KR20200037559A - System for computing parameter of heating under control logic of heating actuator, server and method for determining parameter of heating actuator in greenhouse - Google Patents

Abstract

The present invention relates to a server for determining a heating parameter for calculating a control parameter of a proportional-integral (PI) control logic for driving a greenhouse heating driver. According to the present invention, the server comprises: a greenhouse data collection unit which collects greenhouse data including setting information, environmental information, and heating driver information from a greenhouse control system of a greenhouse; a greenhouse modeling unit for generating a greenhouse model using an artificial neural network which receives the greenhouse data; a condition receiving unit for receiving a restriction condition of a heating parameter from the greenhouse control system of the greenhouse; and a heating parameter extraction unit for deriving the heating parameter from the greenhouse model based on the restriction condition. The heating parameter may include the maximum heating tube temperature and the minimum heating tube temperature.

Description

A system for determining heating parameters of a heating driver for a greenhouse and a system for calculating heating parameters based on control logic of a heating driver {SYSTEM FOR COMPUTING PARAMETER OF HEATING UNDER CONTROL LOGIC OF HEATING ACTUATOR, SERVER AND METHOD FOR DETERMINING PARAMETER OF HEATING ACTUATOR IN GREENHOUSE}

The present invention relates to a server for determining a heating parameter of a heating driver for a greenhouse, a system for calculating a heating parameter based on a control logic of a heating driver.

Plant cultivation is an agriculture that produces high-quality crops by creating better environmental conditions than when the cultivation is difficult in the field due to the environmental control of the greenhouse. Greenhouses are implemented in various forms, such as glass greenhouses, vinyl greenhouses, solar-powered combination plants, and artificial plants.

In plant cultivation, a sophisticated complex environmental control method is required to improve crop productivity and harvest high-quality crops. Conventional greenhouse complex environmental control technology is a method in which a sensor, a driver, and a controller are connected in a greenhouse to perform environmental control and to monitor a greenhouse control situation by being connected to a controller and a central system.

According to the conventional greenhouse control system, when the external conditions that can affect the greenhouse control, for example, climate change by region, greenhouse structure, etc. are varied, optimized greenhouse control is difficult.

According to the conventional greenhouse control system, in the case of ventilation, proportional control and proportional integration technology have been developed, but in the case of heating, heating control is performed only by valve control (normal simple open and closed control) of the boiler.

In order to optimally control the heating of the greenhouse, it is necessary to understand the parameters of the greenhouse heating control system and PI control logic. However, as most of the domestic farmers enter an aging society, there are many difficulties for the elderly to deal with the greenhouse heating control system by directly setting the proportional control parameter value of the greenhouse heating control system.

Korean Patent Publication No. 2014-0077720 (released on June 24, 2014)

The present invention is to generate a greenhouse model using an artificial neural network using greenhouse data as input, and to derive heating parameters from the greenhouse model based on the limitation conditions of the heating parameters received from the greenhouse control system. In addition, the present invention is to calculate the control parameter for driving the heating driver using the derived heating parameters. However, the technical problems to be achieved by the present embodiment are not limited to the technical problems as described above, and other technical problems may exist.

As a technical means for achieving the above technical problem, a server for determining a heating parameter for calculating a control parameter of a PI (Proportional-Integral) control logic for driving a heating driver of a greenhouse according to the first aspect of the present invention A greenhouse data collection unit that collects greenhouse data including setting information, environmental information, and heating driver information from the greenhouse control system of the greenhouse; A greenhouse modeling unit generating a greenhouse model using an artificial neural network inputting the greenhouse data; A condition receiving unit for receiving a limit condition of the heating parameter from the greenhouse control system of the greenhouse; And a heating parameter extracting unit for deriving the heating parameter from the greenhouse model based on the restriction condition, wherein the heating parameter may include a maximum heating pipe temperature and a minimum heating pipe temperature.

A greenhouse control system according to a second aspect of the present invention includes a sensor unit for monitoring environmental information of a greenhouse and driver information for heating; A transmission unit that transmits greenhouse data including setting information, the environmental information, and the heating driver information to a heating parameter determination server; A receiving unit that receives the heating parameter determined by the heating parameter determination server from the heating parameter determination server; And a control unit for calculating PI (Proportional-Integral) control logic for driving the heating driver based on the heating parameter, and controlling the heating driver based on the PI control logic, wherein the heating parameter is the maximum heating tube temperature. And a minimum heating tube temperature.

The method for calculating the control parameters of the PI (Proportional-Integral) control logic for driving the driver for heating of the greenhouse in the heating parameter determination server according to the third aspect of the present invention is for setting information, environment information, and heating from the greenhouse control system of the greenhouse Collecting greenhouse data including driver information; Generating a greenhouse model using an artificial neural network using the greenhouse data as input; Receiving a restriction condition of the heating parameter from the greenhouse control system of the greenhouse; And deriving the heating parameter from the greenhouse model based on the limiting condition, wherein the heating parameter may include a maximum heating tube temperature and a minimum heating tube temperature.

