction, controlling
Manual operation function, the manually operated executing agency that can directly control in greenhouse are added on device processed.The manual behaviour of controller
Make, by button completion, button have been added on controller housing, manual operation button is directly connected on relay, when pressing
During operation button, relay directly turns on, and then controls executing agency's action.The circuit structure of button is as shown in figure 12, Figure 12
Circuit diagram is connected for a kind of hand push button of intelligent controller of the embodiment of the present invention.
In yet another embodiment, in order to prevent executing agency's perseveration in greenhouse, in the controller, to each ring
Border controling parameter is provided with corresponding adjustable range.If some ambient parameter in greenhouse is not up to optimal value, but in phase
In the range of answering, controller will not send action command.
In yet another embodiment, it is contemplated that controller is in auto-control, in fact it could happen that because the damage of executing agency
Or faulty sensor causes controller can not complete the situation of regulation and control, sets time of fire alarming on the controller.The control of controller
System is completed by DO mouths, after the completion of the control of a DO mouth, monitoring period t is set, in monitoring period t, if the equipment
Action, i.e., there is level change in DO mouths, then start to be monitored it.If DO mouths level change continuously occurs reaching setting time
Number, such as three times, then controller stopping act, and send warning message to remote control terminal.
In addition, also setting alarm lamp on the controller, the triggering of alarm lamp is under software control, is breaking down or mistake
Afterwards, manage alarm lamp and the GPIO mouths of buzzer put 1, control the triode ON of the two, alarm lamp lights, and buzzer persistently moves
Make.Only controller is switched to after manual mode and can just stopped by staff, and the control circuit of buzzer is as shown in figure 13, figure
13 connect circuit diagram for a kind of buzzer of intelligent controller of the embodiment of the present invention.The control circuit of alarm lamp controls with buzzer
Circuit is identical.
In yet another embodiment, set controller that there is screen display function.By setting LCD to show in the controller
Screen, realize and operation interface and information displaying interface are set.The hardware circuit of display screen is as shown in figure 14, and Figure 14 is implemented for the present invention
A kind of LCD interface circuit figures of intelligent controller of example.
The content of LCD display displaying includes inquiry control record, the situation for inquiring about current greenhouse and current temperature
Ecotopia situation of room etc..With reference to figure 15, show for a kind of runnable interface screenshotss of Intelligent Greenhouse Controller of the embodiment of the present invention
It is intended to, shows that the controller of the embodiment of the present invention can be to the temperature of greenhouse, humidity, illuminance, dense carbon dioxide in figure
The input quantity such as degree, soil conductivity and soil moisture content measures and shows, while to top portion ventilation window motor, bottom ventilation
Window motor, volume by motor, carbon dioxide release, plant root warmer, top portion ventilation window close, bottom ventilation window close and
Volume is monitored and shown by motor mobile phone actuator.
A kind of intelligent controller towards greenhouse provided in an embodiment of the present invention, coupled for greenhouse multiple-factor
Nonlinear characteristic, using based on neural network algorithm build multiple-input and multiple-output Controlling model, by the environment in greenhouse
Parameter carries out real time automatic detection, Intelligent adjustment greenhouse.It is simple to operate, stable and reliable in work, can effectively reduce resource
Consumption and raising intelligent level, so as to effectively improve economic benefit.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should be understood：It still can be right
Technical scheme described in foregoing embodiments is modified, or carries out equivalent substitution to which part technical characteristic；And this
A little modifications are replaced, and the essence of appropriate technical solution is departed from the spirit and model of various embodiments of the present invention technical scheme
Enclose.
Claims (10)
- A kind of 1. intelligent control method towards greenhouse, it is characterised in that including：S1, using single-factor control mode, respectively multiple environmental variance data in control targe greenhouse, and each environment of corresponding record Variable real data and control implementation procedure data, until record number reaches setting demand, the single-factor control mode is Refer to respectively using single environmental variance as control targe, individually control each environmental variance data；S2, initial neural network model is obtained, and utilize each environmental variance real data and the control implementation procedure number According to, the training initial neural network model, acquisition control neural network model；S3, based on multiple-factor control process accordingly multiple environmental variance real data and it is given it is expected environmental data, using described Control neural network model, the control data-oriented of the multiple-factor control process is obtained, and be based on the control data-oriented The corresponding respective environment variable data for adjusting the target greenhouse.
- 2. according to the method for claim 1, it is characterised in that each environmental variance actual number is utilized described in step S2 Further comprise according to described the step of controlling implementation procedure data, training the initial neural network model：By input of each environmental variance real data, it is corresponding it is each it is described to control implementation procedure data be output, described in training Initial neural network model.
