KR102596522B1 - artificial intelligence farming type solar power generation system - Google Patents

Abstract

The present invention relates to an artificial intelligence farming solar power generation system, which includes a pivoting center axis rotatably coupled between a plurality of support frames extending vertically to the ground, and a support panel coupled to be supported on the pivoting center axis. A solar power generation unit including solar cell panels mounted spaced apart, and a light transmitting unit including at least one dichroic mirror mounted between the solar cell panels to transmit or reflect light in a set wavelength band for the incident light; , a sensing unit including at least one of a panel angle adjustment unit that adjusts the rotation angle of the rotation center axis, a rainfall sensor that detects whether it is raining, a snow sensor that detects whether it is snowing, and a wind speed sensor that detects wind speed, A control unit that controls the support angle of the support panel to be adjusted according to a set adjustment pattern according to the information detected by the sensing unit, an imaging unit that captures an image of the planting area below the solar power generation unit and provides the image to the control unit, and is connected to the control unit. It is equipped with a machine learning unit that determines the type of disease, pest, or weed from the image captured by the imaging unit and extracts and provides a control or removal method. This artificial intelligence agricultural solar power generation system provides the advantage of providing an optimized growth environment for crops grown in the area below the solar panel.

Description

Artificial intelligence farming type solar power generation system

The present invention relates to an artificial intelligence farming solar power generation system, and in detail, provides an optimized growth environment for crops grown in the lower part of the area where solar panels are installed and increases the power generation amount of solar power generation units installed on the upper part of farmland. It is about an artificial intelligence farming solar power generation system that increases and improves maintenance efficiency.

In general, solar power generation is a type of power generation technology using solar energy, and is a power generation method that directly converts solar light energy into electrical energy using a photoelectric converter called a solar cell. A solar cell refers to a device that can convert solar energy into electrical energy, and has recently been rapidly spreading due to lower manufacturing costs and increased efficiency of solar cell devices and support from national policies.

However, solar power generation using solar cells requires space for stable installation of solar cells, that is, a large installation site. However, the installation site has the disadvantage of being limited in being used for purposes other than solar power generation. It is inherent. Recently, in order to solve these shortcomings, agricultural solar power generation facilities that can combine solar power generation and agriculture have been proposed. Here, an agricultural solar power generation facility refers to installing a solar power generation facility on farmland to simultaneously produce solar power while farming, and refers to sharing solar power with agriculture and power generation. These agricultural solar power generation facilities have the effect of increasing the income of farmers, and their scope of use is gradually expanding.

Domestic Patent Publication No. 10-2020-0086146 discloses an agricultural solar power management system that obtains environmental information and adjusts the angle of the solar panel. However, the system is designed to adjust only the angle of the solar panel according to environmental information, so it cannot provide necessary light, such as visible light, to the lower area of the solar panel, and does not support growing crops accordingly, thereby reducing space utilization efficiency. It has the disadvantage of being difficult to improve.

The present invention was created to improve the above problems, and provides an optimized growth environment by applying artificial intelligence-based data utilization technology to crops grown in the area below solar panels, thereby improving crop productivity and solar power generation. The purpose is to provide an artificial intelligence farming-type solar power generation system that can contribute to improving efficiency.

In order to achieve the above object, the artificial intelligence farming solar power generation system according to the present invention includes a plurality of support frames extending vertically on the ground, a rotation center axis rotatably coupled between the support frames, and the rotation A solar power generation unit including solar cell panels spaced apart from each other on a support panel supported and coupled to a central axis; a light transmitting unit mounted between the solar cell panels and including at least one dichroic mirror that transmits or reflects light in a set wavelength band for incident light; a panel angle adjustment unit that adjusts the rotation angle of the rotation center axis; A sensing unit including at least one of a rainfall sensor that detects whether it is raining, a snow sensor that detects whether it is snowing, and a wind speed sensor that detects wind speed; a control unit that controls the support angle of the support panel to be adjusted according to a set adjustment pattern according to the information detected by the sensing unit; an imaging unit that captures an image of the planting area below the solar power generation unit and provides the image to the control unit; and a machine learning unit that is connected to the control unit and determines the type of disease, pest, or weed from the image captured by the imaging unit and extracts and provides a control or removal method.

