CN113785718A - Big data based self-learning planting lamp control system and control method - Google Patents
Big data based self-learning planting lamp control system and control method Download PDFInfo
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- A01C—PLANTING; SOWING; FERTILISING
- A01C23/00—Distributing devices specially adapted for liquid manure or other fertilising liquid, including ammonia, e.g. transport tanks or sprinkling wagons
- A01C23/02—Special arrangements for delivering the liquid directly into the soil
- A01C23/023—Special arrangements for delivering the liquid directly into the soil for liquid or gas fertilisers
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- A01G7/045—Electric or magnetic or acoustic treatment of plants for promoting growth with electric lighting
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Abstract
The invention discloses a self-learning plantation lamp control system and a control method based on big data, and relates to the technical field of plantation lamp control systems. The invention comprises a control unit and at least two groups of planting mechanisms in signal connection with the control unit; any one group of planting mechanisms comprises a planting groove, a plant growth lamp arranged right above the planting groove and a distance sensor used for detecting the distance between the plant growth lamp and the planting groove; the soil nutrient detector is connected with the control unit and is provided with a soil nutrient detector and a soil humidity sensor which are used for detecting soil in the planting groove. According to the invention, by introducing a plurality of detectors and combining a control system, the illumination, nutrients, moisture, temperature and the like can be regulated and controlled more conveniently and flexibly according to the growth process of plants, the method is convenient and efficient, the cost is saved, and the method is more suitable for indoor plant planting; and meanwhile, comparing according to self-learning to obtain the most preferable growth conditions suitable for different growth stages.
Description
Technical Field
The invention belongs to the technical field of planting lamp control systems, and particularly relates to a self-learning planting lamp control system and a control method based on big data.
Background
In the field of plant growth, the traditional open-air cultivation has gradually been transformed into indoor greenhouse cultivation. The lighting requirements of different plants at different growth stages are different.
The Chinese publication No. CN106304464A provides a dendrobium planting light control system, which comprises a sensor, a controller and an LED lamp body, wherein the controller is respectively connected with the sensor and the LED lamp body, the LED lamp body comprises a lamp bead and a lamp shell, the lamp bead comprises a blue light lamp bead and a red light lamp bead, the using number ratio of the blue light lamp bead to the red light lamp bead is 4: 1-6: 1, and the lamp shell is arranged on the outer side of the lamp bead in a semicircular arc shape; according to the dendrobium nobile cultivation light control system, the proportion of the red light beads and the blue light beads is controlled, so that light rays meet the optimal growth spectrum requirement of dendrobium nobile, different proportions of red light and blue light are controlled according to different growth stages of the dendrobium nobile, and the healthy and strong growth of the dendrobium nobile is promoted; the light control is achieved by controlling only the ratio of red to blue light.
In the general knowledge of the general public, the magenta color formed by mixing red light and blue light is a light formula, and the knowledge is not accurate strictly; the true optical recipe contains the following components: light quality and intensity, illumination time, mounting location, illumination uniformity, and other environmental factors; therefore, how to obtain the conditions suitable for the growth of plants is the technical problem to be solved by the invention.
Disclosure of Invention
The invention aims to provide a self-learning planting lamp control system and a control method based on big data, and the problem that the existing plant growth conditions cannot be changed according to the plant growth conditions is solved through the arrangement of the self-learning planting lamp control system.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a big data-based self-learning planting lamp control system, which comprises a control unit and at least two groups of planting mechanisms in signal connection with the control unit; any one group of planting mechanisms comprises a planting groove, a plant growth lamp arranged right above the planting groove, and a distance sensor used for detecting the distance between the plant growth lamp and the planting groove; the soil nutrient detector and the soil humidity sensor are connected with the control unit and used for detecting soil in the planting groove; the carbon dioxide sensor and the temperature and humidity sensor are connected with the control unit and used for detecting the environment of the planting mechanism; the device also comprises a carbon dioxide generating device, a humidifier and an air conditioner, wherein the carbon dioxide generating device is connected with the control unit and used for adjusting carbon dioxide and temperature and humidity in the environment where the planting mechanism is located.
Further, be provided with a U type mount A of invering directly over planting the groove, U type mount A's inner bottom side is provided with an electric telescopic device, electric telescopic device's end connection has a horizontal pole, be provided with the vegetation lamp on the horizontal pole, be located still be provided with a plurality of direction telescopic links between the horizontal pole of electric telescopic device both sides and U type mount A inner bottom side.
Further, the distance sensor is installed at one side of the cross bar.
