CN117519349A - Greenhouse control method and system - Google Patents
Greenhouse control method and system Download PDFInfo
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- CN117519349A CN117519349A CN202311671261.1A CN202311671261A CN117519349A CN 117519349 A CN117519349 A CN 117519349A CN 202311671261 A CN202311671261 A CN 202311671261A CN 117519349 A CN117519349 A CN 117519349A
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- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 24
- 238000003973 irrigation Methods 0.000 claims abstract description 22
- 230000002262 irrigation Effects 0.000 claims abstract description 22
- 238000012549 training Methods 0.000 claims description 39
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 31
- 238000003062 neural network model Methods 0.000 claims description 28
- 238000010191 image analysis Methods 0.000 claims description 11
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- 238000012545 processing Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 238000003709 image segmentation Methods 0.000 description 2
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/25—Greenhouse technology, e.g. cooling systems therefor
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Abstract
The utility model discloses a greenhouse control method and a system, wherein the method comprises the following steps: acquiring image sensing data, temperature and humidity sensing data and rainwater collecting data of a target greenhouse area; according to the temperature and humidity sensing data and the rainwater collection data, determining historical weather information and current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule; according to the image sensing data, determining the crop growth condition corresponding to each subarea in the target greenhouse area based on a preset crop growth analysis data rule; and determining greenhouse control instructions corresponding to the target greenhouse area according to the historical weather information, the current weather information and the crop growth condition so as to control the irrigation equipment and the sunshade equipment in the target greenhouse area to work. Therefore, the intelligent greenhouse control system can realize more intelligent and accurate greenhouse control, and improve the working efficiency of the greenhouse and the cultivation effect of crops.
Description
Technical Field
The utility model relates to the technical field of data control, in particular to a greenhouse control method and system.
Background
The greenhouse cultivation technology is mature and advanced, more cultivation merchants cultivate or cultivate crops which are out of season or are not suitable for local climate by using the greenhouse technology, the intelligent degree of the greenhouse is higher, various controllable components such as a sunshade device or an irrigation device can be arranged, various control modes are arranged, for example, the utility model patent with the patent number of CN202222476217.2 which is developed in the past by the applicant discloses a sunshade structure of the greenhouse, the utility model patent with the patent number of CN202321101376.2 which is developed in the past by the applicant discloses an irrigation device of the greenhouse, and the development results greatly improve the intelligent degree of the greenhouse.
However, when the intelligent control of the greenhouse is realized in the prior art, the weather and crop growth conditions of the greenhouse are not fully analyzed by fully utilizing the algorithm technology and the sensing data, so that the control accuracy is improved, and therefore, the intelligent degree of the greenhouse control technology realized in the prior art is obviously lacking. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The technical problem to be solved by the utility model is to provide a greenhouse control method and a greenhouse control system, which can realize more intelligent and accurate greenhouse control and improve the working efficiency of the greenhouse and the cultivation effect of crops.
In order to solve the technical problems, the first aspect of the utility model discloses a greenhouse control method, which comprises the following steps:
acquiring image sensing data, temperature and humidity sensing data and rainwater collecting data of a target greenhouse area;
according to the temperature and humidity sensing data and the rainwater collection data, determining historical weather information and current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule;
according to the image sensing data, determining the crop growth condition corresponding to each subarea in the target greenhouse area based on a preset crop growth analysis data rule;
determining greenhouse control instructions corresponding to the target greenhouse region according to the historical weather information, the current weather information and the crop growth condition; the greenhouse control instruction is used for controlling the irrigation equipment and the sunshade equipment in the target greenhouse area to work.
As an optional implementation manner, in the first aspect of the present utility model, the rainwater collection data includes water flow data of a plurality of historical time periods corresponding to a plurality of rainwater diversion pipes; the temperature and humidity data comprise temperature and humidity data of the target greenhouse area in a plurality of historical time periods.