Preferably, it is further provided with a night lighting unit mounted on the bottom of the support panel and emitting light to irradiate insufficient light to crops grown below,

The control unit controls the operation of the night lighting unit so that the amount of light irradiation to the crops is adjusted to correspond to the growth environment information provided by the machine learning unit.

According to one aspect of the present invention, the light transmitting unit includes a first dichroic mirror that transmits light in a wavelength band set for the incident light; and a diffusion lens that diffuses the light transmitted from the first dichroic mirror.

Alternatively, the light transmitting unit may include a second dichroic mirror that reflects light in a set wavelength band for the incident light; a path conversion mirror that reflects the light reflected from the second dichroic mirror to a set projection area; and a diffusion lens that diffuses the light reflected from the path change mirror.

The panel angle adjusting unit includes a cylinder with one end rotatably coupled to the support frame below the support panel and an expandable rod at the other end; a forward and backward rod that is hingedly coupled to the rod of the cylinder and moves forward and backward in response to expansion and contraction of the rod; It may be constructed with a tilting support bracket, one end of which is coupled to the rotation center axis and extending downward, and the other end of which is hinged to the advance and retreat rod, and which rotates the rotation center axis forward and backward in response to the advance and retreat of the advance and retreat rod.

According to the artificial intelligence farming solar power generation system according to the present invention, an optimized growth environment is provided for crops grown in the area below the solar panel, and the occurrence of diseases, pests, and weeds can be detected and quick action can be taken. By supporting this, agriculture can be carried out more conveniently, and it provides the effect of increasing agricultural production by selectively providing the light needed for cultivated crops. The solar power generation unit at the top operates in a rotational manner, increasing power generation and typhoon weather. Not only can it increase structural stability by reducing city wind load, but it also provides the advantage of increasing solar power generation efficiency by enabling non-point pollution or snow removal work even after snow or rain.

1 is a side view showing an artificial intelligence farming solar power generation system according to the present invention;
Figure 2 is a block diagram showing the control system of the artificial intelligence farming solar power generation system of Figure 1;
Figure 3 is a perspective view of the solar power generation unit of Figure 1,
Figure 4 is a perspective view showing the solar cell panel and the light transmitting part of the solar power generation unit of Figure 3 separated;
Figure 5 is a side view showing the solar power generation unit of Figure 3 from another angle;
Figure 6 is an enlarged view of a portion of Figure 5,
FIG. 7 is a diagram showing an example of a light transmitting unit applied to FIG. 1.

Hereinafter, an artificial intelligence farming solar power generation system according to a preferred embodiment of the present invention will be described in more detail with reference to the attached drawings.

Figure 1 is a side view showing the artificial intelligence farming solar power generation system according to the present invention, and Figure 2 is a block diagram showing the control system of the artificial intelligence farming solar power generation system of Figure 1.

Referring to Figures 1 and 2, the artificial intelligence farming solar power generation system 100 according to the present invention includes a solar power generation unit 110, a light transmitting unit 120, a panel angle adjusting unit 130, and a sensing unit 140. ), an imaging unit 148, a machine learning unit 150, a night lighting unit 160, and a control unit 180.

The solar power generation unit 110 is capable of generating power by supporting solar cell panels 118, in which solar cells that convert light energy into electrical energy, are spaced apart from the ground, and the detailed structure is shown in Figures 3 to 3. The description will be made with reference to FIG. 6 .

The solar power generation unit 110 includes a plurality of support frames 112 extending vertically to the ground, a rotation center axis 114 rotatably coupled between the support frames 112, and a rotation center axis 114. It is constructed with rectangular solar cell panels 118 mounted spaced apart from each other on a support panel 116 coupled to be supported. Here, the space between the solar cell panels 118 arranged to be spaced apart from each other along the direction of extension of the pivot axis 114 is used as a space where the light transmitting portion 120, which will be described later, can be installed. The separation distance between the solar cell panels 118 installed along the direction of extension of the rotational center axis 114 may be appropriately applied, and for example, is about 1 to 5 cm.

