CN112470888A - Automatic watering method and system for smart community - Google Patents
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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
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
- A01G25/167—Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors
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- A—HUMAN NECESSITIES
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- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
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- A01G25/16—Control of watering
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Abstract
The invention discloses an automatic watering method and system for an intelligent community, wherein the method comprises the following steps: the controller acquires soil humidity information from each sensor node; the controller determines a target sensor node from the at least one sensor node, wherein the target sensor node is a sensor node of which the collected soil humidity information is smaller than a threshold value; the controller acquires first weather forecast data in a future period of time, and predicts rainfall in the future period of time according to the first weather forecast data; the controller determines the watering amount according to the predicted rainfall; the controller sends a starting signal comprising the watering amount to the target watering assembly so as to control the target watering assembly to water the plants in the area where the target watering assembly is located according to the watering amount, wherein the target watering assembly is a watering assembly corresponding to the target sensor node. The watering amount is determined based on the soil humidity information and the weather forecast data, so that the watering of the plants is more accurate, and the waste of water resources is avoided.
Description
Technical Field
The invention relates to the field of automatic control, in particular to an automatic watering method and system for an intelligent community.
Background
Community gardens refer to the afforestation design in the community, in order to keep the green in the afforestation to plant normal growth, need regularly water to the green of community gardens, at present, in the wisdom community, adopt two kinds of modes to water, one kind adopts artifical mode, water the green of gardens through artifical practical hose, another kind adopts automatic watering, but generally water the green of gardens regularly and quantitatively, two kinds of modes above all are all lower to the accuracy that green was watered, though some wisdom communities can water according to the moist degree of soil, do not consider the weather forecast, then there is this limit just to water and finish, rainy weather appears in the same day, lead to the water content in the soil too big and the waste of water resource. Therefore, finding an accurate green plant watering method becomes a problem to be solved for the development of intelligent communities.
Disclosure of Invention
The invention provides an automatic watering method and an automatic watering system for an intelligent community, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In a first aspect, an embodiment of the present invention provides an automatic watering method for a smart community, which is applied to an automatic watering system for the smart community, the system includes at least one sensor node, a controller, and at least one watering component, the sensor node corresponds to the watering component one by one, the at least one sensor node communicates with the controller wirelessly, and the controller controls the at least one watering component through a wired or wireless mode; the automatic watering method comprises the following steps:
the controller acquires soil humidity information from each sensor node, wherein each sensor node periodically samples soil and sends the sampled soil humidity information to the controller;
the controller determines a target sensor node from the at least one sensor node, wherein the target sensor node is a sensor node of which the collected soil humidity information is smaller than a threshold value;
the controller acquires first weather forecast data in a future period of time, and predicts rainfall in the future period of time according to the first weather forecast data;
the controller determines the watering amount according to the predicted rainfall;
the controller sends a starting signal comprising the watering amount to the target watering assembly so as to control the target watering assembly to water the plants in the area where the target watering assembly is located according to the watering amount, wherein the target watering assembly is a watering assembly corresponding to the target sensor node.
Further, each sensor node includes a humidity sensor through which soil is periodically sampled.
Further, the predicting rainfall in a future period of time according to the first weather forecast data comprises:
extracting first weather data features from the first weather forecast data;
determining the type of rainfall according to the first weather data characteristic;
selecting a corresponding rainfall prediction model according to the rainfall type, wherein different rainfall types correspond to different rainfall prediction models;
and inputting the first weather data characteristic as input data into the corresponding rainfall prediction model to obtain the predicted rainfall in a future period of time.
Further, each rainfall prediction model is obtained by the following steps:
collecting rainfall data of a period of time in the past, wherein the rainfall data comprises second weather forecast data and actual rainfall;
extracting a second weather data characteristic from the second weather forecast data;
inputting the second weather data characteristic into a K-means clustering model, and clustering the rainfall types to obtain a clustering result;
according to the clustering result, dividing the second weather data characteristics into weather data characteristics of different rainfall types;
and respectively constructing deep learning network models for different rainfall types, setting the actual rainfall as a label of a second weather data characteristic, inputting the second weather data characteristic with the label of each rainfall type into the corresponding deep learning network model, and training the deep learning network model through a back propagation algorithm to obtain each trained rainfall prediction model.
Further, the weather data features include: weather phenomena, temperature, probability of rainfall, air pressure and relative humidity.
Further, the controller determines the amount of watering based on the predicted amount of rainfall;
the controller determines the rainfall level according to the predicted rainfall;
the controller determines the watering amount according to the rainfall level and the watering amount corresponding table.