Furthermore, a vertical plate is arranged on one side of the planting groove, and an irrigation pipe and a nutrient solution supplementing pipe are arranged on the vertical plate; the irrigation pipe is communicated with a water source through a water pipe A, the nutrient solution supplementing pipe is communicated with a nutrient tank full of nutrient solution through a water pipe B, an electromagnetic valve B connected with the control unit is arranged on the water pipe B, and an electromagnetic valve A connected with the control unit is arranged on the water pipe A.
Furthermore, electromagnetic flow sensors connected with the control unit are respectively arranged in the water pipe A and the water pipe B.
Furthermore, an inverted U-shaped fixing frame B is arranged right above the planting groove, an image acquisition module for acquiring images of the growth state of plants in the planting groove is arranged on the U-shaped fixing frame B, and the image acquisition module is connected with an image analysis module; the image acquisition module is a camera.
A control method of a self-learning planting lamp control system based on big data is characterized in that the number of planting mechanisms is ten, and the control method sequentially comprises the following steps:
stp1, controlling soil nutrients and soil humidity in each group of planting grooves to be the same, and controlling the stretching of the electric stretching device to realize the same distance between the plant growth lamp and the planting grooves;
stp2, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp3, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
the illuminance of Stp4 and plant growth lamps on ten groups of planting mechanisms is respectively 1K, 1.2K, 1.5K, 1.7K, 2K, 2.5K, 4K, 6K, 8K and 10K;
stp5, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp6, judging one group with the optimal growth in the ten groups of planting mechanisms, acquiring the corresponding illumination condition of the group and storing the illumination condition;
stp7, when the above-mentioned determined optimal illumination condition is 2K, the illumination of the plant growth lamps on the ten groups of planting mechanisms is controlled to be 1.8K, 1.85K, 1.9K, 2K, 2.05K, 2.1K, 2.15K, 2.2K, 2.3K and 2.4K respectively;
stp8, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant; cutting off one group with the optimal growth in the ten groups of planting mechanisms, acquiring the corresponding illumination condition of the group and storing the illumination condition;
stp9, and Stp7 and Stp8 are repeated for several times to obtain the final illumination condition.
A control method of a self-learning planting lamp control system based on big data is characterized in that the number of planting mechanisms is ten, and the control method sequentially comprises the following steps:
stp01, controlling soil nutrients and soil humidity in each group of planting grooves to be the same, and controlling illumination of plant growth lamps on each group of planting mechanisms to be 2K;
stp02, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp03, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp04, and the distances between the plant growth lamps on the ten groups of planting mechanisms and the planting grooves are 1L, 1.1L,1.25L,1.5L,1.7L,2L,2.3L,2.7L,3.2L and 3.5L in sequence;
stp05, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp06, judging the optimal growing one of the ten planting mechanisms, obtaining the corresponding illumination distance parameter condition of the group and storing.
A control method of a self-learning planting lamp control system based on big data,
stp11, controlling the illumination of the plant growth lamps on each group of planting mechanisms to be 2K, and controlling the distance between the plant growth lamps on the planting mechanisms and the planting grooves to be 1.5L;
stp12, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp13, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp14, controlling the soil humidity in the ten groups of planting mechanisms to be P1, P1, P2, P2, P3, P3, P4, P4, P5 and P5 in sequence; and simultaneously controlling the concentrations of soil nutrients in the ten groups of planting mechanisms to be M1, M2, M1, M2, M1, M2, M1, M2, M1 and M2 in sequence.
Stp15, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp16, judging one of the ten planting mechanisms with the optimal growth, acquiring corresponding soil humidity and soil nutrient concentration parameters of the group, and storing the parameters.
A control method of a self-learning planting lamp control system based on big data;
stp21, controlling soil nutrients and soil humidity in each group of planting grooves to be the same, controlling illumination of plant growth lamps on each group of planting mechanisms to be 2K, and controlling the distance between the plant growth lamps on the planting mechanisms and the planting grooves to be 1.5L;
stp22, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp23, controlling comprehensive conditions of carbon dioxide concentration, temperature and humidity of the environment where ten groups of planting tanks are located through a carbon dioxide generating device, a humidifier and an air conditioner to be respectively in T1, T2, T3, T4, T5, T6, T7, T8, T9 and T10;
stp24, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp25, judging the optimal growth one of the ten planting mechanisms, acquiring and storing the corresponding comprehensive condition parameters of carbon dioxide concentration, temperature and humidity.