In an optional implementation manner, in a first aspect of the present utility model, the determining, according to the temperature and humidity sensing data and the rainwater collecting data, the historical weather information and the current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule includes:
inputting water flow data of each historical time period corresponding to the plurality of rainwater diversion pipes and temperature and humidity data of the target greenhouse area in the corresponding historical time period into a trained weather prediction neural network model to obtain historical weather information of the target greenhouse area in each historical time period; the weather prediction neural network model is obtained through training a training data set comprising a plurality of training water flow data, training temperature and humidity data and corresponding weather marks;
determining historical weather information corresponding to the target greenhouse area in a last historical time period as current weather information corresponding to the target greenhouse area; the historical weather information and the current weather information are full clear, mostly clear, balanced in sunny and rainy days, mostly rainy days or full rainy days.
As an optional implementation manner, in the first aspect of the present utility model, the image sensing data includes a plurality of image sensing data of a plurality of positions of the target greenhouse area in a plurality of historical time periods.
In an optional implementation manner, in a first aspect of the present utility model, the determining, according to the image sensing data, a crop growth condition corresponding to each sub-area in the target greenhouse area based on a preset crop growth analysis data rule includes:
determining a subarea of the target greenhouse area corresponding to each image sensing data according to the acquisition position corresponding to each image sensing data;
and for each subarea, inputting all the image sensing data corresponding to the subarea into a trained image analysis algorithm model to obtain the crop growth condition corresponding to the subarea.
As an optional implementation manner, in the first aspect of the present utility model, the image analysis algorithm model includes a crop identification algorithm model and a crop growth condition prediction neural network model; the crop identification algorithm model is used for dividing and extracting crop images in each image sensing data; the crop growth condition prediction neural network model is used for predicting the crop growth condition corresponding to each crop image; the crop growth condition prediction neural network model is obtained through training of a training data set comprising a plurality of training crop images and corresponding growth condition labels.
In an optional implementation manner, in a first aspect of the present utility model, the determining, according to the historical weather information, the current weather information, and the crop growth situation, a greenhouse control instruction corresponding to the target greenhouse area includes:
judging whether the crop water shortage condition or the crop sunshine shortage condition exists in the subarea or not based on a preset data judgment rule according to the historical weather information and the crop growth condition of any subarea;
when judging that crops lack water in the subarea exist, determining a control instruction of irrigation equipment of the target greenhouse area as irrigation of the subarea;
when the condition that crops lack of sunlight exists in the subarea is judged, judging whether the current weather information accords with a preset sufficient sunlight weather condition, if so, determining that a control instruction of the sunshade equipment of the target greenhouse area is an opening part area so that the subarea obtains sunlight.
The second aspect of the utility model discloses a greenhouse control system, comprising:
the acquisition module is used for acquiring image sensing data, temperature and humidity sensing data and rainwater collection data of the target greenhouse area;
the first determining module is used for determining historical weather information and current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule according to the temperature and humidity sensing data and the rainwater collection data;
the second determining module is used for determining the crop growth condition corresponding to each subarea in the target greenhouse area based on a preset crop growth analysis data rule according to the image sensing data;
the third determining module is used for determining greenhouse control instructions corresponding to the target greenhouse region according to the historical weather information, the current weather information and the crop growth condition; the greenhouse control instruction is used for controlling the irrigation equipment and the sunshade equipment in the target greenhouse area to work.
As an optional implementation manner, in the second aspect of the present utility model, the rainwater collection data includes water flow data of a plurality of historical time periods corresponding to a plurality of rainwater diversion pipes; the temperature and humidity data comprise temperature and humidity data of the target greenhouse area in a plurality of historical time periods.
In a second aspect of the present utility model, the determining, by the first determining module, the specific manner of determining, according to the temperature and humidity sensing data and the rainwater collection data, the historical weather information and the current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule includes:
inputting water flow data of each historical time period corresponding to the plurality of rainwater diversion pipes and temperature and humidity data of the target greenhouse area in the corresponding historical time period into a trained weather prediction neural network model to obtain historical weather information of the target greenhouse area in each historical time period; the weather prediction neural network model is obtained through training a training data set comprising a plurality of training water flow data, training temperature and humidity data and corresponding weather marks;
determining historical weather information corresponding to the target greenhouse area in a last historical time period as current weather information corresponding to the target greenhouse area; the historical weather information and the current weather information are full clear, mostly clear, balanced in sunny and rainy days, mostly rainy days or full rainy days.
As an alternative embodiment, in the second aspect of the present utility model, the image sensing data includes a plurality of image sensing data of the target greenhouse area at a plurality of positions of a plurality of history periods.
