CN113940218A - Intelligent heat supply method and system for greenhouse - Google Patents
Intelligent heat supply method and system for greenhouse Download PDFInfo
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
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- A—HUMAN NECESSITIES
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- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
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Abstract
The invention provides an intelligent heat supply method and system for a greenhouse. Wherein the method comprises the following steps: s10, acquiring local historical atmospheric temperature data, and establishing a daily atmospheric temperature change curve based on the historical atmospheric temperature data; s20, acquiring greenhouse body data of the greenhouse; s30, establishing a first temperature loss change curve based on the shed body data and the daily temperature change curve; s40, correspondingly establishing a heat supply curve based on the first temperature loss change curve; s50, supplying heat to the greenhouse based on the heat supply curve. The heat supply method comprehensively analyzes the heat loss curve based on the outside air temperature change curve and the heat preservation capacity of the greenhouse body, and further sets the heat supply curve in a targeted manner, so that the accurate control of the heat supply of the greenhouse is realized.
Description
Technical Field
The invention relates to the technical field of intelligent agriculture, in particular to an intelligent heat supply method and system for a greenhouse.
Background
The vigorous development of the greenhouse enriches the vegetable baskets of people, and particularly for people living in the north, the greenhouse can eat various fresh vegetables in severe cold winter. As is known, the temperature of a greenhouse is difficult to control, particularly in high latitude provinces in the northern part of China, a common greenhouse cannot provide a proper growth environment for vegetables simply by heat preservation, the temperature may suddenly drop to-40-30 ℃ in extremely cold weather such as snowstorm or continuous cloudy days, and the greenhouse heat preservation measures cannot resist severe cold, so that crops in the greenhouse cannot normally grow and even die in a large area. Some greenhouses can adopt a mode of heating by using raw fire to resist severe cold weather, and although the mode is simple, local temperature around the fire is easy to be too high, excessive carbon dioxide is generated, heat loss is large, cost is high, time and labor are consumed, and temperature is not easy to control.
Therefore, in the prior art has no technology for accurately controlling the temperature in the greenhouse, and needs to be solved urgently.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides an intelligent heat supply method, an intelligent heat supply system, electronic equipment and a storage medium for a greenhouse, so as to realize accurate control of the temperature in the greenhouse.
The invention provides an intelligent heat supply method for a greenhouse, which comprises the following steps:
s10, acquiring local historical atmospheric temperature data, and establishing a daily atmospheric temperature change curve based on the historical atmospheric temperature data;
s20, acquiring greenhouse body data of the greenhouse;
s30, establishing a first temperature loss change curve based on the shed body data and the daily temperature change curve;
s40, correspondingly establishing a heat supply curve based on the first temperature loss change curve;
s50, supplying heat to the greenhouse based on the heat supply curve.
Alternatively, the daily air temperature change profile corresponds to a plurality of consecutive days.
Optionally, in step S10, the creating a daily air temperature variation curve based on the historical air temperature data includes:
establishing a first day air temperature change curve based on the historical air temperature big data;
acquiring air temperature data of a plurality of days before the current day, and fitting the air temperature data to obtain a second day air temperature change curve;
calculating an accumulated value of a difference between the first daily air temperature change curve and the second daily air temperature change curve, and if the accumulated value is less than or equal to a first threshold value, taking the first daily air temperature change curve as the daily air temperature change curve;
and if the accumulated value is larger than a first threshold value, acquiring weather forecast data, correcting the second day air temperature change curve based on the weather forecast data to obtain a third day air temperature change curve, and taking the third day air temperature change curve as the day air temperature change curve.
Optionally, in step S10, the creating a daily air temperature variation curve based on the historical air temperature data further includes:
if the accumulated value is smaller than a first threshold value but larger than a second threshold value, establishing a fourth daily air temperature change curve based on the historical air temperature data, and taking the fourth daily air temperature change curve as the daily air temperature change curve;
wherein the fourth daily temperature variation curve is created in a larger amount than the historical temperature atmosphere data on which the first daily temperature variation curve is created.