In a second aspect, an embodiment of the present invention further provides an automatic watering system for a smart community, including at least sensor nodes, a controller, and at least watering components, where the sensor nodes correspond to the watering components one to one, the at least one sensor node communicates with the controller wirelessly, and the controller controls the at least one watering component by wire or wirelessly;
each sensor node is used for periodically sampling soil and sending the sampled soil humidity information to the controller;
the controller is used for determining a target sensor node from the at least one sensor node, wherein the target sensor node is a sensor node of which the collected soil humidity information is smaller than a threshold value, acquiring weather forecast data in a period of time in the future, predicting rainfall in the period of time in the future according to the weather forecast data, determining watering amount according to the predicted rainfall, and sending a starting signal to a target watering assembly, wherein the starting signal comprises the watering amount, and the target watering assembly is a watering assembly corresponding to the target sensor node;
and the watering assembly is used for watering the plants in the area with the watering quantity when receiving the starting signal.
Further, each sensor node includes a humidity sensor through which soil is periodically sampled.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of the first aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium, in which processor-executable instructions are stored, and when executed by a processor, the processor-executable instructions are used to implement the method of the first aspect.
The embodiment of the invention at least has the following beneficial effects: the method comprises the steps of periodically acquiring soil humidity information, timely finding that a plant lacks water, considering watering when the acquired soil humidity information is smaller than a threshold value, acquiring weather forecast data in a period of time in the future, predicting rainfall in the period of time in the future according to the weather forecast data, determining watering amount according to the predicted rainfall, and determining watering amount based on the soil humidity information and the weather forecast data, so that the plant is watered more accurately, and water resource waste is avoided.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a flowchart illustrating an automatic watering method for a smart community according to an embodiment of the present invention.
Fig. 2 is a flowchart of predicting rainfall in a future period of time according to the first weather forecast data according to an embodiment of the present invention.
Fig. 3 is a flowchart for obtaining each rainfall prediction model according to an embodiment of the present invention.
FIG. 4 is a flow chart for determining the amount of watering based on the predicted amount of rainfall provided by an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an automatic watering system for a smart community according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that although functional block divisions are provided in the system drawings and logical orders are shown in the flowcharts, in some cases, the steps shown and described may be performed in different orders than the block divisions in the systems or in the flowcharts. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Fig. 1 is an automatic watering method for a smart community, which is applied to an automatic watering system for the smart community, according to an embodiment of the present invention, the automatic watering system for the smart community includes at least one sensor node, a controller and at least one watering component, the sensor node corresponds to the watering component one to one, the at least one sensor node communicates with the controller wirelessly, and the controller controls the at least one watering component through a wired or wireless connection; the automatic watering method for the intelligent community comprises the following steps:
s11, the controller acquires soil humidity information from each sensor node, wherein each sensor node periodically samples soil and sends the sampled soil humidity information to the controller;
s12, the controller determines a target sensor node from at least one sensor node, wherein the target sensor node is a sensor node of which the collected soil humidity information is smaller than a threshold value;
s13, the controller acquires first weather forecast data in a future period of time, and the rainfall in the future period of time is predicted according to the first weather forecast data;
s14, determining the watering amount according to the predicted rainfall by the controller;
and S15, the controller sends a starting signal comprising the watering amount to the target watering assembly to control the target watering assembly to water the plants in the area where the target watering assembly is located according to the watering amount, wherein the target watering assembly is a watering assembly corresponding to the target sensor node.
Wherein, every sensor node all includes humidity transducer, through humidity transducer is periodic to carry out the sampling to soil. The water shortage of the plants can be found in time by periodically sampling. The sampling period may be set in actual seasons, for example, the sampling period in summer may be designed to be shorter than that in winter.
The future period of time can be set according to actual needs, for example, 24 hours in the future, a weather forecast of 24 hours in the future, or a weather forecast of 2 days in the future.
As shown in fig. 2, the step S13 of predicting the rainfall in a future period according to the first weather forecast data specifically includes the following steps:
s21, extracting first weather data features from the first weather forecast data;
s22, determining the rainfall type according to the first weather data characteristics;
s23, selecting corresponding rainfall prediction models according to rainfall types, wherein different rainfall types correspond to different rainfall prediction models;
and S24, inputting the first weather data characteristic as input data into a corresponding rainfall prediction model to obtain the predicted rainfall in a future period of time.
