CN107390754A - Intelligent plant growth environment adjustment system and method based on Internet of Things cloud platform - Google Patents
Intelligent plant growth environment adjustment system and method based on Internet of Things cloud platform Download PDFInfo
- Publication number
- CN107390754A CN107390754A CN201710758513.2A CN201710758513A CN107390754A CN 107390754 A CN107390754 A CN 107390754A CN 201710758513 A CN201710758513 A CN 201710758513A CN 107390754 A CN107390754 A CN 107390754A
- Authority
- CN
- China
- Prior art keywords
- environment parameter
- growing
- server
- growing environment
- environment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000008635 plant growth Effects 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012549 training Methods 0.000 claims abstract description 71
- 230000007246 mechanism Effects 0.000 claims abstract description 53
- 230000033228 biological regulation Effects 0.000 claims abstract description 38
- 238000013528 artificial neural network Methods 0.000 claims abstract description 35
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 31
- 230000002068 genetic effect Effects 0.000 claims abstract description 25
- 230000000243 photosynthetic effect Effects 0.000 claims abstract description 20
- 241000196324 Embryophyta Species 0.000 claims description 68
- 239000002689 soil Substances 0.000 claims description 17
- 230000012010 growth Effects 0.000 claims description 12
- 230000008649 adaptation response Effects 0.000 claims description 8
- 238000005316 response function Methods 0.000 claims description 8
- 241000208340 Araliaceae Species 0.000 claims description 7
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims description 7
- 235000003140 Panax quinquefolius Nutrition 0.000 claims description 7
- 230000004075 alteration Effects 0.000 claims description 7
- 235000008434 ginseng Nutrition 0.000 claims description 7
- 230000001537 neural effect Effects 0.000 claims description 7
- 108090000623 proteins and genes Proteins 0.000 claims description 7
- 230000005855 radiation Effects 0.000 claims description 6
- 238000009423 ventilation Methods 0.000 claims description 6
- 230000033001 locomotion Effects 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 230000000295 complement effect Effects 0.000 claims description 4
- 238000003973 irrigation Methods 0.000 claims description 4
- 230000002262 irrigation Effects 0.000 claims description 4
- 230000028016 temperature homeostasis Effects 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 2
- 238000000605 extraction Methods 0.000 claims description 2
- 230000006855 networking Effects 0.000 claims 1
- 230000004907 flux Effects 0.000 description 9
- 230000006870 function Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 230000006399 behavior Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- GWEVSGVZZGPLCZ-UHFFFAOYSA-N Titan oxide Chemical compound O=[Ti]=O GWEVSGVZZGPLCZ-UHFFFAOYSA-N 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 241000251468 Actinopterygii Species 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 244000241257 Cucumis melo Species 0.000 description 1
- 235000015510 Cucumis melo subsp melo Nutrition 0.000 description 1
- FJJCIZWZNKZHII-UHFFFAOYSA-N [4,6-bis(cyanoamino)-1,3,5-triazin-2-yl]cyanamide Chemical compound N#CNC1=NC(NC#N)=NC(NC#N)=N1 FJJCIZWZNKZHII-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000019637 foraging behavior Effects 0.000 description 1
- 238000010413 gardening Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000003534 oscillatory effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000036413 temperature sense Effects 0.000 description 1
- 239000004408 titanium dioxide Substances 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a kind of Intelligent plant growth environment adjustment system and method based on Internet of Things cloud platform, it is related to agriculture field.Using server according to plant photosynthetic rate corresponding to genetic neural network training pattern, floristics, pre- plant growing cycle, multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample, multigroup preferable first growing environment parameter is tentatively selected；Again optimal first growing environment parameter is selected according to desired value optimizing algorithm model；Calculate the regulation and control difference of current first growing environment parameter and optimal first growing environment parameter, adjustment signal is finally sent to first environment parameter adjustment mechanism, first environment parameter adjustment mechanism is run after receiving adjustment signal, so that current first growing environment parameter is updated to optimal first growing environment parameter, so that, the suitable environment that plant growth environment regulation after for suitable for plant grow high with the matching degree of the optimum growh environment of plant demand itself of the plant growth environment after regulation.
Description
Technical field
The present invention relates to agriculture field, in particular to a kind of Intelligent plant growth ring based on Internet of Things cloud platform
Border regulating system and method.
Background technology
The quality of plant photosynthetic rate depends on the quality of growth cycle, plant growth environment residing for current plant, for example,
Ambient lighting, gas concentration lwevel, environment temperature, air humidity, soil water content etc., wherein, ambient lighting, titanium dioxide
Three concentration of carbon, environment temperature parameters have a great influence to the photosynthetic rate of plant.The species of plant growth environment and plant and
Growth cycle more matches, then the growing state of plant is better, with the development of Science ＆ Society, to the greenhouse gardening of vegetable melon and fruit
It is more and more scientific, to promote plant to be grown under good environment.
In the prior art, the regulation to plant growth environment usually from, is adopted using ambient parameter acquisition module acquisition module
Collect environmental data, when environmental data is more than default threshold value, that is, control executing agency's operation, to control environmental data less than pre-
Given threshold.Plant growth environment after being so adjusted with the matching degree of the optimum growh environment of plant demand itself not
Height, poor reliability, the plant growth environment after regulation are not the suitable environment of suitable for plant growth.
The content of the invention
In view of this, the purpose of the embodiment of the present invention is to provide a kind of Intelligent plant growth based on Internet of Things cloud platform
Environment adjustment system and method.