The invention has the following beneficial effects:
according to the invention, by introducing a plurality of detectors and combining a control system, the illumination, nutrients, moisture, temperature and the like can be regulated and controlled more conveniently and flexibly according to the growth process of plants, the method is convenient and efficient, the cost is saved, and the method is more suitable for indoor plant planting; and meanwhile, comparing according to self-learning to obtain the most preferable growth conditions suitable for different growth stages.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of a structural planting mechanism of the present invention;
FIG. 2 is a block diagram of a control system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.
Example one
Referring to fig. 1-2, the present invention is a big data-based self-learning planting light control system, which comprises a control unit and at least two groups of planting mechanisms in signal connection with the control unit; any one group of planting mechanisms comprises a planting groove 1, a plant growth lamp 14 arranged right above the planting groove 1, and a distance sensor for detecting the distance between the plant growth lamp 14 and the planting groove 1; the soil nutrient detector and the soil humidity sensor are connected with the control unit and used for detecting soil in the planting groove 1; the carbon dioxide sensor and the temperature and humidity sensor are connected with the control unit and used for detecting the environment of the planting mechanism; the device also comprises a carbon dioxide generating device, a humidifier and an air conditioner, wherein the carbon dioxide generating device is connected with the control unit and used for adjusting carbon dioxide and temperature and humidity in the environment where the planting mechanism is located.
An inverted U-shaped fixing frame A11 is arranged right above the planting groove 1, an electric telescopic device 12 is arranged on the inner bottom side face of the U-shaped fixing frame A11, the end portion of the electric telescopic device 12 is connected with a cross rod 13, a plant growing lamp 14 is arranged on the cross rod 13, a plurality of guiding telescopic rods 15 are further arranged between the cross rod 13 located on two sides of the electric telescopic device 12 and the inner bottom side face of the U-shaped fixing frame A11, and a distance sensor is arranged on one side of the cross rod 13.
One side of the planting groove 1 is provided with a vertical plate 17, and the vertical plate 17 is provided with an irrigation pipe 18 and a nutrient solution supplementing pipe 19; the irrigation pipe 18 is communicated with a water source through a water pipe A, the nutrient solution supplementing pipe 19 is communicated with a nutrient tank filled with nutrient solution through a water pipe B, the water pipe B is provided with an electromagnetic valve B connected with the control unit, and the water pipe A is provided with an electromagnetic valve A connected with the control unit; electromagnetic flow sensors connected with the control unit are respectively arranged in the water pipe A and the water pipe B.
An inverted U-shaped fixing frame B2 is arranged right above the planting groove 1, an image acquisition module for acquiring images of the growth state of plants in the planting groove 1 is arranged on the U-shaped fixing frame B2, and the image acquisition module is connected with an image analysis module; the image acquisition module is a camera.
Example 2
A control method of a self-learning planting lamp control system based on big data is characterized in that the number of planting mechanisms is ten, and the control method sequentially comprises the following steps:
stp1, controlling the soil nutrient and soil humidity in each group of planting grooves 1 to be the same, and controlling the stretching of the electric stretching device 12 to control the distance between the plant growth lamp 14 and the planting grooves 1 to be the same;
stp2, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp3, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
the illuminance of the Stp4 and the plant growth lamps 14 on the ten groups of planting mechanisms are respectively 1K, 1.2K, 1.5K, 1.7K, 2K, 2.5K, 4K, 6K, 8K and 10K;
stp5, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp6, judging the optimal group of the ten groups of planting mechanisms, acquiring the corresponding illumination condition of the group and storing the illumination condition;
stp7, when the above-mentioned determined optimal lighting condition is 2K, the illuminance of the plant growth lamps 14 on the ten groups of the planting mechanisms are controlled to be 1.8K, 1.85K, 1.9K, 2K, 2.05K, 2.1K, 2.15K, 2.2K, 2.3K and 2.4K, respectively;
stp8, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant; cutting off one group with the optimal growth in the ten groups of planting mechanisms, acquiring the corresponding illumination condition of the group and storing the illumination condition;
stp9, and Stp7 and Stp8 are repeated for several times to obtain the final illumination condition.
Example 3
A control method of a self-learning planting lamp control system based on big data is characterized in that the number of planting mechanisms is ten, and the control method sequentially comprises the following steps:
stp01, controlling the soil nutrient and soil humidity in each group of planting grooves 1 to be the same, and controlling the illumination of the plant growth lamp 14 on each group of planting mechanism to be 2K;
stp02, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp03, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
the distances between the Stp04 and the plant growth lamps 14 on the ten groups of planting mechanisms and the planting groove 1 are 1L, 1.1L,1.25L,1.5L,1.7L,2L,2.3L,2.7L,3.2L and 3.5L in sequence;
stp05, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp06, judging the optimal growth group of the ten planting mechanisms, obtaining the corresponding illumination distance parameter conditions of the group, and storing.