The above-described problem solving means are merely exemplary and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, there may be additional embodiments described in the drawings and detailed description of the invention.

According to any one of the above-described problem solving means of the present invention, the present invention can predict the environmental change of the greenhouse by generating a greenhouse model using an artificial neural network using greenhouse data as input.

In addition, the present invention can derive the heating parameters from the greenhouse model based on the limiting conditions of the heating parameters received from the greenhouse control system. Through this, it is possible to increase the accuracy of the control of the actuator of the greenhouse by determining the heating parameters that are variably adapted according to the greenhouse data.

In addition, the present invention can derive an optimal heating parameter for a heating driver using a global search method regardless of the size, material, and structure of the greenhouse.

In addition, since the present invention can determine the optimal parameter set by simulating heating parameters through a greenhouse model, it is possible to reduce time and cost compared to experiments in an actual greenhouse.

In addition, the present invention can provide an optimal cultivation environment for cultivation of crops by controlling a heating driver for a greenhouse using a control parameter for driving a heating driver calculated using a heating parameter, and operating the greenhouse control system. Variations in skill and crop cultivation techniques can be minimized, and crop yield of uniform quality can be maximized.

1 is a configuration diagram of a heating parameter determination system of a greenhouse heating driver according to an embodiment of the present invention.
2 is a block diagram of a heating parameter determination server shown in FIG. 1 according to an embodiment of the present invention.
3A to 3C are diagrams illustrating a method of deriving an optimal set of heating parameters according to an embodiment of the present invention.
4 is a flowchart illustrating a method of calculating heating parameters in a heating parameter determination server according to an embodiment of the present invention.
5 is a block diagram of a greenhouse control system shown in FIG. 1 according to an embodiment of the present invention.
6A to 6E are views for explaining a control method of a conventional heating driver.
7 is an operation flowchart showing a method of controlling a heating driver of a greenhouse in a greenhouse control system according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present invention pertains can easily practice. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. In addition, in order to clearly describe the present invention in the drawings, parts irrelevant to the description are omitted, and like reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element in between. . Also, when a part “includes” a certain component, this means that other components may be further included rather than excluding other components, unless otherwise specified.

In the present specification, the term “unit” includes a unit realized by hardware, a unit realized by software, and a unit realized by using both. Further, one unit may be realized by using two or more hardware, and two or more units may be realized by one hardware.

Some of the operations or functions described in this specification as being performed by a terminal or device may be performed instead on a server connected to the corresponding terminal or device. Similarly, some of the operations or functions described as being performed by the server may be performed in a terminal or device connected to the corresponding server.

Hereinafter, specific contents for carrying out the present invention will be described with reference to the accompanying drawings or processing flow charts.

1 is a configuration diagram of a heating parameter determination system of a greenhouse heating driver according to an embodiment of the present invention.

Referring to FIG. 1, the heating parameter determination system of the heating driver may include a heating parameter determination server 100 and a plurality of greenhouse control systems 110. However, since the heating parameter determination system of the heating driver of FIG. 1 is only one embodiment of the present invention, the present invention is not limitedly interpreted through FIG. 1, and is configured differently from FIG. 1 according to various embodiments of the present invention It may be.

Generally, each component of the heating parameter determination system of the heating actuator of FIG. 1 is connected via a network. The network means a connection structure capable of exchanging information between nodes such as terminals and servers, and examples of such a network include 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), and WIMAX ( World Interoperability for Microwave Access), Wi-Fi, 3G, 4G, 5G, and the like.

The heating parameter determination server 100 may collect greenhouse data including setting information of each greenhouse, environment information, and driver information for heating from a plurality of greenhouse control systems 110.

The heating parameter determination server 100 may generate a greenhouse model for each greenhouse control system 110 using an artificial neural network as input to each greenhouse data received from the plurality of greenhouse control systems 110.

The heating parameter determination server 100 may receive the restriction conditions of the heating parameters from each of the plurality of greenhouse control systems 110. Here, the heating parameter may include the temperature of the heating pipe installed in the greenhouse, the target heating set temperature of the greenhouse, the maximum P band, the minimum P band, the integral temperature, and the integral opening value. The limiting condition of the heating parameter may include at least one of a condition for an evaluation range of each heating parameter and a condition for a section of the evaluation range for each heating driver.

The heating parameter determination server 100 may calculate heating parameters for driving the heating driver of the greenhouse for each of the plurality of greenhouse control systems 110.

Specifically, the heating parameter determination server 100 may derive heating parameters for calculating control parameters of a PI (Proportional-Integral) control logic for driving control of a greenhouse heating driver for each greenhouse control system 110.

The heating parameter determination server 100 derives a heating parameter suitable for each greenhouse control system 110 from the greenhouse model generated for each greenhouse control system 110 based on each restriction condition received from each greenhouse control system 110. You can.

The heating parameter determination server 100 may transmit the derived heating parameters to each greenhouse control system 110.

