CN114451257A - Irrigation method and device based on neural network, storage medium and electronic equipment - Google Patents
Irrigation method and device based on neural network, storage medium and electronic equipment Download PDFInfo
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
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Abstract
The invention discloses an irrigation method based on a neural network, which comprises the following steps: respectively acquiring first data acquired by a humidity sensor, a temperature sensor and a light sensor at a first preset time interval; determining a metadata vector based on the first data; and under the condition that the metadata vector is input into a pre-established prediction model and then the irrigation is determined to be needed, controlling an irrigation system to irrigate according to the irrigation quantity output by the prediction model.
Description
Technical Field
The invention relates to the field of irrigation, in particular to an irrigation method and device based on a neural network, a storage medium and electronic equipment.
Background
Greening is also an important component of the community, and a great deal of manpower is spent annually on maintaining irrigation in order to maintain the viability of greening.
Some current intelligent irrigation systems directly use all the data collected at the same time point as one piece of data of an input layer. Even if the conditions of illumination and temperature are considered, the intelligent control model is not trained by using a machine learning method, and only one variable is controlled by using an embedded chip, so that the intelligent degree is not high, and meanwhile, the prediction on the change of weather is not sufficient.
Disclosure of Invention
In order to solve the technical problem that the existing intelligent irrigation system cannot meet intelligent irrigation, the invention provides an irrigation method based on a neural network, which comprises the following steps:
respectively acquiring first data acquired by a humidity sensor, a temperature sensor and a light sensor at a first preset time interval;
determining a metadata vector based on the first data;
determining a metadata vector based on the first data, wherein the metadata vector comprises a humidity sensor vector, a temperature sensor vector, and a light sensor vector;
and under the condition that the metadata vector is input into a pre-established prediction model and then irrigation is determined to be needed, controlling an irrigation system to irrigate according to the irrigation quantity output by the prediction model.
In some embodiments, said determining a metadata vector based on said first data comprises:
respectively determining the difference value between the humidity sensor values of every two adjacent moments in the first data of the humidity sensor to obtain first equal difference value data of the humidity sensor;
a humidity sensor vector is determined based on the first arithmetic difference data and a last moment sensor value in the first data of the humidity sensor.
In some embodiments, said determining a metadata vector based on said first data comprises:
respectively determining the difference value between the temperature sensor values of every two adjacent moments in the first data of the temperature sensors to obtain second equal-grade difference value data of the temperature sensors;
determining the temperature sensor vector based on the second equal difference data and the last moment sensor value in the first data of the temperature sensor.
In some embodiments, said determining a metadata vector based on said first data comprises:
respectively determining the difference value between the optical sensor values of every two adjacent moments in the first data of the optical sensor to obtain third equal difference value data of the optical sensor;
determining the light sensor vector based on the third isodyne data and a last moment sensor value in the first data of the light sensor.
In some embodiments, the method further comprises:
acquiring water consumption data in real time;
and controlling the irrigation system to stop irrigating when the water consumption data reaches the irrigation amount output by the prediction model.
In some embodiments, the method further comprises:
respectively acquiring second data acquired by the humidity sensor, the temperature sensor and the optical sensor at a second preset time interval;
determining a sample metadata vector based on the second data;
generating training data based on the sample metadata vector, whether irrigation is required and a mapping relationship between irrigation amounts, wherein the sample metadata vector comprises a sample humidity sensor vector, a sample temperature sensor vector and a sample light sensor vector;
the predictive model is derived based on the training data.
In some embodiments, said deriving said predictive model based on said training data comprises:
dividing the training data into a training set and a test set;
inputting the training set into a neural network model for training, and using the test set to test the accuracy of the neural network model;
and determining the neural network model as the prediction model under the condition that the accuracy of the neural network model meets the requirement.
The present invention also provides an irrigation device based on a neural network, comprising:
the acquisition module is used for respectively acquiring first data acquired by the humidity sensor, the temperature sensor and the optical sensor at a first preset time interval;
a determination module to determine a metadata vector based on the first data, wherein the metadata vector includes a humidity sensor vector, a temperature sensor vector, and a light sensor vector;
and the control module is used for controlling the irrigation system to irrigate according to the irrigation quantity output by the prediction model under the condition that the metadata vector is input into the pre-established prediction model and then the irrigation is determined to be needed.