The light transmitting unit 120 is supported by the support panel 116 in the space between the solar cell panels 118 and includes at least one dichroic mirror that transmits or reflects light in a set wavelength band for the incident light. Includes.

The light transmitting part 120 may be constructed so that sunlight can be smoothly irradiated to the crops 10 planted in the lower area of the solar cell panel 118 and provide light in the wavelength range required for crop growth.

An example of the light transmitting unit 120 will be described with reference to FIG. 7 .

Referring to FIG. 7, the light transmitting unit 120 includes a first dichroic mirror 121 that transmits light in a wavelength band set for the incident light, and a first dichroic mirror and a transmission wavelength band for the incident light. It is provided with a differently set second dichroic mirror 121, first and second path conversion mirrors 125 and 126, and a diffusion lens 127.

The first dichroic mirror 121 transmits green light and red light and reflects blue light of the incident sunlight. The first dichroic mirror 121 is arranged at an angle with respect to the incident direction of sunlight.

The second dichroic mirror 121 is disposed opposite to the first dichroic mirror 121 and reflects red light and transmits green light among the light passing through the first dichroic mirror 121. there is. The second dichroic mirror 122 is arranged symmetrically and inclined with respect to the first dichroic mirror 121.

The first path conversion mirror 125 is arranged to change the optical path of the blue light reflected from the first dichroic mirror 121 to a set projection area, that is, in the direction of irradiation of the crop 10, and reflects the incident light.

The second path conversion mirror 126 is arranged to change the optical path of the red light reflected from the second dichroic mirror 121 to a set projection area, that is, in the direction of irradiation of the crop 10, and reflects the incident light.

The diffusion lens 127 is installed on each optical path of red, green, and blue light to diffuse the transmitted light, and a concave lens is applied. That is, the diffusion lens 127 divides the path of blue light that passes through the first dichroic mirror 121 and the first path conversion mirror 125 and the blue light that passes through the second dichroic mirror 122. They are disposed in the path of green light and the path of red light that passes through the second dichroic mirror 122 and the second path conversion mirror 126, respectively.

The light transmitting unit 120 is illustrated to separate red, green, and blue light and provide them through different paths. Alternatively, it may be constructed to emit only one or two colors of red, green, and blue light. Of course.

This light transmitting part 120 can provide light in the wavelength range necessary for growth to the crops 10 planted under the solar cell panel 118 during the day, so they are shaded regardless of the occupied area of the solar cell panel 118. The area is resolved to support normal growth of the crop (10).

The panel angle adjustment unit 130 adjusts the rotation angle of the rotation center axis 114, and will be described with reference to FIGS. 5 and 6.

The panel angle adjustment unit 130 includes a cylinder 132, an advance/retract rod 135, and a tilting support bracket 137.

The cylinder 132 has one end of the body 132a rotatably coupled to the support frame 112 under the support panel 116, and an expandable rod 132b at the other end.

The advance and retreat rod 135 is hingedly coupled to the rod 132b of the cylinder 132 and the first auxiliary bracket 136, and advances and retreats in response to the expansion and contraction of the rod 132b.

The tilting support bracket 137 has one end coupled to the rotation center axis 114 and extends downward, and the other end is hinged through a second auxiliary bracket 138 fixed to the advance/retract rod 135. Accordingly, the tilting support bracket 137 rotates the rotation center axis 114 forward and backward in response to the advance and retreat of the advance and retreat rod 135 to interlock and adjust the posture of the solar cell panel 118.

The sensing unit 140 includes a rainfall sensor 141, a snowfall sensor 142, a wind speed sensor 143, and a solar altitude sensor 144.

The rain sensor 141 detects whether it is raining and provides the information to the control unit 180.

The snow sensor 142 detects whether it is snowing and provides the information to the control unit 180.

The wind speed sensor 143 is provided to the control unit 180 to detect wind speed.