In a second aspect of the present utility model, the determining, based on the image sensing data and the preset crop growth analysis data rule, a specific manner of determining the crop growth condition corresponding to each sub-region in the target greenhouse region includes:
determining a subarea of the target greenhouse area corresponding to each image sensing data according to the acquisition position corresponding to each image sensing data;
and for each subarea, inputting all the image sensing data corresponding to the subarea into a trained image analysis algorithm model to obtain the crop growth condition corresponding to the subarea.
As an optional implementation manner, in the second aspect of the present utility model, the image analysis algorithm model includes a crop identification algorithm model and a crop growth condition prediction neural network model; the crop identification algorithm model is used for dividing and extracting crop images in each image sensing data; the crop growth condition prediction neural network model is used for predicting the crop growth condition corresponding to each crop image; the crop growth condition prediction neural network model is obtained through training of a training data set comprising a plurality of training crop images and corresponding growth condition labels.
In a second aspect of the present utility model, the determining, by the third determining module, a specific manner of determining, according to the historical weather information, the current weather information, and the crop growth condition, a greenhouse control instruction corresponding to the target greenhouse area includes:
judging whether the crop water shortage condition or the crop sunshine shortage condition exists in the subarea or not based on a preset data judgment rule according to the historical weather information and the crop growth condition of any subarea;
when judging that crops lack water in the subarea exist, determining a control instruction of irrigation equipment of the target greenhouse area as irrigation of the subarea;
when the condition that crops lack of sunlight exists in the subarea is judged, judging whether the current weather information accords with a preset sufficient sunlight weather condition, if so, determining that a control instruction of the sunshade equipment of the target greenhouse area is an opening part area so that the subarea obtains sunlight.
In a third aspect, the utility model discloses another control system for a greenhouse, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute part or all of the steps in the greenhouse control method disclosed in the first aspect of the utility model.
A fourth aspect of the utility model discloses a computer storage medium storing computer instructions which, when invoked, are used to perform part or all of the steps of the greenhouse control method disclosed in the first aspect of the utility model.
Compared with the prior art, the utility model has the following beneficial effects:
according to the utility model, the weather and crop growth conditions of the greenhouse area can be analyzed according to various sensing data, and the control instruction of the greenhouse is determined based on the weather and crop growth conditions, so that more intelligent and accurate greenhouse control can be realized, and the working efficiency of the greenhouse and the cultivation effect of crops are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present utility model, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present utility model, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a greenhouse control method disclosed in the embodiment of the utility model;
FIG. 2 is a schematic diagram of a control system for a greenhouse according to an embodiment of the present utility model;
FIG. 3 is a schematic diagram of another greenhouse control system according to an embodiment of the present utility model.
Detailed Description
In order that those skilled in the art will better understand the present utility model, a technical solution in the embodiments of the present utility model will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present utility model, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the utility model without making any inventive effort, are intended to be within the scope of the utility model.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the utility model. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The utility model discloses a greenhouse control method and a greenhouse control system, which can analyze weather and crop growth conditions in a greenhouse area according to various sensing data and determine a control instruction of the greenhouse based on the weather and crop growth conditions, so that more intelligent and accurate greenhouse control can be realized, and the working efficiency of the greenhouse and the cultivation effect of crops are improved. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a greenhouse control method according to an embodiment of the utility model. The method described in fig. 1 may be applied to a corresponding data processing device, a data processing terminal, and a data processing server, where the server may be a local server or a cloud server, and the embodiment of the present utility model is not limited to the method described in fig. 1, and the method for controlling a greenhouse may include the following operations:
101. and acquiring image sensing data, temperature and humidity sensing data and rainwater collecting data of the target greenhouse area.
Optionally, the rainwater collection data includes water flow data of a plurality of historical time periods corresponding to the plurality of rainwater diversion pipes. Optionally, a water flow monitor may be disposed on a plurality of rainwater flow conduits of an irrigation device of a greenhouse to obtain a plurality of water flow data, and more specifically, the rainwater flow conduits are used for collecting rainwater falling on a greenhouse roof or surrounding areas to form a water source for irrigation.
Optionally, the temperature and humidity data includes temperature and humidity data of the target greenhouse area in a plurality of historical time periods. Optionally, a plurality of temperature and humidity sensors may be disposed on different sub-areas of the target greenhouse area on average.