Each of the plurality of greenhouse control systems 110 includes a plurality of sensors and a plurality of heating actuators, measures internal environment information of the greenhouse through the plurality of sensors, and adjusts the internal temperature of the greenhouse through the plurality of heating drivers. have.

For example, the plurality of sensors may include a temperature sensor for measuring the temperature in the greenhouse, a humidity sensor for measuring humidity, an optical sensor for measuring the amount of light, and the like.

The heating driver may be composed of various types of drivers that can control the internal temperature of the greenhouse. For example, the driver for heating may include at least one of a skylight, a horizontal curtain, a 3Way valve, and a circulation pump. Here, the skylight and the horizontal curtain are actuators used to block light from outside the greenhouse, and the 3Way valve switches the flow path of the cooling medium or the heating medium while simultaneously controlling the flow rate, and the cold heat returned to the cooling and heating medium storage tanks. It is a driver that plays a role of returning a medium or a warm medium to a heat pump. Here, as the heat source of the cold medium or the warm medium, air, well water, solar heat, and geothermal heat may be used.

The plurality of greenhouse control systems 110 monitor the environmental information of the greenhouse and the driver information of the heating driver installed in the greenhouse control system 110, and determine the heating parameters of the greenhouse data (including setting information, environment information, and heating driver information) It can be transmitted to the server 100.

The plurality of greenhouse control systems 110 may collect environmental information measured by a plurality of sensors installed in the heating pipe of the greenhouse or the greenhouse control system 111 through a sensor node. For example, the plurality of greenhouse control systems 110 may include a supply temperature of a heating tube measured by a temperature sensor installed in a heating tube of a greenhouse (temperature when a cooling medium or a heating medium is supplied to the heating tube) or a heating tube. The recovery temperature (the temperature at which the cold medium or the warm medium is recovered through the heating pipe) can be collected through a temperature sensor. For example, the plurality of greenhouse control systems 110 may collect internal / external temperatures of a greenhouse measured by a temperature sensor inside the greenhouse through a temperature sensor.

The plurality of greenhouse control systems 110 may receive heating parameters determined by the heating parameter determination server 100 from the heating parameter determination server 100.

The plurality of greenhouse control systems 110 may calculate PI control logic for driving the heating driver based on the received heating parameter, and control the heating driver based on the PI control logic.

Hereinafter, the operation of each component of the heating parameter determination system of FIG. 1 will be described in more detail.

2 is a block diagram of a heating parameter determination server 100 shown in FIG. 1 according to an embodiment of the present invention.

Referring to FIG. 2, the heating parameter determination server 100 includes a greenhouse data collection unit 200, a greenhouse modeling unit 210, a condition receiving unit 220, a heating parameter extraction unit 230, and a parameter transmission unit 240. It can contain. Here, the heating parameter extraction unit 230 may include an evaluation range determination unit 232 and a parameter set determination unit 234. However, the heating parameter determination server 100 illustrated in FIG. 2 is only one implementation example of the present invention, and various modifications are possible based on the components illustrated in FIG. 2.

The greenhouse data collection unit 200 may collect, in real time, greenhouse data including setting information, environmental information, and heating driver information from the greenhouse control system 111. Here, the setting information may include, for example, a target setting value (target heating temperature) for the greenhouse temperature. The environmental information may include, for example, the internal temperature of the greenhouse, the supply temperature of the heating pipe of the greenhouse, and the recovery temperature of the heating pipe of the greenhouse. The environmental information may include, for example, properties of a greenhouse (greenhouse type, size, material, etc.), information inside and outside the greenhouse.

The heating driver information includes, for example, installation information of each heating driver (for example, at least one of a skylight, a horizontal curtain, a 3Way valve, and a circulation pump) in a greenhouse (for example, information on the type, location, and holding of the heating driver) and It may include driving history information of each heating driver (on / off information of the hourly heating driver).

The greenhouse modeling unit 210 may generate a plurality of greenhouse models using artificial neural networks using the collected greenhouse data as input. For example, the greenhouse modeling unit 210 may generate a greenhouse model using an artificial neural network based on a Leverenberg-marquardt backpropagation algorithm. Here, the artificial neural network is a statistical learning algorithm in which artificial neurons (nodes) that have formed a network by combining synapses are trained by adjusting synaptic binding strength through learning. Such an artificial neural network may generate a predictive model based on attributes learned through training data.

For example, the greenhouse modeling unit 210 may generate a greenhouse model for the internal temperature of the greenhouse by using setting information, environmental information, and heating driver information of the greenhouse control system 111 as input parameters of the artificial neural network. In addition, the greenhouse modeling unit 210 learns a virtual greenhouse for a predetermined period based on setting information, environment information, and heating driver information of the greenhouse control system 111 to learn a plurality of greenhouse models (eg, a virtual greenhouse model, virtual Greenhouse environment model, virtual greenhouse heating driver model) can be created.