The solar altitude sensor 144 measures the solar altitude during the day and provides it to the control unit 180.

In addition to the exemplified sensors, the sensing unit 140 includes a sensor for detecting the amount of snow, a camera for capturing images to visually monitor the operating state of the solar power generation unit 110, a temperature sensor, a humidity sensor, and a sensor for determining the amount of sunlight. Of course, various sensors that can monitor crop growth and stable solar power generation, such as illuminance sensors, can be applied.

The imaging unit 148 captures an image of the planting area below the solar power generation unit 110 and provides the image to the control unit 148 so that the machine learning unit 150, which will be described later, can use it.

The imaging unit 148 periodically photographs planted crops and analyzes their condition, and also detects distance information from a thermal imaging camera or plant that can measure temperature, and detects red (R), green (G), and blue ( B) It can be built in a variety of ways, including fixed or mobile cameras and auxiliary sensors that support the collection of crop growth data, such as color-distinguishing cameras.

The machine learning unit 150 is connected to the control unit 180 and determines the types of diseases, pests, and weeds from the images captured and provided by the imaging unit 148, extracts control or removal methods, and provides them to the control unit 180. do. In this case, the control unit displays the pest control or removal method extracted from the machine learning unit 150 through the display unit 173 and transmits it to the administrator terminal through the communication unit 175.

In addition, the machine learning unit 150 provides growth environment information for crops 10 grown below the area where the solar power generation unit 110 is installed. The machine learning unit 150 learns the crop cultivation growth environment and applies it to provide information related to crop cultivation by period, such as the amount of sunlight required for crop growth, light saturation point, and wind speed prediction information, based on the input data. .

Preferably, the machine learning unit 150 includes seedling method, transfer date or sowing date, variety characteristics (early maturing species (fine rice, group rice, Geumo rice, Manna rice, Manan rice, late autumn rice, Munmun rice, Sandeuljinmi, Sambaek rice, Samcheon rice, Sangmi rice, Sangsan rice, Sangju rice, Sangju glutinous rice, Saesangju rice, Sobaek rice, Shinunbong rice, Sinunbong rice No. 1, Odae rice No. 1, Odae rice, Ungwang rice, Undu rice, Unmi rice, Unbong rice, Jeokjinju rice, Joryeong Rice, early black rice, Joan rice, Junam early rice, Junghwa rice, Jinmi rice, Jinbu rice, Jinbu Ol rice, Jinbu glutinous rice, Taebong rice, Taeseong rice, Hyangmi rice No. 2, Hoban rice, Golden purple rice, Black pearl rice, etc.), medium-growing rice ( Reclamation rice, Goami rice No. 3, Gopum rice, Geuman rice, Geumoh rice No. 2, Nonglimna rice No. 1, Blizzard rice, Dasan, Dasan rice No. 1, Daejeong rice No. 1, Daebyeong rice, Donghae rice, Borami, Jewel rice, Samdeok rice , Sampyeong rice, Sangnam field rice, Sangok rice, Seoan No. 1, Seoan rice, Seolhyang rice, Sobi rice, Sura rice, Sinseon glutinous rice, Anda, Yeongan rice, Wonhwang rice, Juan rice, Cheonga rice, Cheongan rice, Big-eye rice, Flavor 1 Ho, Jangjangbyeo, Hiami, Hanganchal No. 1, Hanareum, Haeormi, Haechanmulgyeol, Haepyeong rice, Haepyeong rice, Hongjinju, Hwapyeong rice, Hwaseon rice, Hwaseong rice, Hwayeong rice, Hwajin rice, Heukgwang rice, Heukseol, etc.), mid-late ripening species (Ossassin rice, Goami 2, Goami rice, Nakdong rice, Namgang rice, Nampyeong rice, Dami rice, Dacheong rice, Daesan rice, Daean rice, Daecheong rice, Dongan rice, Dongjin 1, Dongjin 2, Dongjin rice, Dongjin glutinous rice , Donghaejin rice, Draechan, Manmi rice, Mihyang rice, Baekseol glutinous rice, Baekok glutinous rice, Baekjinju No. 1, Baekjinju rice, Samgwang rice, Saegyeha rice, Saenuri rice, Saechucheong rice, Seogan rice, Seopyeong rice, Seolgaeng rice, Shennong heukchal. , Shinnongjin rice, Shinmyeong black rice, Shindo black rice, Aranghyang rice, Onuri, Ilmi rice, Jongnam rice, Junam rice, Jinbaek, Jinsumi, Cheongdam rice, Cheongho rice, Chucheong rice, Chilbo rice, Pyeongan rice, Hanam rice, Hanmaeum rice, Hyangnam rice, Fragrant rice No. 1, Hojin rice, Hopyeong rice, Hopum rice, Hwarang rice, Golden Nodeul, Golden Nuri, etc.), fertilization amount, growth stage (tillling stage, water forming stage, watering stage) Comparison of images captured based on agricultural data Machine learning technology to predict crop condition and yield, weeds (Amaranth amaranth, Amaranth, Amaranth, Amaranth, Amaranth, Amaranth, Amaranth, Amaranth, Amaranth, etc.), Monitoring technology that detects signs (observed, unobserved) and provides information by disease type (blast, leaf spot blight, bacterial leaf blight, pepper anthracnose, pear black spot, pear red spot, barley red mold, apple) Brown spot disease, red pepper blight, etc.), by type of pest (leafhoppers, Japanese moth, stink bug, yellow flower thrips, green leafhopper, rice leafhopper, two-lined night moth, rice leaf beetle, water weevil, stink bug, leafhopper, rice leafhopper) , Chinese cabbage moth, rice stem oyster fly, black moth, rice leaf beetle, striped blue butterfly, block thrips, white-backed planthopper, cotton aphid, peach aphid, soybean aphid, millet border aphid, borderless aphid, brown-winged leafhopper, convex thrips. , poinsettia thrips, pumpkin flower fruit fly, white-banded moth, etc.) and artificial intelligence agriculture, including smart diagnosis technology for disease detection, whether or not fertilization was sown before transfer, and tracking and prediction technology for climate change by year (grades 1 to 5). Apply what it was built to do.