Optionally, in step S30, creating a first temperature loss variation curve based on the greenhouse volume data and the daily temperature variation curve includes:
and inputting the shed body data and the daily air temperature change curve into a deep learning model, and outputting a first temperature loss change curve by the deep learning model.
Optionally, in step S40, the correspondingly establishing a heat supply curve based on the first temperature loss variation curve includes:
and acquiring the temperature in the greenhouse in real time, and establishing a heat supply curve based on the temperature and the first temperature loss change curve.
Optionally, before S50, the method further includes:
s40, detecting whether other greenhouses exist around the greenhouses, if so, turning to S42, otherwise, turning to S50;
s42, acquiring the temperature in other greenhouses, and if the temperature in the other greenhouses is higher than that of the greenhouses, correcting the heat supply curve by a first correction coefficient; otherwise, correcting the heat supply curve by a second correction coefficient;
wherein the first correction coefficient is smaller than the second correction coefficient.
The invention provides an intelligent heating system of a greenhouse, which comprises a processing module, a storage module and a communication module, wherein the processing module is connected with the storage module and the communication module;
the storage module is stored with a computer program;
the communication module is used for realizing the communication between the processing module and the heating equipment;
the processing module is adapted to invoke the computer program to implement the method as described above.
A third aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs a method as set forth in any one of the preceding claims.
A fourth aspect of the invention provides an electronic device comprising a processor and a memory, said memory having stored thereon a computer program which, when executed by the processor, performs the method of any of the above.
The invention has the beneficial effects that:
according to the technical scheme, a first temperature loss change curve is established based on a daily temperature change curve and greenhouse body data obtained by local historical atmospheric temperature data fitting, and then a heat supply curve can be established. The heat supply curve established by the invention determines the temperature loss data of the greenhouse based on the factors, and further correspondingly supplies heat, compared with a mode of supplying heat according to a preset value in the prior art, the heat supply curve can obviously keep the stability of the temperature in the greenhouse, and can save energy to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic flow chart of an intelligent heat supply method for a greenhouse disclosed by an embodiment of the invention;
fig. 2 is a schematic structural diagram of an intelligent heating system of a greenhouse disclosed by the embodiment of the invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. indicate an orientation or a positional relationship based on that shown in the drawings or that the product of the present invention is used as it is, this is only for convenience of description and simplification of the description, and it does not indicate or imply that the device or the element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of an intelligent heat supply method for a greenhouse disclosed in the embodiment of the present invention. As shown in fig. 1, an intelligent heat supply method for a greenhouse of an embodiment of the present invention includes:
s10, acquiring local historical atmospheric temperature data, and establishing a daily atmospheric temperature change curve based on the historical atmospheric temperature data;
s20, acquiring greenhouse body data of the greenhouse;
s30, establishing a first temperature loss change curve based on the shed body data and the daily temperature change curve;
s40, correspondingly establishing a heat supply curve based on the first temperature loss change curve;
s50, supplying heat to the greenhouse based on the heat supply curve.
In the embodiment of the invention, a daily temperature change curve is obtained by fitting based on local historical temperature data, the daily temperature change curve can represent local daily temperature change data in the current season, different external temperatures can cause different rates of outward heat dissipation of the greenhouse, and for example, the heat dissipation is more and faster when the internal and external temperature difference is larger. Meanwhile, the data of the greenhouse body of the greenhouse can directly determine the heat preservation capability and effect, for example, the heat preservation effect of the greenhouse body made of hollow materials is better than that of the greenhouse body made of common plastic film materials, and the heat preservation effect of the greenhouse body with a multi-layer structure is better than that of the greenhouse body with a single-layer structure, so that the data of the greenhouse body can also obviously influence the temperature loss rate. Therefore, after the daily temperature change curve and the shed body data are obtained, the temperature/heat loss change curve at different moments every day can be determined, and then the heating equipment can be driven to supply heat in a targeted mode based on the temperature loss change curve, so that the temperature in the shed can be maintained at the set temperature accurately.
The scheme of the invention can be realized by the client arranged in each greenhouse, and can also be realized at the server. The server provided by the embodiment of the invention can be implemented as an independent physical server, can also be a server cluster or distributed system formed by a plurality of physical servers, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; the client may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like.