For example, the greenhouse modeling unit 210 performs modeling of a virtual greenhouse using the setting information of the greenhouse control system 111, the environment information, and the driver information for heating as input values of an artificial neural network algorithm and outputting an increase or decrease of the temperature of the greenhouse. Can be done. Here, since the greenhouse temperature has various shapes according to the structure, size, material, and external environment of the greenhouse, it cannot be expressed in a constant functional formula. Therefore, the greenhouse modeling unit 210 may apply an artificial neural network algorithm, which is a learning algorithm for representing a nonlinear model, to perform modeling of a virtual greenhouse based on greenhouse temperature and driver information for heating.

For example, the greenhouse modeling unit 210 uses the setting information of the greenhouse control system 111, environmental information, and heating driver information as input values of an artificial neural network algorithm, and corresponds to output values of each of the setting information, environmental information, and heating driver information. Can model the environment of a virtual greenhouse.

The condition receiving unit 220 may receive a limit condition of a plurality of heating parameters from the greenhouse control system 111 of the greenhouse. Here, the heating parameter is a value used to calculate a control parameter for driving the heating driver installed in the greenhouse, and may be a variable variable according to greenhouse setting information, environmental information, and heating driver information. The heating parameters include at least one of the temperature of the heating pipe installed in the greenhouse (maximum heating pipe temperature and minimum heating pipe temperature), the target heating setting temperature of the greenhouse, the maximum P band, the minimum P band, the integration temperature, and the integral opening value. You can.

The condition receiving unit 220 may receive a restriction condition including at least one of a condition for each evaluation range of a heating parameter and a condition for a section of the evaluation range for each heating driver from the greenhouse control system 111 of the greenhouse. Specifically, the condition receiving unit 220 is a value for calculating a control parameter of a PI (Proportional-Integral) control logic that controls a heating driver from the greenhouse control system 111 of the greenhouse, and is a limiting condition of heating parameters for each heating driver. Can receive. Here, the control parameters of the PI control logic may include a proportional gain (Kp) and an integral gain (Kpi). For example, the condition receiving unit 220 is a value for calculating control parameters of a PI (Proportional-Integral) control logic that controls a skylight as a heating driver, and can receive a restriction condition of a heating parameter for the skylight. have.

The heating parameter extraction unit 230 may derive the heating parameter from the greenhouse model based on the limiting condition of the heating parameter. For example, the heating parameter extraction unit 230 may derive heating parameters for each heating driver from the greenhouse model based on the restriction conditions of the heating parameters received for each heating driver.

On the other hand, the heating parameter for calculating the control parameter of the PI control logic for driving the existing heating driver is fixed at a preset value, so it is difficult to optimize the greenhouse control for the target temperature even if the administrator sets the target temperature. Therefore, the heating parameter extracting unit 230 may derive heating parameters (values for calculating control parameters of the PI control logic) based on the global search method in order to precisely control the internal temperature of the greenhouse for each greenhouse.

3A to 3C are diagrams illustrating a method of deriving an optimal set of heating parameters according to an embodiment of the present invention.

Referring to FIG. 3A, the evaluation range determination unit 232 may determine the evaluation range 310 of each of the plurality of heating parameters 300 and divide the evaluation range 310 into a plurality of evaluation sections 320. . Here, the plurality of parameters 300 may include a maximum heating tube temperature, a minimum heating tube temperature, a target heating setting temperature, a maximum P band, a minimum P band, an integral temperature, and an integral opening value. For example, the evaluation range determining unit 232 determines the evaluation range for the maximum heating tube temperature based on the season from 18 to 22 degrees, and determines the evaluation range for the maximum heating tube temperature in 5 evaluation sections (1 section is 18 The second section can be divided into 19 degrees, the third section is 20 degrees, the fourth section is 21 degrees, and the fifth section is 22 degrees). As another example, when the evaluation range for the minimum heating tube temperature is 4 to 6 degrees, the evaluation range determination unit 222 sets the evaluation range for the minimum heating tube temperature in 3 evaluation sections (the first section is 4 degrees, the second section) Can be divided into 5 degrees, and the third section is 6 degrees).

Referring to FIG. 3B, the evaluation range determination unit 232 includes heating parameter information (for example, FIG. 3A) including the evaluation range 310 and the evaluation section 320 for each of the determined plurality of heating parameters 300. Heating parameter information) may be provided to the user through the greenhouse control system 111.

Subsequently, the evaluation range determining unit 232 receives the restriction condition 330 of the heating parameter from the user, and the evaluation range 310 of the heating parameter 300 based on the restriction condition 330 of the heating parameter set by the user Alternatively, the evaluation section 320 may be changed. For example, the evaluation range determining unit 232 sets the evaluation section of the target heating set temperature to a plurality of evaluation sections for the target heating set temperature (eg, first evaluation section: 16 degrees, second evaluation section: 17 degrees, second 3 evaluation intervals: 18 degrees, 4th evaluation interval: 19 degrees, and 5th evaluation interval: 20 degrees) may be changed to an evaluation interval selected by the user (eg, 1st evaluation interval: 16 degrees).