The night lighting unit 160 is mounted on the bottom of the support panel 116 to provide night lighting to supplement the insufficient amount of light when there is insufficient sunlight for the crops 10 grown at the bottom. The night lighting unit 160 may be constructed in a structure in which a plurality of light emitting diodes that emit light in a wavelength band necessary for the growth of crops 10 are arrayed and mounted.

The control unit 171 supports setting supported functions and inputting necessary information.

The display unit 173 is controlled by the control unit 180 to display display information.

The communication unit 175 communicates with a registered administrator terminal (not shown) and a management server (not shown).

The control unit 180 controls the support angle of the support panel 116 to be adjusted according to a set adjustment pattern according to the information detected by the sensing unit 140.

The control unit 180 uses solar altitude information provided by the solar altitude sensor 144 to control sunlight to move from east to west over time so that it is incident perpendicularly on the solar cell panel 118.

In addition, when snow is detected in the morning by the snowfall sensor 142 or rain is detected in the morning by the rainfall sensor 141, the control unit 180 controls the panel angle adjustment unit 130 to adjust the angle of the solar cell panel 118 to the east. Rotate 30 degrees and perform a whisking operation by repeatedly moving the solar panel 118 vertically and horizontally for, for example, 20 minutes during the set whisking time from 12 o'clock. This shaking operation is intended to improve power generation efficiency by removing foreign substances attached to the solar cell panel 118.

In addition, when snow or rain is detected in the afternoon, the control unit 180 rotates the angle of the solar panel 118 to the west by 30 degrees, and after sunset, the solar panel 118 stays out of the sun for a set brushing time, for example, 20 minutes, as described above. A whisking operation is performed to repeatedly move the battery panel 118 vertically and horizontally.

In addition, the control unit 180 controls the solar panel 118 to be horizontal when the wind speed is 15 m/s or more from the wind speed sensor 143 and when strong winds of 15 m/s or more are predicted within 24 hours through the weather forecast provided by the management server. Control to maintain the status. Additionally, the control unit 180 controls the solar cell panel 118 to maintain a horizontal state when a strong wind of 15 m/s or more is predicted from the machine learning unit 150 within 24 hours.