102. And according to the temperature and humidity sensing data and the rainwater collecting data, determining the historical weather information and the current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule.
103. And determining the crop growth condition corresponding to each subarea in the target greenhouse area based on a preset crop growth analysis data rule according to the image sensing data.
104. And determining greenhouse control instructions corresponding to the target greenhouse region according to the historical weather information, the current weather information and the crop growth condition.
Specifically, the greenhouse control instruction is used for controlling the work of irrigation equipment and sunshade equipment in the target greenhouse area.
Therefore, the method described by the embodiment of the utility model can analyze the weather and crop growth conditions of the greenhouse area according to various sensing data, and determine the control instruction of the greenhouse based on the weather and crop growth conditions, so that more intelligent and accurate greenhouse control can be realized, and the working efficiency of the greenhouse and the cultivation effect of crops are improved.
As an optional embodiment, in the step, according to the temperature and humidity sensing data and the rainwater collecting data, determining the historical weather information and the current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule includes:
the water flow data of each historical time period corresponding to the plurality of rainwater diversion pipes and the temperature and humidity data of the target greenhouse area in the corresponding historical time period are input into a trained weather prediction neural network model to obtain historical weather information corresponding to the target greenhouse area in each historical time period; the weather prediction neural network model is obtained through training a training data set comprising a plurality of training water flow data, training temperature and humidity data and corresponding weather marks;
determining historical weather information corresponding to the target greenhouse area in the last historical time period as current weather information corresponding to the target greenhouse area; the historical weather information and the current weather information are full clear, most clear, balanced in sunny and rainy days, most rainy days or full rainy days.
Through the embodiment, the historical weather information and the current weather information corresponding to the target greenhouse region can be determined through the weather prediction neural network model, so that the weather information is accurately analyzed, the control instruction of the greenhouse is conveniently determined based on the weather information, more intelligent and accurate greenhouse control is realized, and the working efficiency of the greenhouse and the cultivation effect of crops are improved.
As an alternative embodiment, the image sensing data includes a plurality of image sensing data for a plurality of locations of the target greenhouse area over a plurality of historical time periods. Specifically, a plurality of image sensors may be disposed in each sub-area of the target greenhouse area to sufficiently acquire the crop image of the sub-area.
As an optional embodiment, in the step, according to the image sensing data, determining, based on a preset crop growth analysis data rule, a crop growth condition corresponding to each sub-area in the target greenhouse area includes:
determining a subarea of a target greenhouse area corresponding to each image sensing data according to the acquisition position corresponding to each image sensing data;
and for each subarea, inputting all image sensing data corresponding to the subarea into a trained image analysis algorithm model to obtain the crop growth condition corresponding to the subarea.
Through the embodiment, the crop growth condition corresponding to each subarea of the target greenhouse area can be determined through the image analysis algorithm model and the image sensing data of each subarea, so that the crop growth condition is accurately analyzed, a control instruction of the greenhouse is conveniently and subsequently determined based on the crop growth condition, more intelligent and accurate greenhouse control is realized, and the working efficiency of the greenhouse and the cultivation effect of crops are improved.
As an alternative embodiment, the image analysis algorithm model comprises a crop identification algorithm model and a crop growth condition prediction neural network model; the crop identification algorithm model is used for dividing and extracting crop images in each image sensing data; the crop growth condition prediction neural network model is used for predicting the crop growth condition corresponding to each crop image; the crop growth condition prediction neural network model is obtained through training of a training data set comprising a plurality of training crop images and corresponding growth condition labels.
Alternatively, the crop identification algorithm model may be a combination or collocation of a series of image processing algorithms, for example, a plurality of image segmentation algorithms and corresponding error assessment models may be included to correct the segmentation result, and in a specific embodiment, the image segmentation of the crop is implemented by adopting a collocation of ExG (excess green index) algorithm and auto-threshold algorithm.
Through the embodiment, the crop image corresponding to each subarea of the target greenhouse area can be segmented and the crop growth condition can be analyzed through the crop identification algorithm model and the crop growth condition prediction neural network model, so that the crop growth condition can be accurately analyzed, the control instruction of the greenhouse can be conveniently and subsequently determined based on the crop growth condition, more intelligent and accurate greenhouse control can be realized, and the working efficiency of the greenhouse and the cultivation effect of crops can be improved.