The invention also provides an electronic device comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the control method as described above.
The present invention also provides a storage medium storing a computer program executable by one or more processors and operable to implement the control method as described above.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
all data collected by different sensors at different time points are input as one data of an input layer, so that the intelligence degree of the intelligent irrigation system is obviously improved, the intellectualization of the irrigation system is realized, and the purposes of saving manpower, saving water resources and efficiently irrigating are achieved.
Drawings
The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. Wherein the included drawings are:
fig. 1 is a schematic flow chart of an irrigation method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an irrigation method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of an irrigation method according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an irrigation device according to an embodiment of the present invention;
fig. 5 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 present invention clearer, the present invention will be further described in detail with reference to the accompanying drawings, the described embodiments should not be construed as limiting the present invention, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
To the extent that a similar description of "first/second/third" appears in this document, and where the description below refers to the term "first/second/third" merely to distinguish between similar items and not to imply a particular order of presentation of the items, it is to be understood that "first/second/third" may, where permissible, be interchanged of a particular order or sequence so that embodiments of the invention described herein may be practiced otherwise than as specifically illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
Based on the problems in the related art, the embodiments of the present invention provide an irrigation method, which is applied to an electronic device, where the electronic device may be a computer, a mobile terminal, and the like, and the functions implemented by the control method provided by the embodiments of the present invention may be implemented by a processor of the electronic device calling a program code, where the program code may be stored in a computer storage medium.
An embodiment of the present invention provides an irrigation method, and fig. 1 is a schematic flow chart illustrating an implementation of the irrigation method according to the embodiment of the present invention, as shown in fig. 1, including the following steps.
Step S101: first data collected by a humidity sensor, a temperature sensor and a light sensor at a first preset time interval are respectively obtained.
Step S201: a metadata vector is determined based on the first data.
In particular, the metadata vector includes a humidity sensor vector, a temperature sensor vector, and a light sensor vector.
Step S301: and under the condition that the metadata vector is input into a pre-established prediction model and then irrigation is determined to be needed, controlling an irrigation system to irrigate according to the irrigation quantity output by the prediction model.
Based on the above, the irrigation method based on the neural network provided by the invention inputs all data acquired by different sensors at different time points as one piece of data of the input layer, so that the intelligence degree of the intelligent irrigation system is obviously improved, the intelligence of the irrigation system is realized, and the purposes of saving manpower, saving water resources and efficiently irrigating are achieved.
In some embodiments, said determining a metadata vector based on said first data comprises:
respectively determining the difference value between the humidity sensor values of every two adjacent moments in the first data of the humidity sensor to obtain first equal difference value data of the humidity sensor;
a humidity sensor vector is determined based on the first arithmetic difference data and a last moment sensor value in the first data of the humidity sensor.
Based on the method, all data collected by different sensors at different time points are input as one piece of data of an input layer, and a group of new humidity data can be obtained by sliding a time point backwards, including time change, so that the model can adapt to environmental change, the intelligence degree of the model is improved, and water resources are reasonably utilized.
In some embodiments, said determining a metadata vector based on said first data comprises:
respectively determining the difference value between the temperature sensor values of every two adjacent moments in the first data of the temperature sensors to obtain second equal-grade difference value data of the temperature sensors;
determining the temperature sensor vector based on the second equal difference data and the last moment sensor value in the first data of the temperature sensor.
Based on the method, all data collected by different sensors at different time points are input as one piece of data of an input layer, and a group of new temperature data including time change can be obtained by sliding a time point backwards, so that the model can adapt to environmental change, the intelligence degree of the model is improved, and water resources are reasonably utilized.
In some embodiments, said determining a metadata vector based on said first data comprises:
respectively determining the difference value between the optical sensor values of every two adjacent moments in the first data of the optical sensor to obtain third equal difference value data of the optical sensor;
determining the light sensor vector based on the third isodyne data and a last moment sensor value in the first data of the light sensor.
Based on the method, all data collected by different sensors at different time points are input as one piece of data of an input layer, and a group of new light data can be obtained by sliding a time point backwards, including time change, so that a model can adapt to environmental change, the intelligence degree of the model is improved, and water resources are reasonably utilized.