When the evaluation section of the heating parameter is set by the user, as described later, the derivation time of the optimal parameter can be reduced.

As another example, when the evaluation range determining unit 232 receives a restriction condition of the evaluation range for the minimum P band (eg, evaluation range: 3 to 8) from the user, the evaluation range determination unit 232 is configured according to the restriction condition of the evaluation range for the minimum P band. The evaluation range for the minimum P band can be changed.

In this way, by receiving a restriction condition from the user, it is possible to reduce the derivation time of the optimal parameter setter, and also to determine a parameter value desired by the user for a specific heating parameter (target heating set temperature and minimum P band in FIG. 3B). Can be derived.

Referring to FIG. 3C, the parameter set determination unit 234 may determine a plurality of input parameter sets 340 by combining parameter values corresponding to each of a plurality of sections of the plurality of heating parameters 300. The parameter set determination unit 234 may extract a plurality of possible input parameter sets 340 by combining all parameter values corresponding to each evaluation section for each of the plurality of parameters 300.

For example, the parameter set determination unit 224 is a primary input parameter set (maximum heating tube temperature, minimum heating tube temperature, target heating setting temperature, maximum P band, minimum P band, integral temperature, integral opening value) = (18 degrees, 6 degrees, 24 degrees, 20, 5, 14 degrees, 19) is determined, and as the second input parameter set (maximum heating tube temperature, minimum heating tube temperature, target heating setting temperature, maximum P band , Minimum P band, integral temperature, integral opening value) = (20 degrees, 6 degrees, 20 degrees, 18, 4, 12 degrees, 17) can be determined. The parameter set determining unit 234 may sequentially determine each input parameter set 340 determined through a greenhouse model to determine the optimal parameter set 350. The parameter set determiner 234 may determine an optimal parameter set 350 for each heating driver based on each input parameter set 340.

At this time, the evaluation section of the at least one heating parameter may be set as a specific section according to the restriction condition input from the user. In this case, the parameter set determination unit 234 performs simulation on a specific section for the evaluation section of the heating parameter, and accordingly can significantly shorten the simulation time.

2, the parameter transmission unit 240 may transmit heating parameters to the greenhouse control system 111 of the greenhouse. The parameter transmission unit 240 may transmit heating parameters for each heating driver to the greenhouse control system 111.

On the other hand, those skilled in the art, greenhouse data collection unit 200, greenhouse modeling unit 210, condition receiving unit 220, heating parameter extraction unit 230, evaluation range determining unit 232, parameter set determining unit 234 and It will be fully understood that each of the parameter transmission units 240 may be implemented separately, or one or more of them may be integrated and implemented.

4 is a flowchart illustrating a method of calculating heating parameters in the heating parameter determination server 100 according to an embodiment of the present invention.

The method for calculating the heating parameters according to the embodiment shown in FIG. 4 includes the steps of time-series processing in the heating parameter determination server 100 and the plurality of greenhouse control systems 110 according to the embodiments shown in FIGS. 1 to 3B. Includes. Therefore, even if omitted, the descriptions of the heating parameter determination server 100 and the plurality of greenhouse control systems 110 of FIGS. 1 to 3B are also provided for the heating parameter calculation method according to the embodiment illustrated in FIG. 4. Can be applied.

Referring to FIG. 4, in step S401, the heating parameter determination server 100 may collect greenhouse data including setting information, environmental information, and driver information for heating from the greenhouse control system 111 of the greenhouse.

In step S403, the heating parameter determination server 100 may generate a greenhouse model using an artificial neural network using greenhouse data collected from the greenhouse control system 111 as input.

In step S405, the heating parameter determination server 100 may receive a limit condition of a plurality of heating parameters from the greenhouse control system 111. Here, the heating parameter may include, for example, a maximum heating tube temperature and a minimum heating tube temperature. The heating parameter may further include at least one of a target heating set temperature, a maximum P band, a minimum P band, an integral temperature, and an integral opening value, for example. Here, the limitation conditions of the plurality of heating parameters may include at least one of conditions for an evaluation range of each of the plurality of heating parameters and conditions for a section of the evaluation range.

In step S407, the heating parameter determination server 100 may derive the heating parameter from the greenhouse model based on the restriction condition of the heating parameter.

Although not shown in FIG. 4, in S407, the heating parameter determination server 100 may derive heating parameters based on the global search method.

Although not illustrated in FIG. 4, in S407, the heating parameter determination server 100 may determine the evaluation range of each of the plurality of heating parameters and divide the evaluation range into a plurality of sections. The heating parameter determination server 100 determines a plurality of input parameter sets by combining parameter values corresponding to each of a plurality of sections of the plurality of heating parameters, and sequentially simulates the input parameter set through a greenhouse model to optimize the optimal parameter set. Can decide.

Although not shown in FIG. 4, after S407, the heating parameter determination server 100 may transmit heating parameters to the greenhouse control system 111.