Alternatively, the daily air temperature change profile corresponds to a plurality of consecutive days.
In the embodiment of the invention, because the air temperature data in one season are highly similar, each daily air temperature change curve is set to correspond to a plurality of continuous days, so that the calculation number of the daily air temperature change curves can be effectively reduced, and the calculation load is further reduced. In addition, the high similarity is mainly shown in the middle part of the season, and the similarity is obviously reduced when the temperature changes of the front part and the rear part of the season are close to the adjacent seasons, so that as an improvement, the continuous daily quantity corresponding to a single daily temperature change curve of the front part and the rear part of the season is smaller than that of the middle part of the season, so that the daily temperature change curve is more accurate, and the balance between the accuracy and the calculated quantity is realized.
Optionally, in step S10, the creating a daily air temperature variation curve based on the historical air temperature data includes:
establishing a first day air temperature change curve based on the historical air temperature big data;
acquiring air temperature data of a plurality of days before the current day, and fitting the air temperature data to obtain a second day air temperature change curve;
calculating an accumulated value of a difference between the first daily air temperature change curve and the second daily air temperature change curve, and if the accumulated value is less than or equal to a first threshold value, taking the first daily air temperature change curve as the daily air temperature change curve;
and if the accumulated value is larger than a first threshold value, acquiring weather forecast data, correcting the second day air temperature change curve based on the weather forecast data to obtain a third day air temperature change curve, and taking the third day air temperature change curve as the day air temperature change curve.
In the embodiment of the present invention, although the change in the air temperature has a certain rule, there is a case where there is a sudden change in the rule. Aiming at the problem, the difference value is accumulated and calculated based on the air temperature change curve fitted by recent air temperature data and the air temperature change curve fitted by big data, if the accumulated value is in a normal range, the recent air temperature curve is similar to the historical situation, and the first day air temperature change curve established by the big data can be directly used as the big data to establish the first day air temperature change curve; if the accumulated value is too large, the recent temperature curve does not accord with the historical situation.
The present invention further modifies the second daily air temperature profile for the abrupt change based on weather forecast data, wherein the weather forecast data may be recent air temperature forecast data obtained from a weather bureau. For the correction method, it may be: and judging whether the second daily temperature change curve conforms to the weather forecast data or not based on the change trend of the second daily temperature change curve, and if so, correcting the second daily temperature change curve by using a coefficient corresponding to the weather forecast data. For example, the weather forecast data indicates that the temperature will continuously decrease by 5 ℃ in the next two days, the trend of the second day air temperature change curve also indicates that the air temperature is in a descending trend, and the descending amplitude (which can be judged based on the slope of the trend curve) is also substantially 5 ℃, at this time, the correction coefficient can be determined based on the difference value between the temperature descending amplitude obtained by the trend curve and the temperature descending amplitude in the weather forecast data, the larger the difference value is, the larger the correction coefficient is, the correction coefficient can be any number between [0,1], and the correlation can be established in advance based on the empirical value.
Optionally, in step S10, the creating a daily air temperature variation curve based on the historical air temperature data further includes:
if the accumulated value is smaller than a first threshold value but larger than a second threshold value, establishing a fourth daily air temperature change curve based on the historical air temperature data, and taking the fourth daily air temperature change curve as the daily air temperature change curve;
wherein the fourth daily temperature variation curve is created in a larger amount than the historical temperature atmosphere data on which the first daily temperature variation curve is created.
In the embodiment of the present invention, there is also a case where the cumulative value is still larger than the second threshold value although it is also smaller than the first threshold value, which indicates that, although similar to the history case as a whole, there may be some difference in part of the curve, which may be due to insufficient fitting data amount, and the first daily air temperature change curve as the reference is not accurate enough by itself. In order to further improve the accuracy of the daily air temperature change curve, the fourth daily air temperature change curve is obtained by recalculating the fourth daily air temperature change curve by using a larger amount of big data, so that the accuracy of the first daily air temperature change curve can be further improved, and accordingly, the more accurate daily air temperature change curve can be obtained.
Optionally, in step S30, creating a first temperature loss variation curve based on the greenhouse volume data and the daily temperature variation curve includes:
and inputting the shed body data and the daily air temperature change curve into a deep learning model, and outputting a first temperature loss change curve by the deep learning model.