In a first aspect, the embodiments of the invention provide a kind of Intelligent plant growth environment regulation based on Internet of Things cloud platform
System, the Intelligent plant growth environment adjustment system based on Internet of Things cloud platform include first environment parameter collection module,
Server, first environment parameter adjustment mechanism, the server respectively with first environment parameter collection module, the first environment
Parameter adjustment mechanism communicates to connect,
The first environment parameter collection module is used for the current first growing environment parameter of herborization local environment, and
Current first growing environment parameter is transmitted to the server；
The server is used for the current first growing environment parameter for receiving the transmission of first environment parameter collection module；And according to
According to genetic neural network training pattern, presetting floristics, presetting plant growing cycle, pre-stored multigroup first
Plant photosynthetic rate corresponding to growing environment parameter training sample and every group of first growing environment parameter training sample, from described
Multigroup first growing environment parameter training sample choose meet presetting floristics, presetting plant growing cycle it is more
The preferable first growing environment parameter of group；Secondly according to desired value optimizing algorithm model, presetting floristics, presetting plant
Thing growth cycle, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample
Plant photosynthetic rate corresponding to this, chosen from the multigroup preferable first growing environment parameter selected meet it is presetting
The optimal first growing environment parameter of floristics, presetting plant growing cycle；Current first growing environment ginseng is calculated again
Number and the regulation and control difference of optimal first growing environment parameter, and according to the regulation and control difference generation adjustment signal, finally send institute
Adjustment signal is stated to the first environment parameter adjustment mechanism；
The first environment parameter adjustment mechanism is used to receive the adjustment signal that the server is sent, and according to the tune
Control the operation of signal performing environment parameter regulation.
Second aspect, the embodiment of the present invention additionally provide a kind of Intelligent plant growth environment based on Internet of Things cloud platform and adjusted
Section method, it is described to be based on Internet of Things cloud platform applied to the Intelligent plant growth environment adjustment system based on Internet of Things cloud platform
Intelligent plant growth environment adjustment system include first environment parameter collection module, server, first environment parameter regulation machine
Structure, the server communicates to connect with first environment parameter collection module, the first environment parameter adjustment mechanism respectively, described
Intelligent plant growth environment adjustment method based on Internet of Things cloud platform includes：
The current first growing environment parameter of the first environment parameter collection module herborization local environment, and ought
Preceding first growing environment parameter is transmitted to the server；
The server receives the current first growing environment parameter that first environment parameter collection module is sent；
The server is according to genetic neural network training pattern, presetting floristics, presetting plant growth
Cycle, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample are corresponding
Plant photosynthetic rate, chosen from the multigroup first growing environment parameter training sample and meet presetting floristics, pre-
Multigroup preferable first growing environment parameter of the plant growing cycle of setting；
The server is according to desired value optimizing algorithm model, presetting floristics, presetting plant growth week
Corresponding to phase, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample
Plant photosynthetic rate, chosen from the multigroup preferable first growing environment parameter selected and meet presetting plant species
Class, the optimal first growing environment parameter of presetting plant growing cycle；
The server calculates the regulation and control difference of current first growing environment parameter and optimal first growing environment parameter, and
According to the regulation and control difference generation adjustment signal, the adjustment signal is finally sent to the first environment parameter adjustment mechanism；
The first environment parameter adjustment mechanism receives the adjustment signal that the server is sent, and according to the regulation and control letter
The operation of number performing environment parameter regulation.
Compared with prior art, the Intelligent plant growth environment adjustment system provided by the invention based on Internet of Things cloud platform
It is all according to genetic neural network training pattern, presetting floristics, presetting plant growth using server with method
Corresponding to phase, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample
Plant photosynthetic rate, chosen from multigroup first growing environment parameter training sample and meet presetting floristics, preset
Multigroup preferable first growing environment parameter of fixed plant growing cycle；Again according to desired value optimizing algorithm model, presetting
Floristics, presetting plant growing cycle, pre-stored multigroup first growing environment parameter training sample and every group
Plant photosynthetic rate corresponding to one growth ambient parameter training sample, from the multigroup preferable first growing environment ginseng selected
The optimal first growing environment parameter for meeting presetting floristics, presetting plant growing cycle is chosen in number；And count
The regulation and control difference of current first growing environment parameter and optimal first growing environment parameter is calculated, and according to the regulation and control difference generation
Adjustment signal；The adjustment signal to first environment parameter adjustment mechanism, first environment parameter adjustment mechanism is finally sent to receive
To after adjustment signal, run according to adjustment signal, so that current first growing environment parameter is updated to optimal first growing environment
Parameter, so that the matching degree height with the optimum growh environment of plant demand itself of the plant growth environment after regulation, can
High by property, the plant growth environment after regulation is the suitable environment of suitable for plant growth, and the production for considerably improving farm is received
Benefit.
To enable the above objects, features and advantages of the present invention to become apparent, preferred embodiment cited below particularly, and coordinate
Appended accompanying drawing, is described in detail below.
Brief description of the drawings
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
Part of the embodiment of the present invention, rather than whole embodiments.The present invention implementation being generally described and illustrated herein in the accompanying drawings
The component of example can be configured to arrange and design with a variety of.Therefore, the reality of the invention to providing in the accompanying drawings below
The detailed description for applying example is not intended to limit the scope of claimed invention, but is merely representative of the selected implementation of the present invention
Example.Based on the embodiment in the present invention, what those of ordinary skill in the art were obtained under the premise of creative work is not made
Every other embodiment, belongs to the scope of protection of the invention.
Fig. 1 be server provided in an embodiment of the present invention respectively with first environment parameter collection module, second environment parameter
Interaction schematic diagram between acquisition module, first environment parameter adjustment mechanism, second environment parameter adjustment mechanism and alarm；
Fig. 2 is the stream of the Intelligent plant growth environment adjustment method provided in an embodiment of the present invention based on Internet of Things cloud platform
Journey schematic diagram；
Fig. 3 is the schematic flow sheet that genetic neural network training pattern provided in an embodiment of the present invention is established.
Icon：100- first environment parameter collection modules；200- servers；300- second environment parameter collection modules；
400- first environment parameter adjustment mechanisms；500- alarms；600- solar radiation sensors；700- environment temperature sensors；
800-CO2 concentration sensors；900- soil temperature sensors；1001- Soil Moisture Sensors；1002- relative humidity senses
Device；1003- automatic irrigations mechanism；1005- puggaree drive mechanisms；1006- thermoregulation mechanisms；The quantitative light fillings of 1007-LED
Lamp；1008- ventilations executing agency.