In some embodiments, the method further comprises:
acquiring water consumption data in real time;
and controlling the irrigation system to stop irrigating when the water consumption data reaches the irrigation amount output by the prediction model.
Based on the method, the water consumption data is monitored in real time, the intelligent control system stops irrigation, and the purposes of saving manpower and water resources and efficiently irrigating are achieved.
In some embodiments, the method further comprises:
respectively acquiring second data acquired by the humidity sensor, the temperature sensor and the optical sensor at a second preset time interval;
determining a sample metadata vector based on the second data;
generating training data based on the sample metadata vector, whether irrigation is needed and the mapping relation between the irrigation quantity;
the predictive model is derived based on the training data.
In particular, the sample metadata vector includes a sample humidity sensor vector, a sample temperature sensor vector, and a sample light sensor vector.
Based on the above, the irrigation method based on the neural network provided by the invention inputs all data acquired by different sensors at different time points as one piece of data of an input layer to train the neural network, so that the intelligence degree of the intelligent irrigation system is obviously improved, and the intelligence of the irrigation system is realized.
In some embodiments, said deriving said predictive model based on said training data comprises:
dividing the training data into a training set and a test set;
inputting the training set into a neural network model for training, and using the test set to test the accuracy of the neural network model;
and determining the neural network model as the prediction model under the condition that the accuracy of the neural network model meets the requirement.
Based on the neural network, the irrigation method based on the neural network provided by the invention uses the neural network, so that the intelligence degree of the intelligent irrigation system is obviously improved, and the intelligence of the irrigation system is realized.
Fig. 2 and fig. 3 are a specific implementation flow of an irrigation method based on a neural network according to an embodiment of the present invention.
The embodiment is provided with a sensor module (comprising a humidity sensor, a temperature sensor and an illumination sensor), a control module and an irrigation module. The sensor module integrates a plurality of sensors into a whole, and is convenient to install. The underground part is provided with a humidity sensor, and the overground part is provided with a temperature sensor and an illumination sensor. It should be noted that a plurality of sensor modules may be disposed, but the disposing of the plurality of sensor modules requires special processing to avoid the occurrence of single-point failure or uneven illumination. The server module is connected to the sensor and the irrigation nozzle and used as a carrier of the operation control software system. The irrigation module consists of an electric control water valve, an electronic water meter and a rotary irrigation spray head.
As shown in fig. 2, the model training process used by the intelligent irrigation system is as follows.
S11: data of each sensor (only one of each sensor) in the experimental environment is collected and stored. The main information of the acquired data comprises recording time T, a value X of a humidity sensor, a value Y of temperature sensor data and a value Z of a light sensor, and six continuous times of data acquired every half hour form a group and serve as an acquired data unit.
S12: preprocessing acquired data before training: processing missing values: and discarding the data aiming at the missing recording time. For the missing sensor data, 6 pieces of data before and after the time point of using the missing value (data acquired by each sensor) were used as samples, and the missing value was filled by taking the median thereof.
And (3) format arrangement: and the data is arranged into data with a unified format, so that the subsequent processing is facilitated.
S13: and distinguishing the processed data. The method comprises the following steps of dividing the training set into a training set and a testing set, wherein the training set is used for training a model, and the testing set is used for verifying whether the trained model meets the standard.
S14: and (5) carrying out RBF training by taking the training set as an input layer. The data recording the time of humidity sensor, temperature sensor and light sensor tm are respectively Xm、Ym、ZmM 6 × (1,2, 3.), a set of data of the humidity sensor is obtained [ X × (1,2, 3.) ]m-5,Xm-4,Xm-3,Xm-2,Xm-1,Xm]Is equal to the difference and added with tmTime sensor value XmTo obtain a vector TXm：
[Xm-4-Xm-5,Xm-3-Xm-4,Xm-2-Xm-3,Xm-1-Xm-2,Xm-Xm-1,Xm]。
In the same way, the vector T of the temperature sensor is obtainedYmAnd a photosensor vector TZm：
[Ym-4-Ym-5,Ym-3-Ym-4,Ym-2-Ym-3,Ym-1-Ym-2,Ym-Ym-1,Ym]，
[Zm-4-Zm-5,Zm-3-Zm-4,Zm-2-Zm-3,Zm-1-Zm-2,Zm-Zm-1,Zm]。
The purpose of this is to make the data closer to real environmental changes, reflecting to some extent their variability. Thus obtaining a metadata vector Mm＝{TXm,TYm,TZmAnd then training by using an RBF neural network according to the mapping relation between the sensor data and the irrigation quantity to obtain a training result.