In the embodiment of the invention, no realistic and available correlation exists between the greenhouse data and the daily temperature change curve and the temperature loss, and a usable correlation formula is difficult to obtain by utilizing manpower research. Therefore, the invention constructs a deep learning model, the greenhouse body data, the daily temperature change curve and the temperature loss data (obtained by pre-measurement) are input into the deep learning model to train the deep learning model, the trained deep learning model can master the relationship among the greenhouse body data, the daily temperature change curve and the temperature loss, and then the deep learning model can output an accurate first temperature loss change curve after inputting the real-time greenhouse body data and the daily temperature change curve into the deep learning model.
For the construction of the Deep learning model, the Deep learning model can be constructed based on algorithms such as a Forward Neural Network (FNN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a Recurrent Neural Network (Recurrent Neural Network), a Deep Belief Network (Deep Belief Network), a Restricted Boltzmann machine (Restricted Boltzmann machine), a generation countermeasure Network (generic adaptive Network), and the like, and specific construction modes are not described in detail.
Optionally, in step S40, the correspondingly establishing a heat supply curve based on the first temperature loss variation curve includes:
and acquiring the temperature in the greenhouse in real time, and establishing a heat supply curve based on the temperature and the first temperature loss change curve.
In the embodiment of the present invention, after the first temperature loss variation curve is determined, the temperature loss amounts in different time periods can be known, and accordingly, it can be determined that the corresponding amount of temperature compensation should be provided, that is, the heat supply curve is determined. In addition, since heating requires a certain period of time, in order to further improve the stability of the temperature in the greenhouse, the heating curve is set to be advanced appropriately with respect to the first temperature loss change curve, and the degree of advancement increases as the temperature loss amount in the first temperature loss change curve increases.
Optionally, before S50, the method further includes:
s40, detecting whether other greenhouses exist around the greenhouses, if so, turning to S42, otherwise, turning to S50;
s42, acquiring the temperature in other greenhouses, and if the temperature in the other greenhouses is higher than that of the greenhouses, correcting the heat supply curve by a first correction coefficient; otherwise, correcting the heat supply curve by a second correction coefficient;
wherein the first correction coefficient is smaller than the second correction coefficient.
In the embodiment of the present invention, the greenhouses are usually arranged in a cluster, and a plurality of greenhouses will form a temperature focusing effect, which is considered by the present invention. If the temperature of the peripheral greenhouse is higher than that of the greenhouse, the greenhouse is obviously prolonged by the temperature of the peripheral greenhouse, so that the temperature loss of the greenhouse is reduced, and the heat supply curve is corrected by using a smaller first correction coefficient, namely the heat supply amount in the original heat supply curve is reduced; on the contrary, the greenhouse cannot obtain the temperature delay of the surrounding greenhouse, the temperature loss is relatively large, and the smaller second correction coefficient is used for correcting the heat supply curve, namely the heat supply amount in the original heat supply curve is reduced in a smaller range.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of an intelligent heating system of a greenhouse disclosed in the embodiment of the present invention. As shown in fig. 2, the intelligent heating system (100) for the greenhouse of the embodiment of the present invention comprises a processing module (101), a storage module (102), and a communication module (103), wherein the processing module (101) is connected to the storage module (102) and the communication module;
the storage module (102) having a computer program stored thereon;
the communication module (103) is used for realizing the communication between the processing module (101) and the heating equipment;
the processing module (101) is used for calling the computer program to realize the method according to the first embodiment.
The specific functions of the intelligent heating system for a greenhouse in this embodiment refer to the first embodiment, and since the system in this embodiment adopts all the technical solutions of the first embodiment, at least all the beneficial effects brought by the technical solutions of the first embodiment are achieved, and are not described in detail herein.
EXAMPLE III
Referring to fig. 3, fig. 3 is an electronic device according to an embodiment of the present invention, the electronic device includes:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the method according to the first embodiment.