Embodiment
Below in conjunction with accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Ground describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.Generally exist
The component of the embodiment of the present invention described and illustrated in accompanying drawing can be configured to arrange and design with a variety of herein.Cause
This, the detailed description of the embodiments of the invention to providing in the accompanying drawings is not intended to limit claimed invention below
Scope, but it is merely representative of the selected embodiment of the present invention.Based on embodiments of the invention, those skilled in the art are not doing
The every other embodiment obtained on the premise of going out creative work, belongs to the scope of protection of the invention.
Referring to Fig. 1, the embodiments of the invention provide a kind of Intelligent plant growth environment tune based on Internet of Things cloud platform
Section system, the Intelligent plant growth environment adjustment system based on Internet of Things cloud platform include first environment parameter collection module 100,
Second environment parameter collection module 300, server 200, first environment parameter adjustment mechanism 400, second environment parameter regulation machine
Structure, server 200 are joined with first environment parameter collection module 100, second environment parameter collection module 300, first environment respectively
Number governor motion 400, the communication connection of second environment parameter adjustment mechanism.
First environment parameter collection module 100 is used for the current first growing environment parameter of herborization local environment, and
Current first growing environment parameter is transmitted to server 200.
In the present embodiment, first environment parameter collection module 100 includes solar radiation sensor 600, environment temperature senses
Device 700 and CO2 concentration sensor 800, solar radiation sensor 600 are used for the pharosage for detecting plant local environment,
Environment temperature sensor 700 is used for the temperature for detecting plant local environment, and CO2 concentration sensor 800 is used for residing for herborization
The CO2 concentration of environment, first environment parameter adjustment mechanism 400 include puggaree drive mechanism 1005, thermoregulation mechanism 1006,
The quantitative light compensating lamps 1007 of LED, ventilation executing agency 1008.
Second environment parameter collection module 300 include soil temperature sensor 900, Soil Moisture Sensor 1001 and
Relative humidity sensor 1002, soil temperature sensor 900 are used for the temperature of herborization soil for growth, soil moisture content sensing
Device 1001 is used for the water content of herborization soil for growth, and relative humidity sensor 1002 is used for the phase of herborization growing environment
To humidity, second environment parameter adjustment mechanism includes automatic irrigation mechanism 1003.
Wherein, it is dense to include environment temperature, feux rouges flux density, blue flux density, CO2 for current first growing environment parameter
Degree.Wherein, environment temperature, feux rouges flux density, blue flux density, CO2 concentration are to the influence degree of the photosynthetic rate of plant
It is larger.It is dense that first environment parameter collection module 100 includes solar radiation sensor 600, environment temperature sensor 700 and CO2
Spend sensor 800.
Server 200 is used for the current first growing environment parameter for receiving the transmission of first environment parameter collection module 100；And
According to genetic neural network training pattern, presetting floristics, presetting plant growing cycle, pre-stored multigroup the
Plant photosynthetic rate corresponding to one growth ambient parameter training sample and every group of first growing environment parameter training sample, from more
Group the first growing environment parameter training sample choose meet presetting floristics, presetting plant growing cycle it is multigroup
Preferable first growing environment parameter；Secondly according to desired value optimizing algorithm model, presetting floristics, presetting plant
Growth cycle, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample
Corresponding plant photosynthetic rate, chosen from the multigroup preferable first growing environment parameter selected and meet presetting plant species
Class, the optimal first growing environment parameter of presetting plant growing cycle；Calculate again current first growing environment parameter with most
The regulation and control difference of good first growing environment parameter, and according to regulation and control difference generation adjustment signal, adjustment signal is finally sent to the
One ambient parameter governor motion 400.
Server 200 is additionally operable to determine neural network topology structure and creates initial multilayer feedforward neural network；Next is carried
The type of coding of the first pre-stored growing environment parameter training sample, code length, population scale, definition is taken to intersect, variation
Rate and termination condition；Then according to type of coding, code length, population scale, definition intersection, aberration rate and termination condition
Determine adaptive response function；Then N number of two-value gene chain code is generated according to the first pre-stored growing environment parameter training sample
Individual, and N number of two-value gene chain code individual is decoded as one group of connection weight；Again according to pre-stored the first growing environment ginseng
Number training sample, adaptive response function calculate the error of multilayer feedforward neural network and adaptation corresponding to every group of connection weight
Degree；Then choose error and fitness meets that the connection weight of predetermined condition is initial as the weights and threshold value of network neural
Value；Weights and the newly-built current multilayer feedforward neural network of threshold value initial value according to the network neural selected；And calculate each layer
Reality output and calculating reality output and multiple errors of target output；Then instructed according to LM coaching methods, multiple errors
Practice and adjust the weights and threshold value initial value of each layer；Multigroup preferably weights and the threshold value for meeting predetermined condition are finally selected,
And preserve genetic neural network training pattern.
Initial weight scope is determined using the convergence capabilities of quick global through the above way, afterwards with this weights
Complete genetic neural network training pattern structure.Possesses the spy that global optimization ability is strong, adaptivity is strong in view of genetic algorithm
Point, it is possible to achieve the quick obtaining of globally optimal solution neighborhood in a wide range of, but it is not high in the low optimization accuracy of local small neighbourhood, therefore
The present embodiment is combined genetic algorithm with BP neural network algorithm, can so as to build above-mentioned genetic neural network training pattern
Certain plant is precisely predicted under different temperatures, pharosage and CO2 concentration under some growth cycle with realizing, and
The temperature, pharosage and CO2 concentration of the suitable plant are tentatively selected, reference number is provided for next step desired value optimizing
According to, and significantly improve convergence rate.
In the present embodiment, desired value optimizing algorithm model can use Genetic Algorithm Model or modified fish-swarm algorithm mould
Type.When desired value optimizing algorithm model uses Genetic Algorithm Model, Genetic Algorithm Model comparative analysis genetic neural network mould
Difference of the type on Searching efficiency and optimizing result, so as to obtain optimal first growing environment parameter.Genetic Algorithm Model is being transported
In capable process, multiple optimizing condition sample sets are established by the way of nested, pass through the example to genetic algorithm back propagation neural network model
Change obtains object function.