Specifically, a BP neural network can be used in the RBF training process, and during training, the BP neural network can be self-adaptively memorized in the weight of the neural network by learning rules among self-extracted data, so that the RBF training method has good self-learning and self-adaptive capabilities. In addition, the RBF is a feedforward neural network with excellent performance and has global approximation capability, so that the problem of partial optimal solution of the BP network is solved, the RBF can better approximate to a real environment during training, and the change of the real environment is reflected.
S15: and (4) testing and evaluating the trained data by using the test set, and judging whether the accuracy meets the specified standard or not. If not, the model is parameter-adjusted and the process returns to S14. If yes, the next step is carried out.
S16: and after the file data generated in the training process is arranged, packaging an interface and compiling an irrigation system program.
As shown in fig. 3, the overall operation of the irrigation system is as follows.
S21: data of each sensor is collected at intervals, and the interval period is 30 minutes.
S22: and uploading the data acquired in the previous step, and storing the data in the system for the next judgment of the system.
S23: the system judges irrigation once per hour, calculates whether irrigation is carried out or not, continuously calculates the irrigation quantity N if irrigation is needed, and continues to the next step. If irrigation is not required, the process returns to step S21.
It should be noted that "three hours" here is the front-to-back span of a group of data times, and "thirty minutes" is the interval between two adjacent time points in the group of data. The "three hours" depends on the number of time points and the interval of adjacent time points of each set of data. Too long an interval may not represent "environmental variability"; too short an interval tends to make the data dimension too high, i.e. too many variables for a single input data. In the present invention, they may be controlled within a certain reasonable range.
S24: the system sends an irrigation instruction to the electric control water valve to start irrigation.
S25: during the irrigation period, the system collects water consumption data continuously fed back by the electronic water meter.
S26: and calculating and judging whether the specified amount to be irrigated is reached, if so, carrying out the next step, and if not, returning to the previous step.
S27: the system sends an instruction to stop irrigation to the electrically controlled water valve and then returns to step S21.
Based on the above, the irrigation method based on the neural network provided by the invention inputs all data acquired by different sensors at different time points as one piece of data of the input layer, so that the intelligence degree of the intelligent irrigation system is obviously improved, the intelligence of the irrigation system is realized, and the purposes of saving manpower, saving water resources and efficiently irrigating are achieved.
The invention also provides an irrigation device based on the neural network. Fig. 4 is a schematic structural diagram of an irrigation device based on a neural network according to an embodiment of the present invention. As shown in fig. 4, the neural network-based irrigation apparatus 500 includes the following modules.
The acquiring module 501 is configured to acquire first data acquired by the humidity sensor, the temperature sensor, and the light sensor at a first preset time interval, respectively.
A determining module 502 for determining a metadata vector based on the first data.
In particular, the metadata vector includes a humidity sensor vector, a temperature sensor vector, and a light sensor vector.
And the control module 503 is configured to, when it is determined that irrigation is required after the metadata vector is input into the pre-established prediction model, control the irrigation system to irrigate with the irrigation quantity output by the prediction model.
Based on the above, the irrigation device based on the neural network provided by the invention inputs all data acquired by different sensors at different time points as one piece of data of the input layer, so that the intelligence degree of the intelligent irrigation system is obviously improved, the intellectualization of the irrigation system is realized, and the purposes of saving manpower, saving water resources and efficiently irrigating are achieved.
It should be noted that, in the embodiment of the present invention, if the neural network-based irrigation method is implemented in the form of a software functional module and is sold or used as a standalone product, it may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present invention provides a storage medium having a computer program stored thereon, wherein the computer program is used for implementing the steps in the control method provided in the above embodiment when being executed by a processor.