Example 3
A control method of a self-learning planting lamp control system based on big data,
stp11, controlling the illumination of the plant growth lamps 14 on each group of planting mechanisms to be 2K, and controlling the distance between the plant growth lamps 14 on the planting mechanisms and the planting groove 1 to be 1.5L;
stp12, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp13, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp14, controlling soil humidity in ten groups of planting mechanisms to be P1, P1, P2, P2, P3, P3, P4, P4, P5 and P5 in sequence; and simultaneously controlling the concentrations of soil nutrients in the ten groups of planting mechanisms to be M1, M2, M1, M2, M1, M2, M1, M2, M1 and M2 in sequence.
Stp15, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp16, judging the group with the optimal growth in the ten groups of planting mechanisms, acquiring and storing the corresponding soil humidity and soil nutrient concentration parameters of the group.
Example 4
A control method of a self-learning planting lamp control system based on big data;
stp21, controlling the soil nutrient and soil humidity in each group of planting grooves 1 to be the same, controlling the illumination of the plant growth lamps 14 on each group of planting mechanisms to be 2K, and controlling the distance between the plant growth lamps 14 on the planting mechanisms and the planting grooves 1 to be 1.5L;
stp22, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp23, controlling comprehensive conditions of carbon dioxide concentration, temperature and humidity of the environment where the ten groups of planting tanks are located through a carbon dioxide generating device, a humidifier and an air conditioner to be respectively in T1, T2, T3, T4, T5, T6, T7, T8, T9 and T10;
stp24, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp25, judging the optimal growth group in the ten planting mechanisms, acquiring and storing the corresponding comprehensive condition parameters of carbon dioxide concentration, temperature and humidity.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. The utility model provides a control system is planted to self-learning based on big data which characterized in that: comprises a control unit and at least two groups of planting mechanisms in signal connection with the control unit;
any one group of planting mechanisms comprises a planting groove (1), a plant growth lamp (14) arranged right above the planting groove (1), and a distance sensor used for detecting the distance between the plant growth lamp (14) and the planting groove (1);
the soil nutrient detector and the soil humidity sensor are connected with the control unit and used for detecting soil in the planting groove (1);
the carbon dioxide sensor and the temperature and humidity sensor are connected with the control unit and used for detecting the environment of the planting mechanism;
the device also comprises a carbon dioxide generating device, a humidifier and an air conditioner, wherein the carbon dioxide generating device is connected with the control unit and used for adjusting carbon dioxide and temperature and humidity in the environment where the planting mechanism is located.
2. The big data-based self-learning planting lamp control system according to claim 1, wherein an inverted U-shaped fixing frame A (11) is arranged right above the planting groove (1), an electric telescopic device (12) is arranged on the inner bottom side surface of the U-shaped fixing frame A (11), a cross rod (13) is connected to the end of the electric telescopic device (12), a plant growing lamp (14) is arranged on the cross rod (13), and a plurality of guiding telescopic rods (15) are further arranged between the cross rod (13) on two sides of the electric telescopic device (12) and the inner bottom side surface of the U-shaped fixing frame A (11).
3. A big data based self-learning plant lamp control system according to claim 2, characterized in that the distance sensor is mounted on one side of the cross bar (13).
4. A self-learning planting lamp control system based on big data according to any one of claims 1-3, characterized in that a vertical plate (17) is arranged on one side of the planting tank (1), and an irrigation pipe (18) and a nutrient solution supplement pipe (19) are arranged on the vertical plate (17);
irrigation pipe (18) communicate the water source through a water pipe A, nutrient solution supply pipe (19) communicate the nutrition jar that is full of the nutrient solution through a water pipe B, be provided with the solenoid valve B who is connected with the control unit on the water pipe B, be provided with the solenoid valve A who is connected with the control unit on the water pipe A.
5. The self-learning planting lamp control system based on big data as claimed in claim 4, wherein the water pipe A and the water pipe B are respectively provided with an electromagnetic flow sensor connected with the control unit.
6. The big data based self-learning planting lamp control system according to claim 1, wherein an inverted U-shaped fixing frame B (2) is arranged right above the planting tank (1), an image acquisition module for acquiring an image of the growth state of the plant in the planting tank (1) is arranged on the U-shaped fixing frame B (2), and the image acquisition module is connected with an image analysis module;
the image acquisition module is a camera.