When desired value optimizing algorithm model uses modified fish-swarm algorithm model (different from traditional fish-swarm algorithm model)
When, modified fish-swarm algorithm model to establish process as described below：
The optimizing initiation parameter for losing artificial fish-swarm algorithm and the random initializtion shoal of fish are set first, establish multidimensional optimizing tune
Save set of data samples and extract one group of optimizing condition and specific objective function set in advance, secondly, calculate the position of Artificial Fish
The food concentration put, and the Artificial Fish in population space is evaluated, judge whether evaluation result meets presetting termination
Condition, if being unsatisfactory for end condition, the dynamic regulation amount of the visual field and step-length is adjusted, is then met in evaluation result presetting
Knock into the back condition complete knock into the back behavior and when evaluation result meets presetting cluster condition complete cluster behavior, commenting
Valency result had not both met presetting knock into the back and condition or has not met and complete to look for when evaluation result meets presetting cluster condition
Food behavior, (chosen so as to select optimal behavior outcome from the multigroup preferable first growing environment parameter selected
Meet the optimal first growing environment parameter of presetting floristics, presetting plant growing cycle).If above-mentioned comments
Valency result meets end condition, then directly determines optimal first growing environment parameter.
Due to traditional fish-swarm algorithm perform foraging behavior, cluster behavior, knock into the back behavior and random behavior when by
To the influence of visual field step-length, if field range is bigger, the global search of Artificial Fish and convergence capabilities are stronger, if the part of Artificial Fish
Search capability is strong, and step-length is bigger, then convergence rate is faster, it sometimes appear that oscillatory occurences；Conversely, then convergence rate is faster, ask
Solve precision higher, above-mentioned modified fish-swarm algorithm model realization to optimal first growing environment parameter in speed of searching optimization and
Low optimization accuracy mutually takes into account the state of balance, i.e., solves Traditional Man fish-swarm algorithm receipts on the basis of low optimization accuracy is effectively provided
Hold back slow-footed problem.Tested through invention, the optimal first growing environment parameter being calculated by above-mentioned mode and plant
For the error of the growing environment parameter of actual demand within 6%, precision is very high.
First environment parameter adjustment mechanism 400 is used for the adjustment signal that the reception server 200 is sent, and according to adjustment signal
The operation of performing environment parameter regulation.
After first environment parameter adjustment mechanism 400 receives adjustment signal, run according to adjustment signal, so that current first
Growing environment parameter is updated to optimal first growing environment parameter so that regulation after plant growth environment with plant sheet
The matching degree of the optimum growh environment of body demand is high, and reliability is high, and the plant growth environment after regulation is suitable for plant growth
Suitable environment.It is fixed that first environment parameter adjustment mechanism 400 includes puggaree drive mechanism 1005, thermoregulation mechanism 1006, LED
Measure light compensating lamp 1007, ventilation executing agency 1008.When luminous flux is more, puggaree drive mechanism 1005 can drive puggaree
The sunlight projected in greenhouse is stopped, so as to reduce inject in greenhouse luminous flux (including blue flux density and
Feux rouges flux density), when luminous flux is less, the quantitative light compensating lamps 1007 of LED are lighted and pharosage can be made up.Temperature
Degree governor motion 1006 can adjust the temperature in greenhouse, and ventilation executing agency 1008 can be by ventilation parameters greenhouse
CO2 concentration.
Server 200 is additionally operable to receive the current second growing environment parameter that second environment parameter collection module 300 is sent,
Judge current second growing environment parameter whether within presetting threshold range；If current second growing environment parameter does not exist
When within presetting threshold range, control alarm 500 is alarmed.
For example, if the water content of soil is too low, control alarm 500 is alarmed, to remind staff that scene is grasped
Make or start automatic irrigation mechanism 1003 to irrigate plant, until the water content in soil to presetting threshold value.
In addition, the Intelligent plant growth environment adjustment system based on Internet of Things cloud platform also includes wind light mutual complementing power supply system
System, wind-photovoltaic complementary power supply system respectively with first environment parameter collection module 100, first environment parameter adjustment mechanism 400, second
Ambient parameter acquisition module 300, the electrical connection of second environment parameter adjustment mechanism, wind-photovoltaic complementary power supply system is for by wind energy and too
Sun can be converted into electric energy as first environment parameter collection module 100, first environment parameter adjustment mechanism 400, second environment parameter
Acquisition module 300, the power supply of second environment parameter adjustment mechanism, energy-conserving and environment-protective.
Referring to Fig. 2, the embodiment of the present invention additionally provides a kind of Intelligent plant growth environment based on Internet of Things cloud platform
Adjusting method is, it is necessary to explanation, the Intelligent plant growth environment based on Internet of Things cloud platform that the embodiment of the present invention is provided
Adjusting method, its general principle and caused technique effect are identical with above-described embodiment, to briefly describe, portion of the embodiment of the present invention
Divide and do not refer to part, refer to corresponding contents in the above embodiments.The Intelligent plant growth ring based on Internet of Things cloud platform
Border adjusting method is applied to the Intelligent plant growth environment adjustment system based on Internet of Things cloud platform, based on Internet of Things cloud platform
Intelligent plant growth environment adjustment system includes first environment parameter collection module 100, server 200, first environment parameter and adjusted
Mechanism 400 is saved, server 200 communicates with first environment parameter collection module 100, first environment parameter adjustment mechanism 400 respectively
Connection.Intelligent plant growth environment adjustment method based on Internet of Things cloud platform includes：
Step S201：The current first growing environment ginseng of the herborization local environment of first environment parameter collection module 100
Number, and current first growing environment parameter is transmitted to server 200.
Step S202：Server 200 receives the current first growing environment ginseng that first environment parameter collection module 100 is sent
Number.
Step S203：Server 200 is according to genetic neural network training pattern, presetting floristics, presetting
Plant growing cycle, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training
Plant photosynthetic rate corresponding to sample, chosen from multigroup first growing environment parameter training sample and meet presetting plant species
Class, multigroup preferable first growing environment parameter of presetting plant growing cycle.Wherein, as shown in figure 3, server 200 is built
Founding the method for the genetic neural network training pattern includes：
Step S2031：Server 200 determines neural network topology structure and creates initial multilayer feedforward neural network.
Step S2032：The type of coding of the first pre-stored growing environment parameter training sample of the extraction of server 200, compile
Code length, population scale, definition intersection, aberration rate and termination condition.