As an optional embodiment, in the step, determining a greenhouse control instruction corresponding to the target greenhouse area according to the historical weather information, the current weather information and the crop growth condition includes:
judging whether the crop water shortage condition or the crop sun shortage condition exists in any subarea or not according to the historical weather information and the crop growth condition of the subarea based on a preset data judgment rule;
when judging that crops lack water in the subareas exist, determining that the control instruction of the irrigation equipment in the target greenhouse area is to irrigate the subareas;
when the condition that crops lack sunlight exists in the subarea is judged, judging whether the current weather information accords with the preset sufficient sunlight weather condition, if so, determining that the control instruction of the sunshade equipment of the target greenhouse area is to open a partial area so that the subarea obtains sunlight.
Specifically, the preset data judging rule may be a specific numerical rule, for example, the data judging rule is determined to be a crop water shortage condition after the whole sunny weather for a preset amount of time and when the crop growth condition of a specific subarea is poor, and the data judging rule may also be a data prediction model, for example, a neural network model, which is obtained by training a training data set including a plurality of training weather information and training crop growth conditions and corresponding crop water shortage marks or crop sunlight shortage marks.
Specifically, the irrigation equipment of the target greenhouse area can comprise a water pipe leading to each subarea and a corresponding control valve, and after determining that the specific subarea needs to be irrigated according to the steps, the irrigation equipment sends an opening instruction to the corresponding control valve so as to realize targeted irrigation.
Specifically, the sunshade device of the target greenhouse area can be controlled to be partially opened, and in a specific embodiment, the sunshade device is realized as a sunshade ceiling comprising a plurality of controllable light areas, each controllable light area corresponds to a specific subarea, and after determining that the specific subarea needs to be subjected to sunlight according to the steps, the corresponding controllable light area is opened. In particular, the controllable light region may be a shutter-like structure that can be controlled to open or close.
More specifically, the sub-areas corresponding to different controllable light areas are related to the sunlight direction, and in a specific embodiment, the corresponding relationship between the sub-areas and the different controllable light areas corresponding to different sunlight time or sunlight direction is established, so as to realize more accurate control.
Through the embodiment, the crop condition of each subarea and the corresponding greenhouse control instruction can be determined according to the historical weather information, the current weather information and the crop growth condition, so that more intelligent and accurate greenhouse control can be realized, and the working efficiency of the greenhouse and the cultivation effect of crops are improved.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a greenhouse control system according to an embodiment of the utility model. The system described in fig. 2 may be applied to a corresponding data processing device, a data processing terminal, and a data processing server, where the server may be a local server or a cloud server, and embodiments of the present utility model are not limited. As shown in fig. 2, the system may include:
the acquisition module 201 is used for acquiring image sensing data, temperature and humidity sensing data and rainwater collection data of a target greenhouse area;
the first determining module 202 is configured to determine, according to the temperature and humidity sensing data and the rainwater collection data, historical weather information and current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule;
the second determining module 203 is configured to determine, according to the image sensing data, a crop growth condition corresponding to each sub-region in the target greenhouse region based on a preset crop growth analysis data rule;
the third determining module 204 is configured to determine a greenhouse control instruction corresponding to the target greenhouse area according to the historical weather information, the current weather information and the crop growth condition; the greenhouse control instruction is used for controlling the work of irrigation equipment and sunshade equipment in the target greenhouse area.
As an alternative embodiment, the rainwater collection data includes a plurality of historical time period water flow data corresponding to a plurality of rainwater diversion pipes; the temperature and humidity data comprise temperature and humidity data of the target greenhouse area in a plurality of historical time periods.
As an optional embodiment, the first determining module 202 determines, according to the temperature and humidity sensing data and the rainwater collection data, a specific manner of determining the historical weather information and the current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule, where the specific manner includes:
the water flow data of each historical time period corresponding to the plurality of rainwater diversion pipes and the temperature and humidity data of the target greenhouse area in the corresponding historical time period are input into a trained weather prediction neural network model to obtain historical weather information corresponding to the target greenhouse area in each historical time period; the weather prediction neural network model is obtained through training a training data set comprising a plurality of training water flow data, training temperature and humidity data and corresponding weather marks;
determining historical weather information corresponding to the target greenhouse area in the last historical time period as current weather information corresponding to the target greenhouse area; the historical weather information and the current weather information are full clear, most clear, balanced in sunny and rainy days, most rainy days or full rainy days.