The embodiment of the invention also provides the electronic equipment. Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 5, the electronic device 600 includes: a processor 601, at least one communication bus 602, a user interface 603, at least one external communication interface 604, memory 605.
Wherein the communication bus 602 may be configured to enable connective communication between these components.
The user interface 603 may comprise a display screen, and the external communication interface 604 may comprise a standard wired interface and a wireless interface, among others. The processor 601 is configured to execute a program of the irrigation method stored in the memory to implement the steps of the neural network-based irrigation method provided in the above embodiments.
The above description of the display device and storage medium embodiments is similar to the description of the method embodiments above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the computer device and the storage medium of the present invention, reference is made to the description of the embodiments of the method of the present invention for understanding.
Here, it should be noted that: the above description of the storage medium and device embodiments is similar to the description of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and the apparatus according to the invention, reference is made to the description of the embodiments of the method according to the invention.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on this understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a controller to execute all or part of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and all such changes or substitutions 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. A neural network-based irrigation method, comprising:
respectively acquiring first data acquired by a humidity sensor, a temperature sensor and a light sensor at a first preset time interval;
determining a metadata vector based on the first data, wherein the metadata vector comprises a humidity sensor vector, a temperature sensor vector, and a light sensor vector;
and under the condition that the metadata vector is input into a pre-established prediction model and then irrigation is determined to be needed, controlling an irrigation system to irrigate according to the irrigation quantity output by the prediction model.
2. The method of claim 1, wherein determining a metadata vector based on the first data comprises:
respectively determining the difference value between the humidity sensor values of every two adjacent moments in the first data of the humidity sensor to obtain first equal difference value data of the humidity sensor;
a humidity sensor vector is determined based on the first arithmetic difference data and a last moment sensor value in the first data of the humidity sensor.
3. The method of claim 1, wherein determining a metadata vector based on the first data comprises:
respectively determining the difference value between the temperature sensor values of every two adjacent moments in the first data of the temperature sensors to obtain second equal-grade difference value data of the temperature sensors;
determining the temperature sensor vector based on the second equal difference data and the last moment sensor value in the first data of the temperature sensor.
4. The method of claim 1, wherein determining a metadata vector based on the first data comprises:
respectively determining the difference value between the optical sensor values of every two adjacent moments in the first data of the optical sensor to obtain third equal difference value data of the optical sensor;
determining the light sensor vector based on the third isodyne data and a last moment sensor value in the first data of the light sensor.
5. The method of claim 1, further comprising:
acquiring water consumption data in real time;
and controlling the irrigation system to stop irrigating when the water consumption data reaches the irrigation amount output by the prediction model.
6. The method of claim 1, further comprising:
respectively acquiring second data acquired by the humidity sensor, the temperature sensor and the optical sensor at a second preset time interval;
determining a sample metadata vector based on the second data, wherein the sample metadata vector comprises a sample humidity sensor vector, a sample temperature sensor vector, and a sample light sensor vector;
generating training data based on the sample metadata vector, whether irrigation is needed and the mapping relation between the irrigation quantity;
the predictive model is derived based on the training data.
7. The method of claim 6, wherein deriving the predictive model based on the training data comprises:
dividing the training data into a training set and a test set;
inputting the training set into a neural network model for training, and using the test set to test the accuracy of the neural network model;
and determining the neural network model as the prediction model under the condition that the accuracy of the neural network model meets the requirement.
8. An irrigation device based on a neural network, comprising:
the acquisition module is used for respectively acquiring first data acquired by the humidity sensor, the temperature sensor and the optical sensor at a first preset time interval;
a determination module to determine a metadata vector based on the first data, wherein the metadata vector includes a humidity sensor vector, a temperature sensor vector, and a light sensor vector;
and the control module is used for controlling the irrigation system to irrigate according to the irrigation quantity output by the prediction model under the condition that the metadata vector is input into the pre-established prediction model and then the irrigation is determined to be needed.
9. A storage medium, characterized in that it stores a program which, when executed by a processor, performs the method of any one of the preceding claims 1 to 7.
10. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory having stored thereon a computer program that is run by the processor to perform a method that can be implemented as claimed in any one of the preceding claims 1 to 7.
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