Step S2033：Server 200 according to type of coding, code length, population scale, definition intersect, aberration rate and
Termination condition determines adaptive response function.
Step S2034：Server 200 generates N number of two-value base according to the first pre-stored growing environment parameter training sample
Because of chain code individual, and N number of two-value gene chain code individual is decoded as one group of connection weight.
Step S2035：Server 200 is according to the first pre-stored growing environment parameter training sample, adaptive response function
Calculate the error and fitness of multilayer feedforward neural network corresponding to every group of connection weight
Step S2036：Server 200 chooses error and fitness meets the connection weight of predetermined condition as network
The weights and threshold value initial value of nerve.
Step S2037：Weights and threshold value initial value newly-built current multilayer of the server 200 according to the network neural selected
Feedforward neural network.
Step S2038：Server 200 calculates each layer reality output and calculates reality output and multiple mistakes of target output
Difference.
Step S2039：Server 200 is trained according to LM coaching methods, multiple errors and adjusts the weights and threshold of each layer
It is worth initial value.
Step S2040：Server 200 selects multigroup preferably weights and the threshold value, and preserve something lost for meeting predetermined condition
Pass neural network training model.
Step S204：Server 200 is according to desired value optimizing algorithm model, presetting floristics, presetting plant
Thing growth cycle, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample
Plant photosynthetic rate corresponding to this, chosen from the multigroup preferable first growing environment parameter selected and meet presetting plant
Species, the optimal first growing environment parameter of presetting plant growing cycle.
Step S205：Server 200 calculates the tune of current first growing environment parameter and optimal first growing environment parameter
Difference is controlled, and according to regulation and control difference generation adjustment signal.
Step S206：Server 200 sends adjustment signal to first environment parameter adjustment mechanism 400.
Step S207：The adjustment signal that the reception server 200 of first environment parameter adjustment mechanism 400 is sent, and according to tune
Control the operation of signal performing environment parameter regulation.
In summary, it is provided by the invention based on the Intelligent plant growth environment adjustment system of Internet of Things cloud platform and side
Method, using server according to genetic neural network training pattern, presetting floristics, presetting plant growing cycle,
Planted corresponding to pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample
Thing photosynthetic rate, chosen from the multigroup first growing environment parameter training sample and meet presetting floristics, presetting
Plant growing cycle multigroup preferable first growing environment parameter；Again according to desired value optimizing algorithm model, presetting plant
Species, presetting plant growing cycle, pre-stored multigroup first growing environment parameter training sample and every group first
Plant photosynthetic rate corresponding to growing environment parameter training sample, from the multigroup preferable first growing environment parameter selected
Middle selection meets the optimal first growing environment parameter of presetting floristics, presetting plant growing cycle；And calculate
The regulation and control difference of current first growing environment parameter and optimal first growing environment parameter, and adjusted according to the regulation and control difference generation
Control signal；The adjustment signal to first environment parameter adjustment mechanism, first environment parameter adjustment mechanism is finally sent to receive
After adjustment signal, run according to adjustment signal, so that current first growing environment parameter is updated to optimal first growing environment ginseng
Number, so that the matching degree height with the optimum growh environment of plant demand itself of the plant growth environment after regulation, reliably
Property it is high, the plant growth environment after regulation is the suitable environment of suitable for plant growth, considerably improves the Production Gain on farm.
In several embodiments provided herein, it should be understood that disclosed apparatus and method, can also pass through
Other modes are realized.Device embodiment described above is only schematical, for example, flow chart and block diagram in accompanying drawing
Show the device of multiple embodiments according to the present invention, method and computer program product architectural framework in the cards,
Function and operation.At this point, each square frame in flow chart or block diagram can represent the one of a module, program segment or code
Part, a part for the module, program segment or code include one or more and are used to realize holding for defined logic function
Row instruction.It should also be noted that at some as in the implementation replaced, the function that is marked in square frame can also with different from
The order marked in accompanying drawing occurs.For example, two continuous square frames can essentially perform substantially in parallel, they are sometimes
It can perform in the opposite order, this is depending on involved function.It is it is also noted that every in block diagram and/or flow chart
The combination of individual square frame and block diagram and/or the square frame in flow chart, function or the special base of action as defined in performing can be used
Realize, or can be realized with the combination of specialized hardware and computer instruction in the system of hardware.
In addition, each functional module in each embodiment of the present invention can integrate to form an independent portion
Point or modules individualism, can also two or more modules be integrated to form an independent part.
If the function is realized in the form of software function module and is used as independent production marketing or in use, can be with
It is stored in a computer read/write memory medium.Based on such understanding, technical scheme is substantially in other words
The part to be contributed to prior art or the part of the technical scheme can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, including some instructions are causing a computer equipment (can be
People's computer, server, or network equipment etc.) perform all or part of step of each embodiment methods described of the present invention.
And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with the medium of store program codes.Need
Illustrate, herein, such as first and second or the like relational terms be used merely to by an entity or operation with
Another entity or operation make a distinction, and not necessarily require or imply between these entities or operation any this reality be present
The relation or order on border.Moreover, term " comprising ", "comprising" or its any other variant are intended to the bag of nonexcludability
Contain, so that process, method, article or equipment including a series of elements not only include those key elements, but also including
The other element being not expressly set out, or also include for this process, method, article or the intrinsic key element of equipment.
In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that including the key element
Process, method, other identical element also be present in article or equipment.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.It should be noted that：Similar label and letter exists
Similar terms is represented in following accompanying drawing, therefore, once being defined in a certain Xiang Yi accompanying drawing, is then not required in subsequent accompanying drawing
It is further defined and explained.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention described should be defined by scope of the claims.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality
Body or operation make a distinction with another entity or operation, and not necessarily require or imply and deposited between these entities or operation
In any this actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant are intended to
Nonexcludability includes, so that process, method, article or equipment including a series of elements not only will including those
Element, but also the other element including being not expressly set out, or it is this process, method, article or equipment also to include
Intrinsic key element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that
Other identical element also be present in process, method, article or equipment including the key element.