As an alternative embodiment, the image sensing data includes a plurality of image sensing data for a plurality of locations of the target greenhouse area over a plurality of historical time periods.
As an optional embodiment, the second determining module 203 determines, according to the image sensing data, a specific manner of crop growth condition corresponding to each sub-area in the target greenhouse area based on a preset crop growth analysis data rule, where the specific manner includes:
determining a subarea of a target greenhouse area corresponding to each image sensing data according to the acquisition position corresponding to each image sensing data;
and for each subarea, inputting all image sensing data corresponding to the subarea into a trained image analysis algorithm model to obtain the crop growth condition corresponding to the subarea.
As an alternative embodiment, the image analysis algorithm model comprises a crop identification algorithm model and a crop growth condition prediction neural network model; the crop identification algorithm model is used for dividing and extracting crop images in each image sensing data; the crop growth condition prediction neural network model is used for predicting the crop growth condition corresponding to each crop image; the crop growth condition prediction neural network model is obtained through training of a training data set comprising a plurality of training crop images and corresponding growth condition labels.
As an optional embodiment, the third determining module 204 determines, according to the historical weather information, the current weather information, and the crop growth condition, a specific manner of the greenhouse control instruction corresponding to the target greenhouse area, where the specific manner includes:
judging whether the crop water shortage condition or the crop sun shortage condition exists in any subarea or not according to the historical weather information and the crop growth condition of the subarea based on a preset data judgment rule;
when judging that crops lack water in the subareas exist, determining that the control instruction of the irrigation equipment in the target greenhouse area is to irrigate the subareas;
when the condition that crops lack sunlight exists in the subarea is judged, judging whether the current weather information accords with the preset sufficient sunlight weather condition, if so, determining that the control instruction of the sunshade equipment of the target greenhouse area is to open a partial area so that the subarea obtains sunlight.
The details and technical effects of the modules in the embodiment of the present utility model may refer to the description in the first embodiment, and are not described herein.
Example III
Referring to fig. 3, fig. 3 is a schematic structural diagram of another greenhouse control system according to an embodiment of the present utility model. As shown in fig. 3, the system may include:
a memory 301 storing executable program code;
a processor 302 coupled with the memory 301;
the processor 302 invokes the executable program code stored in the memory 301 to perform some or all of the steps in the greenhouse control method disclosed in the first embodiment of the present utility model.
Example IV
The embodiment of the utility model discloses a computer storage medium which stores computer instructions for executing part or all of the steps in the greenhouse control method disclosed in the first embodiment of the utility model when the computer instructions are called.
The system embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present utility model without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the utility model discloses a greenhouse control method and a greenhouse control system, which are disclosed as preferred embodiments of the utility model, and are only used for illustrating the technical scheme of the utility model, but not limiting the technical scheme; although the utility model has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A greenhouse control method, the method comprising:
acquiring image sensing data, temperature and humidity sensing data and rainwater collecting data of a target greenhouse area;
according to the temperature and humidity sensing data and the rainwater collection data, determining historical weather information and current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule;
according to the image sensing data, determining the crop growth condition corresponding to each subarea in the target greenhouse area based on a preset crop growth analysis data rule;
determining greenhouse control instructions corresponding to the target greenhouse region according to the historical weather information, the current weather information and the crop growth condition; the greenhouse control instruction is used for controlling the irrigation equipment and the sunshade equipment in the target greenhouse area to work.
2. The method of claim 1, wherein the rainwater collection data comprises a plurality of historical time period water flow data corresponding to a plurality of rainwater diversion pipes; the temperature and humidity data comprise temperature and humidity data of the target greenhouse area in a plurality of historical time periods.