5 is a block diagram of the greenhouse control system 111 shown in FIG. 1 according to an embodiment of the present invention.

Referring to FIG. 5, the greenhouse control system 111 may include a sensor unit 500, a transmission unit 510, a reception unit 520, and a control unit 530. However, the greenhouse control system 111 shown in FIG. 5 is only one implementation example of the present invention, and various modifications are possible based on the components shown in FIG. 5.

The sensor unit 500 may monitor environmental information of the greenhouse and driver information for heating. Here, the environmental information includes the internal temperature of the greenhouse, the supply temperature of the heating pipe, and the recovery temperature of the heating pipe, and the heating driver information may include the installation location information of each heating driver in the greenhouse and the driving history information of each heating driver. have.

The heating driver may include at least one of a skylight, a horizontal curtain, a 3Way valve, and a circulation pump, for example. For example, the sensor unit 500 monitors the internal temperature of the greenhouse through the temperature sensor installed in the greenhouse control system 111, and monitors the supply temperature and the recovery temperature of the heating tube through the temperature sensor installed in the heating pipe of the greenhouse. can do. In addition, the sensor unit 500 may monitor driving history information of the heating driver through a driving sensor installed in each heating driver.

The transmission unit 510 may transmit greenhouse data including setting information, environmental information, and heating driver information to the heating parameter determination server 100. Here, the setting information may include a target heating temperature with respect to the temperature inside the greenhouse.

The transmission unit 510 may transmit the restriction conditions of the heating parameters to the heating parameter determination server 100. Here, the heating parameter may be a value used to calculate a control parameter for driving the heating driver installed in the greenhouse.

Such heating parameters may include, for example, maximum heating tube temperature and minimum heating tube temperature. In addition, the heating parameter may include at least one of a target heating set temperature, a maximum P band, a minimum P band, an integral temperature, and an integral opening value. Here, the limiting condition of the heating parameter may include at least one of a condition for an evaluation range of each of a plurality of heating parameters required to determine an optimal heating parameter and a condition for a section of the evaluation range.

For example, the transmission unit 510 may transmit a limiting condition for each of the integral temperature and the integral opening value to the heating parameter determination server 100 in order to reduce a minute error between the internal temperature of the greenhouse and the target heating set temperature.

The reception unit 520 may receive heating parameters for each heating driver determined by the heating parameter determination server 100 from the heating parameter determination server 100. Here, the heating parameter may include a plurality of parameter values for calculating a control parameter for a heating driver for a greenhouse, and may be derived based on a restriction condition of a heating parameter from a greenhouse model using an artificial neural network using greenhouse data as input. have.

The controller 530 may calculate the PI control logic for driving the heating driver based on the heating parameter, and control the valve of the heating driver based on the calculated PI control logic. Specifically, the controller 530 calculates control parameters (including a proportional gain (K _p ) and an integral gain (K _pi )) of PI control logic for driving each heating driver based on heating parameters for each heating driver, respectively , It is possible to control each driver for the diffuser based on the calculated control parameters.

At this time, the control parameter may have a different parameter value for each heating driver. The proportional gain (K _p ) and the integral gain (K _pi ) calculated for each heating driver may mean a control value of the degree of opening / closing for the valve of each heating driver. The proportional gain (K _p ) and the integral gain (K _pi ) may have characteristics of dynamic parameters that vary depending on greenhouse conditions. Here, the proportional gain (K _p ) means the P band value, and the integral gain (K _pi ) is the integral used to reach the target heating set temperature after the internal temperature of the greenhouse after proportional control through the proportional gain (K _p ) It can mean the temperature and integral opening value.

For example, the control unit 530 may calculate a control parameter for driving control of the 3Way valve based on the heating parameter for the 3Way valve, and control the 3Way valve based on the calculated control parameter.

For a while, the control method of the heating driver will be described with reference to FIGS. 6A to 6E.

Referring to FIG. 6A, the P band in the heating control is equal to the excess from the target retardation setting temperature in the opening or closing of the heating driver (for example, a 3Way valve) according to the relationship between the internal temperature of the greenhouse and the target heating setting temperature. It means the range showing the corresponding temperature.

For example, suppose the internal temperature difference 600 between the internal temperature of the greenhouse and the target heating set temperature is 10. When the first P band value 61 is smaller than a preset P band value (for example, 10), the valve of the heating actuator is controlled to open at a ratio 602 corresponding to 100%, and the second P band value ( When 63) is greater than a preset P band value (for example, 10), the determination of the P band value is very important because the valve of the heating actuator needs to be opened at a ratio 604 corresponding to 60%.

Referring to FIG. 6B, the control unit 530 may determine the P band value by the heating tube temperature (maximum heating tube temperature and minimum heating tube temperature). That is, the control unit 530 may control the valve of the heating driver by setting the P band value small when the temperature of the heating tube is low and setting the P band value high when the temperature of the heating tube is high.

Referring to FIG. 6C, the control unit 530 may determine the P band value by the difference between the supply temperature and the recovery temperature of the heating tube.