Claims (10)
1. a kind of Intelligent plant growth environment adjustment system based on Internet of Things cloud platform, it is characterised in that described to be based on Internet of Things
The Intelligent plant growth environment adjustment system of net cloud platform includes first environment parameter collection module, server, first environment ginseng
Number governor motion, the server communicate with first environment parameter collection module, the first environment parameter adjustment mechanism respectively
Connection,
The first environment parameter collection module is used for the current first growing environment parameter of herborization local environment, and ought
Preceding first growing environment parameter is transmitted to the server；
The server is used for the current first growing environment parameter for receiving the transmission of first environment parameter collection module；And according to something lost
Pass neural network training model, presetting floristics, presetting plant growing cycle, pre-stored multigroup first growth
Plant photosynthetic rate corresponding to ambient parameter training sample and every group of first growing environment parameter training sample, from described multigroup
First growing environment parameter training sample choose meet presetting floristics, presetting plant growing cycle it is multigroup compared with
Good first growing environment parameter；Secondly according to desired value optimizing algorithm model, presetting floristics, the life of presetting plant
Long period, pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample pair
The plant photosynthetic rate answered, chosen from the multigroup preferable first growing environment parameter selected and meet presetting plant
Species, the optimal first growing environment parameter of presetting plant growing cycle；Calculate again current first growing environment parameter with
The regulation and control difference of optimal first growing environment parameter, and according to the regulation and control difference generation adjustment signal, finally send the tune
Signal is controlled to the first environment parameter adjustment mechanism；
The first environment parameter adjustment mechanism is used to receive the adjustment signal that the server is sent, and according to the regulation and control letter
The operation of number performing environment parameter regulation.
2. the Intelligent plant growth environment adjustment system according to claim 1 based on Internet of Things cloud platform, its feature exist
In the server is additionally operable to determine neural network topology structure and creates initial multilayer feedforward neural network；Secondly extract pre-
The type of coding of the first growing environment parameter training sample of storage, code length, population scale, definition intersect, aberration rate with
And stop condition；Then determined according to type of coding, code length, population scale, definition intersection, aberration rate and termination condition
Adaptive response function；Then N number of two-value gene chain code individual is generated according to the first pre-stored growing environment parameter training sample,
And N number of two-value gene chain code individual is decoded as one group of connection weight；Again according to pre-stored the first growing environment parameter instruction
Practice sample, adaptive response function calculates the error and fitness of multilayer feedforward neural network corresponding to every group of connection weight；So
Error is chosen afterwards and fitness meets weights and threshold value initial value of the connection weight as network neural of predetermined condition.
3. the Intelligent plant growth environment adjustment system according to claim 2 based on Internet of Things cloud platform, its feature exist
In,
The server is additionally operable to according to the newly-built current multilayer feedforward god of the weights and threshold value initial value of the network neural selected
Through network；And calculate each layer reality output and calculate reality output and multiple errors of target output；Then trained according to LM
Method, the multiple error are trained and adjust the weights and threshold value initial value of each layer；Finally select and meet predetermined condition
Multigroup preferably weights and threshold value, and preserve genetic neural network training pattern.
4. the Intelligent plant growth environment adjustment system according to claim 1 based on Internet of Things cloud platform, its feature exist
In the desired value optimizing algorithm model is Genetic Algorithm Model or modified fish-swarm algorithm model.
5. the Intelligent plant growth environment adjustment system according to claim 1 based on Internet of Things cloud platform, its feature exist
In first environment parameter collection module includes solar radiation sensor, environment temperature sensor and CO2 concentration sensor, institute
The pharosage that solar radiation sensor is used to detect plant local environment is stated, the environment temperature sensor, which is used to detect, plants
The temperature of thing local environment, the CO2 concentration sensor are used for the CO2 concentration of herborization local environment, the first environment
Parameter adjustment mechanism includes the quantitative light compensating lamp of puggaree drive mechanism, thermoregulation mechanism, LED, ventilation executing agency.
6. the Intelligent plant growth environment adjustment system according to claim 1 based on Internet of Things cloud platform, its feature exist
In, the Intelligent plant growth environment adjustment system based on Internet of Things cloud platform also includes second environment parameter collection module,
The second environment parameter collection module includes soil temperature sensor, Soil Moisture Sensor and relative humidity sensing
Device, the soil temperature sensor are used for the temperature of herborization soil for growth, and the Soil Moisture Sensor is used to gather
The water content of plant growth soil, the relative humidity sensor are used for the relative humidity of herborization growing environment, and described the
Two ambient parameter governor motions include automatic irrigation mechanism.
7. the Intelligent plant growth environment adjustment system according to claim 6 based on Internet of Things cloud platform, its feature exist
In the server is additionally operable to receive the current second growing environment parameter that second environment parameter collection module is sent, and judges institute
Current second growing environment parameter is stated whether within presetting threshold range；If current second growing environment parameter is not pre-
When within the threshold range of setting, alarm equipment alarm is controlled.
8. the Intelligent plant growth environment adjustment system according to claim 1 based on Internet of Things cloud platform, its feature exist
In the Intelligent plant growth environment adjustment system based on Internet of Things cloud platform also includes wind-photovoltaic complementary power supply system, described
Wind-photovoltaic complementary power supply system is electrically connected with the first environment parameter collection module, the first environment parameter adjustment mechanism respectively
Connect.
9. a kind of Intelligent plant growth environment adjustment method based on Internet of Things cloud platform, it is characterised in that applied to based on thing
The Intelligent plant growth environment adjustment system for cloud platform of networking, the Intelligent plant growth environment based on Internet of Things cloud platform are adjusted
Section system includes first environment parameter collection module, server, first environment parameter adjustment mechanism, and the server is respectively with the
One ambient parameter acquisition module, first environment parameter adjustment mechanism communication connection, the intelligence based on Internet of Things cloud platform
Energy plant growth environment adjusting method includes：
The current first growing environment parameter of the first environment parameter collection module herborization local environment, and by current
One growth ambient parameter is transmitted to the server；
The server receives the current first growing environment parameter that first environment parameter collection module is sent；
The server according to genetic neural network training pattern, presetting floristics, presetting plant growing cycle,
Planted corresponding to pre-stored multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample
Thing photosynthetic rate, chosen from the multigroup first growing environment parameter training sample and meet presetting floristics, presetting
Plant growing cycle multigroup preferable first growing environment parameter；
The server is according to desired value optimizing algorithm model, presetting floristics, presetting plant growing cycle, pre-
Plant corresponding to the multigroup first growing environment parameter training sample and every group of first growing environment parameter training sample of storage
Photosynthetic rate, chosen from the multigroup preferable first growing environment parameter selected and meet presetting floristics, pre-
The optimal first growing environment parameter of the plant growing cycle of setting；
The server calculates the regulation and control difference of current first growing environment parameter and optimal first growing environment parameter, and foundation
The regulation and control difference generation adjustment signal, finally sends the adjustment signal to the first environment parameter adjustment mechanism；
The first environment parameter adjustment mechanism receives the adjustment signal that the server is sent, and is held according to the adjustment signal
The operation of row ambient parameter regulation.