Example four
The embodiment of the invention also discloses a computer storage medium, wherein a computer program is stored on the storage medium, and the computer program executes the method in the first embodiment when being executed by a processor.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An intelligent heat supply method for a greenhouse is characterized by comprising the following steps:
s10, acquiring local historical atmospheric temperature data, and establishing a daily atmospheric temperature change curve based on the historical atmospheric temperature data;
s20, acquiring greenhouse body data of the greenhouse;
s30, establishing a first temperature loss change curve based on the shed body data and the daily temperature change curve;
s40, correspondingly establishing a heat supply curve based on the first temperature loss change curve;
s50, supplying heat to the greenhouse based on the heat supply curve.
2. The intelligent heat supply method for the greenhouse as claimed in claim 1, wherein the intelligent heat supply method comprises the following steps: the daily air temperature change curve corresponds to a plurality of consecutive days.
3. The intelligent heat supply method for the greenhouse as claimed in claim 1, wherein the intelligent heat supply method comprises the following steps: in step S10, the creating a daily air temperature change curve based on the historical air temperature data includes:
establishing a first day air temperature change curve based on the historical air temperature big data;
acquiring air temperature data of a plurality of days before the current day, and fitting the air temperature data to obtain a second day air temperature change curve;
calculating an accumulated value of a difference between the first daily air temperature change curve and the second daily air temperature change curve, and if the accumulated value is less than or equal to a first threshold value, taking the first daily air temperature change curve as the daily air temperature change curve;
and if the accumulated value is larger than a first threshold value, acquiring weather forecast data, correcting the second day air temperature change curve based on the weather forecast data to obtain a third day air temperature change curve, and taking the third day air temperature change curve as the day air temperature change curve.
4. The intelligent heat supply method for the greenhouse as claimed in claim 3, wherein the intelligent heat supply method comprises the following steps: in step S10, the creating a daily air temperature change curve based on the historical air temperature data further includes:
if the accumulated value is smaller than a first threshold value but larger than a second threshold value, establishing a fourth daily air temperature change curve based on the historical air temperature data, and taking the fourth daily air temperature change curve as the daily air temperature change curve;
wherein the fourth daily temperature variation curve is created in a larger amount than the historical temperature atmosphere data on which the first daily temperature variation curve is created.
5. The intelligent heat supply method for the greenhouse as claimed in claim 1, wherein the intelligent heat supply method comprises the following steps: in step S30, creating a first temperature loss variation curve based on the greenhouse volume data and the daily temperature variation curve includes:
and inputting the shed body data and the daily air temperature change curve into a deep learning model, and outputting a first temperature loss change curve by the deep learning model.
6. The intelligent heat supply method for the greenhouse as claimed in any one of claims 1 to 5, wherein: in step S40, the correspondingly establishing a heat supply curve based on the first temperature loss variation curve includes:
and acquiring the temperature in the greenhouse in real time, and establishing a heat supply curve based on the temperature and the first temperature loss change curve.
7. The intelligent heat supply method for the greenhouse as claimed in claim 6, wherein the intelligent heat supply method comprises the following steps: before S50, the method further includes:
s40, detecting whether other greenhouses exist around the greenhouses, if so, turning to S42, otherwise, turning to S50;
s42, acquiring the temperature in other greenhouses, and if the temperature in the other greenhouses is higher than that of the greenhouses, correcting the heat supply curve by a first correction coefficient; otherwise, correcting the heat supply curve by a second correction coefficient;
wherein the first correction coefficient is smaller than the second correction coefficient.
8. An intelligent heating system of a greenhouse comprises a processing module, a storage module and a communication module, wherein the processing module is connected with the storage module and the communication module;
the storage module is stored with a computer program;
the communication module is used for realizing the communication between the processing module and the heating equipment;
the method is characterized in that: the processing module is for invoking the computer program to implement the method of any one of claims 1-7.
9. A storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, performs the method of any one of claims 1-7.
10. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, performs the method of any one of claims 1-7.
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| CN202111165451.7A CN113940218B (en) | 2021-09-30 | 2021-09-30 | Intelligent heat supply method and system for greenhouse |
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| CN202111165451.7A CN113940218B (en) | 2021-09-30 | 2021-09-30 | Intelligent heat supply method and system for greenhouse |
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