3. The method according to claim 2, wherein the determining the historical weather information and the current weather information corresponding to the target greenhouse area based on the preset weather analysis data rule according to the temperature and humidity sensing data and the rainwater collection data comprises:
inputting water flow data of each historical time period corresponding to the plurality of rainwater diversion pipes and temperature and humidity data of the target greenhouse area in the corresponding historical time period into a trained weather prediction neural network model to obtain historical weather information of the target greenhouse area in each historical time period; the weather prediction neural network model is obtained through training a training data set comprising a plurality of training water flow data, training temperature and humidity data and corresponding weather marks;
determining historical weather information corresponding to the target greenhouse area in a last historical time period as current weather information corresponding to the target greenhouse area; the historical weather information and the current weather information are full clear, mostly clear, balanced in sunny and rainy days, mostly rainy days or full rainy days.
4. The method of claim 3, wherein the image sensing data comprises a plurality of image sensing data for a plurality of locations of the target greenhouse area over a plurality of historical time periods.
5. The method according to claim 4, wherein the determining, based on the image sensing data and the preset crop growth analysis data rule, the crop growth condition corresponding to each sub-region in the target greenhouse region includes:
determining a subarea of the target greenhouse area corresponding to each image sensing data according to the acquisition position corresponding to each image sensing data;
and for each subarea, inputting all the image sensing data corresponding to the subarea into a trained image analysis algorithm model to obtain the crop growth condition corresponding to the subarea.
6. The method according to claim 5, wherein the image analysis algorithm model includes a crop recognition algorithm model and a crop growth condition prediction neural network model; the crop identification algorithm model is used for dividing and extracting crop images in each image sensing data; the crop growth condition prediction neural network model is used for predicting the crop growth condition corresponding to each crop image; the crop growth condition prediction neural network model is obtained through training of a training data set comprising a plurality of training crop images and corresponding growth condition labels.
7. The method according to claim 6, wherein determining the greenhouse control command corresponding to the target greenhouse area according to the historical weather information, the current weather information and the crop growth condition comprises:
judging whether the crop water shortage condition or the crop sunshine shortage condition exists in the subarea or not based on a preset data judgment rule according to the historical weather information and the crop growth condition of any subarea;
when judging that crops lack water in the subarea exist, determining a control instruction of irrigation equipment of the target greenhouse area as irrigation of the subarea;
when the condition that crops lack of sunlight exists in the subarea is judged, judging whether the current weather information accords with a preset sufficient sunlight weather condition, if so, determining that a control instruction of the sunshade equipment of the target greenhouse area is an opening part area so that the subarea obtains sunlight.
8. A greenhouse control system, the system comprising:
the acquisition module is used for acquiring image sensing data, temperature and humidity sensing data and rainwater collection data of the target greenhouse area;
the first determining module is used for determining historical weather information and current weather information corresponding to the target greenhouse area based on a preset weather analysis data rule according to the temperature and humidity sensing data and the rainwater collection data;
the second determining module is used for determining the crop growth condition corresponding to each subarea in the target greenhouse area based on a preset crop growth analysis data rule according to the image sensing data;
the third determining module is used for determining greenhouse control instructions corresponding to the target greenhouse region according to the historical weather information, the current weather information and the crop growth condition; the greenhouse control instruction is used for controlling the irrigation equipment and the sunshade equipment in the target greenhouse area to work.
9. A greenhouse control system, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the greenhouse control method of any one of claims 1-7.