10. the Intelligent plant growth environment adjustment method according to claim 9 based on Internet of Things cloud platform, its feature exist
In the method that the server establishes the genetic neural network training pattern includes：
The server determines neural network topology structure and creates initial multilayer feedforward neural network；
The type of coding of the first pre-stored growing environment parameter training sample of the server extraction, code length, population rule
Mould, definition intersection, aberration rate and termination condition；
The server is according to type of coding, code length, population scale, definition intersection, aberration rate and stops condition determination
Adaptive response function；
The server generates N number of two-value gene chain code individual according to the first pre-stored growing environment parameter training sample, and
N number of two-value gene chain code individual is decoded as one group of connection weight；
The server calculates every group of connection weight according to the first pre-stored growing environment parameter training sample, adaptive response function
The error and fitness of multilayer feedforward neural network corresponding to value；Then choose error and fitness meets predetermined condition
Weights and threshold value initial value of the connection weight as network neural；
Weights and threshold value initial value newly-built current multilayer feedforward neural network of the server according to the network neural selected；
The server calculates each layer reality output and calculates reality output and multiple errors of target output；
The server is trained according to LM coaching methods, the multiple error and adjusts the weights and threshold value initial value of each layer；
The server selects multigroup preferably weights and the threshold value for meeting predetermined condition, and preserves genetic neural network training
Model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710758513.2A CN107390754B (en) | 2017-08-29 | 2017-08-29 | Intelligent plant growth environment adjustment system and method based on Internet of Things cloud platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710758513.2A CN107390754B (en) | 2017-08-29 | 2017-08-29 | Intelligent plant growth environment adjustment system and method based on Internet of Things cloud platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107390754A true CN107390754A (en) | 2017-11-24 |
| CN107390754B CN107390754B (en) | 2019-07-30 |
Family
ID=60347088
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710758513.2A Expired - Fee Related CN107390754B (en) | 2017-08-29 | 2017-08-29 | Intelligent plant growth environment adjustment system and method based on Internet of Things cloud platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107390754B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109582050A (en) * | 2018-11-27 | 2019-04-05 | 中国农业大学 | A kind of the carbon dioxide intelligent control system and method for fish dish edible mushroom cogeneration system |
| CN110727299A (en) * | 2019-10-11 | 2020-01-24 | 新疆大学 | Greenhouse control method and device |
| CN111026206A (en) * | 2019-12-24 | 2020-04-17 | 张治华 | Automatic control system for hydroponic plants |
| CN112099557A (en) * | 2020-09-24 | 2020-12-18 | 苏州七采蜂数据应用有限公司 | Internet-based household plant planting method and system |
| CN112400515A (en) * | 2020-11-20 | 2021-02-26 | 腾讯科技（深圳）有限公司 | Plant growth environment control method, device, equipment and storage medium based on artificial intelligence |
| CN112804785A (en) * | 2021-03-29 | 2021-05-14 | 宏泰智能科技(东莞)有限公司 | Method and system for intelligently controlling lamplight |
| CN113570240A (en) * | 2021-07-27 | 2021-10-29 | 蒋俊伟 | Wisdom farm platform based on full life cycle management of crops |
| CN114564056A (en) * | 2022-02-21 | 2022-05-31 | 蚌埠市鹏慈农业科技有限公司 | Intelligent control system for planting greenhouse |
| CN116300620A (en) * | 2023-03-21 | 2023-06-23 | 上海华维可控农业科技集团股份有限公司 | Crop growth demand coordination control system and method based on big data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020059154A1 (en) * | 2000-04-24 | 2002-05-16 | Rodvold David M. | Method for simultaneously optimizing artificial neural network inputs and architectures using genetic algorithms |
| CN1568158A (en) * | 2001-10-11 | 2005-01-19 | 德尔格医疗***有限公司 | System for processing signal data representing physiological parameters |
| CN104374053A (en) * | 2014-11-25 | 2015-02-25 | 珠海格力电器股份有限公司 | Intelligent control method, device and system |
| CN104866970A (en) * | 2015-05-26 | 2015-08-26 | 徐吉祥 | Intelligent plantation management method and intelligent plantation equipment |
| CN205922136U (en) * | 2016-03-08 | 2017-02-08 | 华中农业大学 | Automatic accessory drive of artifical high -efficient healing system of grafting |
| CN106648654A (en) * | 2016-12-20 | 2017-05-10 | 深圳先进技术研究院 | Data sensing-based Spark configuration parameter automatic optimization method |
-
2017
- 2017-08-29 CN CN201710758513.2A patent/CN107390754B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020059154A1 (en) * | 2000-04-24 | 2002-05-16 | Rodvold David M. | Method for simultaneously optimizing artificial neural network inputs and architectures using genetic algorithms |
| CN1568158A (en) * | 2001-10-11 | 2005-01-19 | 德尔格医疗***有限公司 | System for processing signal data representing physiological parameters |
| CN104374053A (en) * | 2014-11-25 | 2015-02-25 | 珠海格力电器股份有限公司 | Intelligent control method, device and system |
| CN104866970A (en) * | 2015-05-26 | 2015-08-26 | 徐吉祥 | Intelligent plantation management method and intelligent plantation equipment |
| CN205922136U (en) * | 2016-03-08 | 2017-02-08 | 华中农业大学 | Automatic accessory drive of artifical high -efficient healing system of grafting |