As shown in fig. 3, each rainfall prediction model is obtained by the following steps:
s31, collecting rainfall data in a past period, wherein the rainfall data comprises a second weather data characteristic and an actual rainfall; wherein the rainfall data over a period of time may be historical rainfall data. Thus, rainfall data includes a large amount of rainfall data.
S32, extracting second weather data characteristics from the second weather forecast data;
s33, inputting the second weather data characteristic into a K-means clustering model, and clustering the rainfall type to obtain a clustering result;
s34, dividing the second weather data characteristics into weather data characteristics of different rainfall types according to the clustering result;
s35, respectively constructing deep learning network models for different rainfall types, setting actual rainfall as a label of a second weather data characteristic, inputting the second weather data characteristic with the label of each rainfall type into the corresponding deep learning network model, and training the deep learning network model through a back propagation algorithm to obtain each trained rainfall prediction model.
Further, the weather data features include: weather phenomena, temperature, probability of rainfall, air pressure and relative humidity.
Part of the weather phenomenon information codes are shown in the following table 1.
TABLE 1 correspondence between weather phenomena and information coding
The rainfall types can be classified according to the weather phenomenon types: small rain, medium rain, heavy rain, gusty rain and heavy rain. Due to the low frequency of heavy rainstorms and heavy rainstorms, heavy rainstorms and heavy rainstorms are classified as rainstorms and are represented by the code 10. The encoding of weather phenomena is used as input data. In addition, normalization processing is carried out before the first weather data characteristic is input into the trained rainfall prediction model and before the second weather data characteristic is input into the deep learning network model.
As shown in fig. 4, the determining of the watering amount according to the predicted rainfall in step S14 includes the steps of:
s41, determining the rainfall level according to the predicted rainfall;
and S42, determining the watering amount according to the rainfall level and the watering amount corresponding table.
Specifically, the classification into seven grades according to the rainfall classification criteria is shown in table 2 below.
TABLE 2 corresponding relationship between rainfall and rainfall level
The controller prestores a rainfall level and rainfall mapping table, determines the rainfall level according to the predicted rainfall, and determines the watering amount according to the rainfall level. Presetting a corresponding table of rainfall grade and watering amount, wherein the corresponding relation between the watering amount and the rainfall grade in the corresponding table of the rainfall grade and the watering amount is as follows:
wherein W is the watering amount output by the watering assembly, R is the rainfall grade, R is 0, 1, 2, 3, 4, 5 and 6 respectively correspond to no rain, light rain, medium rain, heavy rain and extra heavy rain, and W represents the watering amount output when no rain exists. Therefore, when the rainfall level of the rainfall is predicted to be heavy rain or above the heavy rain level according to the weather forecast, watering is not carried out even if the soil humidity information is smaller than the threshold value, so that the phenomenon that the rainfall is heavy rain or above the heavy rain level after watering is avoided, the water content in the soil is too large, and water resources are wasted.
Fig. 5 is an automatic watering system for a smart community according to an embodiment of the present invention, which includes at least sensor nodes, a controller and at least watering components, wherein the sensor nodes correspond to the watering components one to one, the at least one sensor node is in wireless communication with the controller, and the controller controls the at least one watering component through a wired or wireless connection; although only 3 areas are shown in fig. 5, each area is provided with 1 sensor node and 1 watering component, it should be noted that the number of the divided areas can be divided according to the garden size of the smart community, the number of the sensors and the watering components can be set according to the garden size of the smart community, each watering component is arranged in different areas, and each sensor node and the watering component corresponding to the sensor node are arranged in the same area.
Each sensor node is used for periodically sampling soil and sending the sampled soil humidity information to the controller;
the controller is used for determining a target sensor node from the at least one sensor node, wherein the target sensor node is a sensor node of which the collected soil humidity information is smaller than a threshold value, acquiring weather forecast data in a period of time in the future, predicting rainfall in the period of time in the future according to the weather forecast data, determining watering amount according to the predicted rainfall, and sending a starting signal to a target watering assembly, wherein the starting signal comprises the watering amount, and the target watering assembly is a watering assembly corresponding to the target sensor node;
and the watering assembly is used for watering the plants in the area with the watering quantity when receiving the starting signal.
Further, each sensor node includes a humidity sensor through which soil is periodically sampled.
The intelligent home control method and the intelligent home control system provided by the embodiment of the invention at least have the following beneficial effects: the method comprises the steps of periodically acquiring soil humidity information, timely finding that a plant lacks water, considering watering when the acquired soil humidity information is smaller than a threshold value, acquiring weather forecast data in a period of time in the future, predicting rainfall in the period of time in the future according to the weather forecast data, determining watering amount according to the predicted rainfall, and determining watering amount based on the soil humidity information and the weather forecast data, so that the plant is watered more accurately, and water resource waste is avoided.