7. The control method of the big data based self-learning planting lamp control system according to any one of the above claims 1-6, wherein the number of the planting mechanisms is ten, and the control method comprises the following steps in sequence:
stp1, controlling soil nutrients and soil humidity in each group of planting grooves (1) to be the same, and controlling the stretching of the electric stretching device (12) to control the distance between the plant growth lamp (14) and the planting grooves (1) to be the same;
stp2, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp3, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp4, and illuminance of plant growth lamps (14) on ten groups of planting mechanisms are respectively 1K, 1.2K, 1.5K, 1.7K, 2K, 2.5K, 4K, 6K, 8K and 10K;
stp5, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp6, judging one group with the optimal growth in the ten groups of planting mechanisms, acquiring the corresponding illumination condition of the group and storing the illumination condition;
stp7, when the above-mentioned determined optimal illumination condition is 2K, the illumination of the plant growth lamps (14) on the ten groups of planting mechanisms are controlled to be 1.8K, 1.85K, 1.9K, 2K, 2.05K, 2.1K, 2.15K, 2.2K, 2.3K and 2.4K respectively;
stp8, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant; cutting off one group with the optimal growth in the ten groups of planting mechanisms, acquiring the corresponding illumination condition of the group and storing the illumination condition;
stp9, and Stp7 and Stp8 are repeated for several times to obtain the final illumination condition.
8. The control method of the big data based self-learning planting lamp control system according to any one of the above claims 1-6, wherein the number of the planting mechanisms is ten, and the control method comprises the following steps in sequence:
stp01, controlling the soil nutrient and soil humidity in each group of planting grooves (1) to be the same, and controlling the illumination of plant growth lamps (14) on each group of planting mechanisms to be 2K;
stp02, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp03, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp04, and the distances between the plant growth lamps (14) on the ten groups of planting mechanisms and the planting groove (1) are 1L, 1.1L,1.25L,1.5L,1.7L,2L,2.3L,2.7L,3.2L and 3.5L in sequence;
stp05, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp06, judging the optimal growing one of the ten planting mechanisms, obtaining the corresponding illumination distance parameter condition of the group and storing.
9. The control method of the big data based self-learning plantation lamp control system as claimed in any one of the above claims 1-6,
stp11, controlling the illumination of the plant growth lamps (14) on each group of planting mechanisms to be 2K, and controlling the distance between the plant growth lamps (14) on the planting mechanisms and the planting groove (1) to be 1.5L;
stp12, controlling the concentration, temperature and humidity of the carbon dioxide in the environment of the planting tank to be in a constant state through a carbon dioxide generating device, a humidifier and an air conditioner;
stp13, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp14, controlling the soil humidity in the ten groups of planting mechanisms to be P1, P1, P2, P2, P3, P3, P4, P4, P5 and P5 in sequence; and simultaneously controlling the concentrations of soil nutrients in the ten groups of planting mechanisms to be M1, M2, M1, M2, M1, M2, M1, M2, M1 and M2 in sequence.
Stp15, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp16, judging one of the ten planting mechanisms with the optimal growth, acquiring corresponding soil humidity and soil nutrient concentration parameters of the group, and storing the parameters.
10. The control method of the big data based self-learning plantation lamp control system according to any one of the above claims 1-6, wherein;
stp21, controlling soil nutrients and soil humidity in each group of planting grooves (1) to be the same, controlling illumination of plant growth lamps (14) on each group of planting mechanisms to be 2K, and controlling the distance between the plant growth lamps (14) on the planting mechanisms and the planting grooves (1) to be 1.5L;
stp22, acquiring images through an image acquisition module, and analyzing through an image analysis module to obtain the initial growth state of the plants;
stp23, controlling comprehensive conditions of carbon dioxide concentration, temperature and humidity of the environment where ten groups of planting tanks are located through a carbon dioxide generating device, a humidifier and an air conditioner to be respectively in T1, T2, T3, T4, T5, T6, T7, T8, T9 and T10;
stp24, growing for N days under the condition of the illumination, then carrying out image acquisition by an image acquisition module, and analyzing by an image analysis module to obtain the middle growth state of the plant;
stp25, judging the optimal growth one of the ten planting mechanisms, acquiring and storing the corresponding comprehensive condition parameters of carbon dioxide concentration, temperature and humidity.
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Application publication date: 20211214 |