- 3. according to the method for claim 1, it is characterised in that：The multiple environmental variance data further comprise gas concentration lwevel, intensity of illumination, temperature and humidity；The control implementation procedure data further comprise, when each environmental variance real data reaches preset value, corresponding ring The duration of border Variable Control implementation procedure.
- 4. according to any described method in claims 1 to 3, it is characterised in that before the step of S3, in addition to：The given expectation environmental data is obtained by remote control terminal.
- 5. according to the method for claim 4, it is characterised in that the remote control terminal further comprises：Industrial computer, cloud server or user's intelligent terminal.
- A kind of 6. intelligence control system towards greenhouse, it is characterised in that including：Greenhouse controller, multivariable environment are adjusted Save executing agency and measure the environmental data sensor of multiple environmental variance real data in target greenhouse, the greenhouse controller Communicated to connect respectively between each environmental data sensor and each environment adjustment actuating mechanism；The greenhouse controller is used for：Using single-factor control mode, the multiple environmental variance data are controlled respectively, and each environmental variance of corresponding record is actual Data and control implementation procedure data, until record number reaches setting demand, the single-factor control mode refer to respectively with Single environmental variance is control targe, individually controls each environmental variance data；Initial neural network model is built, and utilizes each environmental variance real data and the control implementation procedure data, The initial neural network model is trained, obtains control neural network model；And based on multiple-factor control process respective environment variable real data and given expectation environmental data, utilize the control Neural network model processed, the control data-oriented of the multiple-factor control process is obtained, and based on the control data-oriented control Environment adjustment actuating mechanism corresponding to system adjusts the respective environment variable data in the target greenhouse.
- 7. system according to claim 6, it is characterised in that also include：Connect the remote control of the greenhouse controller Terminal, the remote control terminal are used to obtain and issue the given expectation environmental data to the greenhouse controller.
- 8. system according to claim 7, it is characterised in that the greenhouse controller further comprises wireless communication module And/or wire communication module, the remote control terminal further comprise wireless communication module and/or wire communication module, institute State and communicated between greenhouse controller and the remote control terminal using udp protocol；The remote control terminal issues time interval by setting and issues the given expectation environment number to the greenhouse controller According to the given expectation environmental data includes each environmental variance optimal value and each environmental variance value range；The greenhouse controller uploads the multiple-factor control process by setting uplink time interval to the remote control terminal Respective environment variable real data.
- 9. system according to claim 8, it is characterised in that the greenhouse controller is further additionally operable to：When judge with the remote control terminal communicating interrupt when, by last time receive the given expectation environmental data, Automatically control the environmental variance data in the target greenhouse, and by the multiple-factor control process respective environment variable real data The data memory module is stored in, until recovering with the communication of the remote control terminal, switches to multiple-factor control process.
- A kind of 10. intelligent controller towards greenhouse, it is characterised in that including：Central processing unit and with the center Data memory module, communication module, analog-to-digital converting module and the relay module that processor communicates to connect respectively；The environmental data sensing of multiple environmental variance real data in the analog-to-digital converting module connection measurement target greenhouse The output end of device, the relay module connect the input of each environment adjustment actuating mechanism in the target greenhouse；The data memory module is used to store towards the intelligent control algorithm of greenhouse, environmental variance real data, control Implementation procedure data, multiple-factor control process respective environment variable real data, give and it is expected environmental data and control to fixed number According to；The central processing unit obtains each environmental variance real data by the analog-to-digital converting module, and is used for：Using single-factor control mode, the multiple environmental variance data are controlled respectively, and each environmental variance of corresponding record is actual Data and control implementation procedure data, until record number reaches setting demand, the single-factor control mode refer to respectively with Single environmental variance is control targe, individually controls each environmental variance data；Initial neural network model is built, and utilizes each environmental variance real data and the control implementation procedure data, The initial neural network model is trained, obtains control neural network model；And based on the multiple-factor control process respective environment variable real data and the given expectation environmental data, profit With the control neural network model, the control data-oriented of the multiple-factor control process is obtained, and passes through the relay The control data-oriented is issued to corresponding environment adjustment actuating mechanism by module, adjusts the respective environment in the target greenhouse Variable data.
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| CN113534862A (en) * | 2021-07-09 | 2021-10-22 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | System and method for controlling gas concentration in culture chamber |
| CN113534862B (en) * | 2021-07-09 | 2024-05-03 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | Gas concentration control system and method for culture cavity |
| CN113867167A (en) * | 2021-10-28 | 2021-12-31 | 中央司法警官学院 | Household environment intelligent monitoring method and system based on artificial neural network |
| CN114594814A (en) * | 2022-03-07 | 2022-06-07 | 上海工程技术大学 | Digital control device based on artificial intelligence |
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