In addition, the control unit 180 determines the amount of sunlight for the crops in response to the growth environment information provided by the machine learning unit 150, and controls the operation of the night lighting unit 160 when the amount of sunlight is insufficient.

According to the artificial intelligence farming solar power generation system described above, it provides the advantage of providing an optimized growth environment for crops grown in the area below the solar cell panel.

110: solar power generation unit 120: light transmitting unit
130: Panel angle adjustment unit 140: Sensing unit
150: Machine learning department
160: night lighting unit 170: control unit

Claims (6)

It includes a plurality of holding frames extending vertically to the ground, a pivoting center axis rotatably coupled between the holding frames, and solar cell panels mounted spaced apart from each other on a support panel coupled to be supported on the pivoting center axis. A solar power generation unit;
a light transmitting unit mounted between the solar cell panels and including at least one dichroic mirror that transmits or reflects light in a set wavelength band for incident light;
a panel angle adjustment unit that adjusts the rotation angle of the rotation center axis;
A sensing unit including at least one of a rainfall sensor that detects whether it is raining, a snow sensor that detects whether it is snowing, and a wind speed sensor that detects wind speed;
a control unit that controls the support angle of the support panel to be adjusted according to a set adjustment pattern according to the information detected by the sensing unit;
an imaging unit that captures an image of the planting area below the solar power generation unit and provides the image to the control unit;
A machine learning unit that is connected to the control unit and determines the type of disease, pest, or weed from the image captured by the imaging unit and extracts and provides a control or removal method,
When snow is detected in the morning by the snowfall sensor or rain is detected by the rainfall sensor, the control unit controls the panel angle adjustment unit to rotate the angle of the solar panel by 30 degrees to the east, and operates the solar panel for a set shaking time from 12 o'clock. A dusting operation is performed by repeatedly moving the solar panel vertically and horizontally, and when snow or rain is detected in the afternoon, the angle of the solar cell panel is rotated 30 degrees to the west, and after sunset, the solar cell is shaken for a set brushing time. A dusting operation is performed to repeatedly move the panel vertically and horizontally, and when the wind speed is 15 m/s or more from the wind speed sensor and the weather forecast predicts strong winds of 15 m/s or more within 24 hours, the solar panel is in a horizontal state. An artificial intelligence farming solar power generation system characterized by control to maintain. According to paragraph 1,
It is further provided with a night lighting unit mounted on the bottom of the support panel and emitting light to irradiate insufficient light to crops grown below,
The control unit is an artificial intelligence farming solar power generation system, characterized in that the operation of the night lighting unit is adjusted so that the amount of light irradiation to crops is adjusted in accordance with the growth environment information provided by the machine learning unit. The light transmitting unit of claim 1, further comprising: a first dichroic mirror that transmits light in a wavelength band set for the incident light;
An artificial intelligence agricultural solar power generation system characterized by comprising a diffusion lens that diffuses the light transmitted from the first dichroic mirror. The light transmitting unit of claim 1, further comprising: a second dichroic mirror that reflects light in a set wavelength band for incident light;
a path conversion mirror that reflects the light reflected from the second dichroic mirror to a set projection area;
An artificial intelligence agricultural solar power generation system comprising a diffusion lens that diffuses the light reflected from the path change mirror. The method of claim 1, wherein the panel angle adjustment unit
a cylinder having one end rotatably coupled to the support frame below the support panel and a rod having an expandable other end;
a forward and backward rod that is hingedly coupled to the rod of the cylinder and moves forward and backward in response to expansion and contraction of the rod;
An artificial intelligence characterized by comprising a tilting support bracket, one end of which is coupled to the rotation center axis and extending downward, the other end of which is hinged to the advance and retreat rod, and which rotates the rotation center axis forward and backward in response to the advance and retreat of the advance and retreat rod. Agricultural solar power generation system. delete

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