That is, the control unit 530 sets the P band value smaller as the difference between the supply temperature and the recovery temperature of the heating pipe is larger, and sets the P band value larger as the difference value between the supply temperature and the recovery temperature of the heating pipe is smaller. The valve of the actuator can be controlled.

As described above, the valve control value (that is, the optimum control position of the valve) of the heating actuator is determined by the P band, the target heating set temperature, the temperature inside the greenhouse, the temperature of the heating pipe, the temperature of the heating pipe, and the temperature of the heating pipe recovery. It can be derived based on [Equation 1].

[Equation 1]

Here, Valve is a valve control value of a heating actuator, heat Temp (set) is a target heating set temperature, and Inside Temp is a greenhouse internal temperature.

At this time, the P band value is determined by the difference between the supply temperature of the heating tube, the determination of the P band range according to the heating tube temperature, the supply temperature and the recovery temperature of the heating tube, and can be derived through [Equation 2].

[Equation 2]

Here, the pipe temp is the supply temperature of the heating pipe, the pband range is the P band range according to the heating pipe temperature, and the temp gap is a difference value between the supply temperature and the recovery temperature of the heating pipe.

6D and 6E, when the actuator for heating is controlled based on the proportional gain K _p having a P band value calculated through Equation 2, the deviation between the target heating set temperature and the current internal temperature of the greenhouse Can occur.

The controller 530 may control the driver for heating by using the integral gain K _pi to reduce such a deviation. At this time, the integral temperature parameter and the integral opening value parameter are used to use the integral gain. The equation for reducing the range of deviation between the target heating set temperature and the current internal temperature of the greenhouse is expressed as [Equation 3].

[Equation 3]

The deviation between the target heating set temperature and the current internal temperature of the greenhouse generated during proportional control by the integral gain (K _pi ) can be reduced through the integral gain, and the current internal temperature of the greenhouse can reach the target heating set temperature. .

In the conventional case, it has been difficult for the farmer to understand the control logic for the heating driver and to set the control parameters of the heating driver.

However, according to the present invention, it is possible to help the greenhouse operation by determining a value (heating parameter) required for calculating the control parameter of each heating driver in the heating parameter determination server 100 and providing it to the greenhouse control system 111.

Referring back to FIG. 5, the control unit 530 selects at least one heating driver from among a plurality of heating drivers based on the environment information of the greenhouse, and controls PI for driving the heating driver selected based on the environment information of the greenhouse It is possible to select the control parameter of and control the selected driver based on the selected control parameter.

The control unit 530 may determine which heating driver to operate by comparing a target heating set temperature for the greenhouse with a real greenhouse environment. In addition, the control unit 530 may set priority among a plurality of heating drivers or select which of the plurality of heating drivers to control in parallel.

On the other hand, those skilled in the art, the sensor unit 500, the transmitting unit 510, the receiving unit 520 and the control unit 530, each of which is implemented separately, or one or more of them will be fully realized that can be implemented.

7 is a flowchart illustrating a method for controlling a heating driver of a greenhouse in the greenhouse control system 111 according to an embodiment of the present invention.

The method for controlling the driver for heating according to the embodiment shown in FIG. 7 includes the steps of time-series processing in the heating parameter determination server 100 and the plurality of greenhouse control systems 110 according to the embodiments shown in FIGS. 1 to 6. Includes. Therefore, even if omitted, the descriptions of the heating parameter determination server 100 and the plurality of greenhouse control systems 110 of FIGS. 1 to 6 are also provided in the heating driver control method according to the embodiment illustrated in FIG. 7. Can be applied.

Referring to FIG. 7, in step S701, the greenhouse control system 111 may monitor environmental information of the greenhouse and driver information for heating.

In step S703, the greenhouse control system 111 may transmit greenhouse data including setting information, environmental information, and heating driver information to the heating parameter determination server 100.

In step S705, the greenhouse control system 111 may receive the heating parameter determined by the heating parameter determination server 100. Here, the heating parameter includes an optimal parameter set for calculating control parameters for each heating driver of the greenhouse, and may be derived from a greenhouse model using an artificial neural network using greenhouse data as input. Heating parameters may include, for example, maximum heating tube temperature and minimum heating tube temperature.

In step S707, the greenhouse control system 111 may calculate the PI control logic for driving the heating driver based on the received heating parameter.

In step S709, the greenhouse control system 111 may control the driver for heating based on the calculated PI control logic.

In the above description, steps S701 to S709 may be further divided into additional steps or combined into fewer steps, according to an embodiment of the present invention. In addition, some steps may be omitted if necessary, and the order between the steps may be changed.

One embodiment of the present invention may also be implemented in the form of a recording medium including instructions executable by a computer, such as program modules, being executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, computer readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism, and includes any information delivery media.

The above description of the present invention is for illustration only, and those skilled in the art to which the present invention pertains can understand that it can be easily modified to other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be interpreted to be included in the scope of the present invention. .