10. A computer storage medium storing computer instructions which, when invoked, are adapted to perform the greenhouse control method of any one of claims 1-7.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1631098A (en) * | 2003-12-25 | 2005-06-29 | 中国农业大学 | Insufficient irrigation forecast and control method |
| JP2010081877A (en) * | 2008-09-30 | 2010-04-15 | National Institute Of Agrobiological Sciences | Closed-system cultivation greenhouse, and method for using the same |
| CN102035876A (en) * | 2010-10-15 | 2011-04-27 | 江苏鸿信***集成有限公司 | Remote monitoring and intelligent control system and method of agricultural greenhouse based on M2M framework |
| CN103886409A (en) * | 2014-03-13 | 2014-06-25 | 汕头大学 | Assistant decision making system for agricultural planting |
| CN205431373U (en) * | 2016-03-31 | 2016-08-10 | 吉林省农业机械研究院 | Water -saving irrigation system |
| CN107220903A (en) * | 2017-06-23 | 2017-09-29 | 深圳市盛路物联通讯技术有限公司 | A kind of reading intelligent agriculture management method and system |
| CN109982559A (en) * | 2016-11-02 | 2019-07-05 | 益而得技术解决方案私人有限公司 | Control agricultural producing areas |
| CN111406550A (en) * | 2020-01-21 | 2020-07-14 | 国网浙江省电力有限公司湖州供电公司 | Big-arch shelter is planted to wisdom based on thing networking |
| CN212344642U (en) * | 2020-02-26 | 2021-01-15 | 山东农业大学 | Simulation device for overcast and rainy short-exposure weather in plant growth period |
| CN112602563A (en) * | 2020-12-15 | 2021-04-06 | 珠海市现代农业发展中心(珠海市金湾区台湾农民创业园管理委员会、珠海市农渔业科研与推广中心) | Water-saving irrigation system and accurate irrigation method |
| CN113016450A (en) * | 2021-03-22 | 2021-06-25 | 北京农业智能装备技术研究中心 | Greenhouse crop irrigation method and system |
| KR102351713B1 (en) * | 2021-06-24 | 2022-01-17 | 성한 주식회사 | System for creating weather intelligence using illuminometer and method thereof |
| CN114637353A (en) * | 2022-03-24 | 2022-06-17 | 四川省水利科学研究院 | Agricultural irrigation control method, system and terminal based on multi-environment factor analysis |
| CN115686110A (en) * | 2023-01-07 | 2023-02-03 | 广州市农业科学研究院 | Greenhouse intelligent control method, system, monitoring device, equipment and medium |
-
2023
- 2023-12-06 CN CN202311671261.1A patent/CN117519349B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1631098A (en) * | 2003-12-25 | 2005-06-29 | 中国农业大学 | Insufficient irrigation forecast and control method |
| JP2010081877A (en) * | 2008-09-30 | 2010-04-15 | National Institute Of Agrobiological Sciences | Closed-system cultivation greenhouse, and method for using the same |
| CN102035876A (en) * | 2010-10-15 | 2011-04-27 | 江苏鸿信***集成有限公司 | Remote monitoring and intelligent control system and method of agricultural greenhouse based on M2M framework |
| CN103886409A (en) * | 2014-03-13 | 2014-06-25 | 汕头大学 | Assistant decision making system for agricultural planting |
| CN205431373U (en) * | 2016-03-31 | 2016-08-10 | 吉林省农业机械研究院 | Water -saving irrigation system |
| CN109982559A (en) * | 2016-11-02 | 2019-07-05 | 益而得技术解决方案私人有限公司 | Control agricultural producing areas |
| CN107220903A (en) * | 2017-06-23 | 2017-09-29 | 深圳市盛路物联通讯技术有限公司 | A kind of reading intelligent agriculture management method and system |
| CN111406550A (en) * | 2020-01-21 | 2020-07-14 | 国网浙江省电力有限公司湖州供电公司 | Big-arch shelter is planted to wisdom based on thing networking |
| CN212344642U (en) * | 2020-02-26 | 2021-01-15 | 山东农业大学 | Simulation device for overcast and rainy short-exposure weather in plant growth period |
| CN112602563A (en) * | 2020-12-15 | 2021-04-06 | 珠海市现代农业发展中心(珠海市金湾区台湾农民创业园管理委员会、珠海市农渔业科研与推广中心) | Water-saving irrigation system and accurate irrigation method |
| CN113016450A (en) * | 2021-03-22 | 2021-06-25 | 北京农业智能装备技术研究中心 | Greenhouse crop irrigation method and system |
| KR102351713B1 (en) * | 2021-06-24 | 2022-01-17 | 성한 주식회사 | System for creating weather intelligence using illuminometer and method thereof |
| CN114637353A (en) * | 2022-03-24 | 2022-06-17 | 四川省水利科学研究院 | Agricultural irrigation control method, system and terminal based on multi-environment factor analysis |
| CN115686110A (en) * | 2023-01-07 | 2023-02-03 | 广州市农业科学研究院 | Greenhouse intelligent control method, system, monitoring device, equipment and medium |
Non-Patent Citations (1)
| Title |
|---|
| 温耀华等: "基于BP神经网络的大棚作物腾发量预测模型", 中国农村水利水电, no. 02, 29 February 2008 (2008-02-29), pages 25 - 26 * |
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