| CN106648654A (en) * | 2016-12-20 | 2017-05-10 | 深圳先进技术研究院 | Data sensing-based Spark configuration parameter automatic optimization method |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109582050A (en) * | 2018-11-27 | 2019-04-05 | 中国农业大学 | A kind of the carbon dioxide intelligent control system and method for fish dish edible mushroom cogeneration system |
| CN110727299A (en) * | 2019-10-11 | 2020-01-24 | 新疆大学 | Greenhouse control method and device |
| CN111026206A (en) * | 2019-12-24 | 2020-04-17 | 张治华 | Automatic control system for hydroponic plants |
| CN112099557A (en) * | 2020-09-24 | 2020-12-18 | 苏州七采蜂数据应用有限公司 | Internet-based household plant planting method and system |
| CN112400515B (en) * | 2020-11-20 | 2023-11-03 | 腾讯科技（深圳）有限公司 | Plant growth environment control method, device, equipment and storage medium based on artificial intelligence |
| CN112400515A (en) * | 2020-11-20 | 2021-02-26 | 腾讯科技（深圳）有限公司 | Plant growth environment control method, device, equipment and storage medium based on artificial intelligence |
| CN112804785A (en) * | 2021-03-29 | 2021-05-14 | 宏泰智能科技(东莞)有限公司 | Method and system for intelligently controlling lamplight |
| CN113570240A (en) * | 2021-07-27 | 2021-10-29 | 蒋俊伟 | Wisdom farm platform based on full life cycle management of crops |
| CN113570240B (en) * | 2021-07-27 | 2024-02-27 | 蒋俊伟 | Intelligent farm platform based on whole life cycle management of crops |
| CN114564056B (en) * | 2022-02-21 | 2023-05-26 | 蚌埠市鹏慈农业科技有限公司 | Intelligent control system for planting greenhouse |
| CN114564056A (en) * | 2022-02-21 | 2022-05-31 | 蚌埠市鹏慈农业科技有限公司 | Intelligent control system for planting greenhouse |
| CN116300620A (en) * | 2023-03-21 | 2023-06-23 | 上海华维可控农业科技集团股份有限公司 | Crop growth demand coordination control system and method based on big data |
| CN116300620B (en) * | 2023-03-21 | 2023-11-07 | 上海华维可控农业科技集团股份有限公司 | Crop growth demand coordination control system and method based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107390754B (en) | 2019-07-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107390754A (en) | Intelligent plant growth environment adjustment system and method based on Internet of Things cloud platform | |
| CN107390753B (en) | Intelligent plant growth environment regulating device and method based on Internet of Things cloud platform | |
| CN108446997B (en) | A kind of Crop Water-saving Technology irrigation decision method and TT&C system based on Multi-source Information Fusion | |
| CN111263920A (en) | System and method for controlling the growing environment of a crop | |
| Umamaheswari et al. | Integrating scheduled hydroponic system | |
| CN107169621A (en) | A kind of Dissolved Oxygen in Water Forecasting Methodology and device | |
| CN107329511B (en) | Based on the vegetable aquaculture luminous environment high efficiency regulatory method and system between suitable root warm area | |
| CN110084417A (en) | A kind of strawberry greenhouse environment parameter intelligent monitor system based on GRNN neural network | |
| CN110414115B (en) | Wavelet neural network tomato yield prediction method based on genetic algorithm | |
| CN110119169B (en) | Tomato greenhouse temperature intelligent early warning system based on minimum vector machine | |
| CN102736596A (en) | Multi-scale greenhouse environment control system based on crop information fusion | |
| CN106842923A (en) | Greenhouse multiple-factor control method for coordinating based on plant physiology and energy optimization | |
| CN111915062B (en) | Greenhouse crop water demand regulation and control method with water utilization rate and photosynthesis rate being coordinated | |
| CN108256181A (en) | The structure of the Optimum Regulation model of the facility carbon dioxide desired value of fusion efficiencies constraint and application | |
| CN108614601A (en) | A kind of facility luminous environment regulation and control method of fusion random forests algorithm | |
| CN110069032A (en) | A kind of eggplant greenhouse intelligent checking system based on wavelet neural network | |
| CN110100620A (en) | Seedbed dispatching method, system and the medium that illumination is precisely predicted | |
| CN108181814A (en) | Plant growth environment monitoring method and device, computer storage media | |
| CN115453868A (en) | Full-growth-period light intensity regulation and control method based on tomato photoresponse difference characteristics | |
| Valenzuela et al. | Pre-harvest factors optimization using genetic algorithm for lettuce | |
| Li et al. | Determining optimal CO2 concentration of greenhouse tomato based on PSO-SVM | |
| CN114859734A (en) | Greenhouse environment parameter optimization decision method based on improved SAC algorithm | |
| CN105654242A (en) | Fish swarm algorithm-based cucumber seedling stage carbon dioxide optimization regulation and control model, establishment method and application thereof | |
| CN114092776A (en) | Multi-sensor data fusion method applied to intelligent agriculture | |
| CN207039650U (en) | Long-range multiterminal implant system based on IOT technologies |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20191227 Address after: 221000 No.1, Zhangjiagang East Road, Yitang Town, Pizhou City, Xuzhou City, Jiangsu Province Patentee after: Pizhou Binhe SME Management Service Co.,Ltd. Address before: 556000, No. 16, No. 2, building 01, F building, grand landscape, No. 32 Hualian Road, Kaili, Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou Patentee before: GUIZHOU LANLINYANG ENVIRONMENTAL PROTECTION ENERGY TECHNOLOGY CO.,LTD. |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200629 Address after: 430070 building a3-5-2f-201-1, rongkezhigu, 555 Wenhua Avenue, Hongshan District, Wuhan City, Hubei Province Patentee after: Wuhan Jingtu sky Farm Ecological Technology Co.,Ltd. Address before: 221000 No.1, Zhangjiagang East Road, Yitang Town, Pizhou City, Xuzhou City, Jiangsu Province Patentee before: Pizhou Binhe SME Management Service Co.,Ltd. |
|
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190730 |