100: heating parameter determination server
110: multiple greenhouse control system
200: greenhouse data collection department
210: greenhouse modeling department
220: condition receiving unit
230: heating parameter extraction unit
232: evaluation range determining unit
234: parameter set determination unit
240: parameter transmission unit
500: sensor unit
510: transmission unit
520: receiver
530: control unit

Claims (18)

In the server for determining the heating parameters for calculating the control parameters of the PI (Proportional-Integral) control logic for driving the driver for heating the greenhouse,
A greenhouse data collection unit that collects greenhouse data including setting information, environmental information, and heating driver information from the greenhouse control system of the greenhouse;
A greenhouse modeling unit generating a greenhouse model using an artificial neural network inputting the greenhouse data;
A condition receiving unit for receiving a limit condition of the heating parameter from the greenhouse control system of the greenhouse; And
A heating parameter extraction unit deriving the heating parameter from the greenhouse model based on the restriction condition
Including,
The heating parameter is to include the maximum heating tube temperature and the minimum heating tube temperature, heating parameter determination server.
According to claim 1,
The heating parameter further includes at least one of a target heating set temperature, a maximum P band, a minimum P band, an integral temperature, and an integral opening value.
According to claim 1,
Further comprising a parameter transmission unit for transmitting the heating parameters to the greenhouse control system of the greenhouse, heating parameter determination server.
According to claim 1,
The greenhouse heating driver includes at least one of a skylight, a horizontal curtain, a 3Way valve, and a circulation pump, a heating parameter determination server.
According to claim 1,
The setting information includes a target heating temperature,
The environment information includes the internal temperature of the greenhouse, the supply temperature of the heating pipe of the greenhouse, and the recovery temperature of the heating pipe of the greenhouse, a heating parameter determination server.
According to claim 1,
The heating driver information includes driving history information of at least one of a skylight, a horizontal curtain, a 3Way valve, and a circulation pump.
According to claim 1,
The greenhouse model using the artificial neural network is based on the Levenberg-Marquardt backpropagation algorithm, a heating parameter determination server.
According to claim 1,
The control parameter of the PI control logic includes a proportional gain (Kp) and an integral gain (Kpi), a heating parameter determination server.
According to claim 1,
The heating parameter extracting unit derives the heating parameter based on the global search method, the heating parameter determination server.
The method of claim 9,
The heating parameter extraction unit
An evaluation range determining unit which determines an evaluation range of each of the plurality of heating parameters and divides the evaluation range into a plurality of sections; And
Parameters for determining a plurality of input parameter sets by combining parameter values corresponding to each of the plurality of sections of the plurality of heating parameters, and sequentially determining each input parameter set through the greenhouse model to determine an optimal parameter set Set decision
It includes, heating parameter determination server.
The method of claim 10,
The limiting condition includes at least one of a condition for an evaluation range of each of the plurality of heating parameters and a condition for a section of the evaluation range, a heating parameter determination server.
In the greenhouse control system,
A sensor unit for monitoring environmental information of the greenhouse and driver information for heating;
A transmission unit that transmits greenhouse data including setting information, the environmental information, and the heating driver information to a heating parameter determination server;
A receiving unit that receives the heating parameter determined by the heating parameter determination server from the heating parameter determination server;
Comprising a control unit for calculating a PI (Proportional-Integral) control logic for driving the heating driver based on the heating parameter, and controlling the heating driver based on the PI control logic,
The heating parameter is to include the maximum heating tube temperature and the minimum heating tube temperature, greenhouse control system.
In the method for calculating the control parameters of the PI (Proportional-Integral) control logic for driving the heating driver of the greenhouse in the heating parameter determination server,
Collecting greenhouse data including setting information, environmental information, and heating driver information from the greenhouse control system of the greenhouse;
Generating a greenhouse model using an artificial neural network using the greenhouse data as input;
Receiving a restriction condition of the heating parameter from the greenhouse control system of the greenhouse; And
Deriving the heating parameter from the greenhouse model based on the limiting condition
Including,
The heating parameter is to include the maximum heating tube temperature and the minimum heating tube temperature, heating parameter calculation method.
The method of claim 13,
The step of deriving the heating parameter is
The heating parameter calculation method is to derive the heating parameter based on the global search method.
The method of claim 14,
The step of deriving the heating parameter is
Determining an evaluation range of each of the plurality of heating parameters, and dividing the evaluation range into a plurality of sections;
Determining a plurality of input parameter sets by combining parameter values corresponding to each of the plurality of sections of the plurality of heating parameters; And
And sequentially determining each input parameter set through the greenhouse model to determine an optimal parameter set.
The method of claim 15,
The limiting condition includes at least one of a condition for an evaluation range of each of the plurality of heating parameters and a condition for a section of the evaluation range.
The method of claim 13,
And transmitting the heating parameter to the greenhouse control system of the greenhouse.
The method of claim 13,
The greenhouse model using the artificial neural network is based on the Leverenberg-Marquardt backpropagation algorithm, a method for calculating heating parameters.

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