An embodiment of the present invention further provides an electronic device, including:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to perform the method steps described above.
Embodiments of the present invention also provide a computer-readable storage medium having stored therein processor-executable instructions for implementing the above-described method steps when executed by a processor.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.
Claims (10)
1. The automatic watering method for the intelligent community is characterized by being applied to an automatic watering system of the intelligent community, wherein the system comprises at least one sensor node, a controller and at least one watering component, the sensor node corresponds to the watering component one by one, the at least one sensor node is in wireless communication with the controller, and the controller controls the at least one watering component through a wired line or a wireless line; the automatic watering method comprises the following steps:
the controller acquires soil humidity information from each sensor node, wherein each sensor node periodically samples soil and sends the sampled soil humidity information to the controller;
the controller determines a target sensor node from the at least one sensor node, wherein the target sensor node is a sensor node of which the collected soil humidity information is smaller than a threshold value;
the controller acquires first weather forecast data in a future period of time, and predicts rainfall in the future period of time according to the first weather forecast data;
the controller determines the watering amount according to the predicted rainfall;
the controller sends a starting signal comprising the watering amount to the target watering assembly so as to control the target watering assembly to water the plants in the area where the target watering assembly is located according to the watering amount, wherein the target watering assembly is a watering assembly corresponding to the target sensor node.
2. The intelligent community automatic watering method according to claim 1, wherein each sensor node comprises a humidity sensor through which soil is periodically sampled.
3. The intelligent community automatic watering method of claim 1, wherein said predicting rainfall in a future period of time based on first weather forecast data comprises:
extracting first weather data features from the first weather forecast data;
determining the type of rainfall according to the first weather data characteristic;
selecting a corresponding rainfall prediction model according to the rainfall type, wherein different rainfall types correspond to different rainfall prediction models;
and inputting the first weather data characteristic as input data into the corresponding rainfall prediction model to obtain the predicted rainfall in a future period of time.
4. The automated watering method for smart communities according to claim 3, wherein each rainfall prediction model is obtained by the following steps:
collecting rainfall data of a period of time in the past, wherein the rainfall data comprises second weather forecast data and actual rainfall;
extracting a second weather data characteristic from the second weather forecast data;
inputting the second weather data characteristic into a K-means clustering model, and clustering the rainfall types to obtain a clustering result;
according to the clustering result, dividing the second weather data characteristics into weather data characteristics of different rainfall types;
and respectively constructing deep learning network models for different rainfall types, setting the actual rainfall as a label of a second weather data characteristic, inputting the second weather data characteristic with the label of each rainfall type into the corresponding deep learning network model, and training the deep learning network model through a back propagation algorithm to obtain each trained rainfall prediction model.
5. The intelligent community automatic watering method of claim 3, wherein the weather data characteristics comprise: weather phenomena, temperature, probability of rainfall, air pressure and relative humidity.
6. The intelligent community automatic watering method of claim 1, wherein the controller determines the amount of watering based on a predicted amount of rainfall;
the controller determines the rainfall level according to the predicted rainfall;
the controller determines the watering amount according to the rainfall level and the watering amount corresponding table.
7. The automatic watering system of the intelligent community is characterized by comprising at least sensor nodes, a controller and at least watering components, wherein the sensor nodes correspond to the watering components one to one, the at least one sensor node is in wireless communication with the controller, and the controller controls the at least one watering component through wired or wireless;
each sensor node is used for periodically sampling soil and sending the sampled soil humidity information to the controller;
the controller is used for determining a target sensor node from the at least one sensor node, wherein the target sensor node is a sensor node of which the collected soil humidity information is smaller than a threshold value, acquiring weather forecast data in a period of time in the future, predicting rainfall in the period of time in the future according to the weather forecast data, determining watering amount according to the predicted rainfall, and sending a starting signal to a target watering assembly, wherein the starting signal comprises the watering amount, and the target watering assembly is a watering assembly corresponding to the target sensor node;
and the watering assembly is used for watering the plants in the area with the watering quantity when receiving the starting signal.
8. The intelligent community's automatic watering system of claim 7, wherein each sensor node includes a humidity sensor through which soil is periodically sampled.
9. An electronic device, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-6.
10. A computer-readable storage medium having stored therein instructions executable by a processor, the computer-readable storage medium comprising: the processor-executable instructions, when executed by a processor, are for implementing the method of any one of claims 1-6.
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