CN116415488A - Soil humidity prediction method and device, electronic equipment and storage medium - Google Patents
Soil humidity prediction method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116415488A CN116415488A CN202310226978.9A CN202310226978A CN116415488A CN 116415488 A CN116415488 A CN 116415488A CN 202310226978 A CN202310226978 A CN 202310226978A CN 116415488 A CN116415488 A CN 116415488A
- Authority
- CN
- China
- Prior art keywords
- soil humidity
- target area
- information
- current
- time step
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000002689 soil Substances 0.000 title claims abstract description 299
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000003860 storage Methods 0.000 title claims abstract description 16
- 230000012010 growth Effects 0.000 claims abstract description 129
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 51
- 230000008859 change Effects 0.000 claims description 58
- 238000003973 irrigation Methods 0.000 claims description 10
- 230000002262 irrigation Effects 0.000 claims description 10
- 230000003698 anagen phase Effects 0.000 claims description 3
- 238000010521 absorption reaction Methods 0.000 abstract description 6
- 230000008569 process Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 241000209140 Triticum Species 0.000 description 4
- 235000021307 Triticum Nutrition 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004278 EU approved seasoning Substances 0.000 description 1
- 241000668842 Lepidosaphes gloverii Species 0.000 description 1
- 240000008042 Zea mays Species 0.000 description 1
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 1
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 235000005822 corn Nutrition 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 235000011194 food seasoning agent Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000009331 sowing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Marketing (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- Game Theory and Decision Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Agronomy & Crop Science (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Computer Hardware Design (AREA)
- Development Economics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Geometry (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a soil humidity prediction method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring current soil humidity, current meteorological information and crop growth stage information of a target area; determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area; determining the water demand of crops in the current growth stage according to the information of the growth stage of the crops; and determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in the preset time step and the water demand of crops in the current growth stage. According to the method provided by the scheme, the crop water demand represented by the crop growth stage information is used as an important influence factor for predicting the soil moisture, so that the absorption of the crop to the soil moisture is considered when the soil moisture is predicted in the target area, and the accuracy of the soil moisture prediction result is improved.
Description
Technical Field
The present disclosure relates to the field of humidity prediction technologies, and in particular, to a soil humidity prediction method, a device, an electronic apparatus, and a storage medium.
Background
Soil humidity is a key factor for crop growth, and a prediction result of the soil humidity can be used as an important guiding basis for crop irrigation, so that how to predict the soil humidity becomes important research content.
In the prior art, the soil humidity of several days in the future is usually predicted according to local meteorological data, but the accuracy of the soil humidity prediction result obtained by the prior art is lower because the soil humidity is not only influenced by meteorological factors.
Disclosure of Invention
The application provides a soil humidity prediction method, a device, electronic equipment and a storage medium, which are used for solving the defects of low accuracy and the like of a soil humidity prediction result obtained in the prior art.
A first aspect of the present application provides a soil moisture prediction method, including:
acquiring current soil humidity, current meteorological information and crop growth stage information of a target area;
determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area;
determining the water demand of crops in the current growth stage according to the crop growth stage information;
and determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in a preset time step and the water demand of the crops in the current growth stage.
Optionally, the current weather information includes current weather data and current circulation data, and determining, according to the current soil humidity and the current weather information of the target area, the predicted soil humidity information of the target area within a preset time step includes:
determining soil humidity change information of the target area in a preset time step according to the current meteorological data and the current circulation data of the target area;
and determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity of the target area and the soil humidity change information of the target area in the preset time step.
Optionally, obtaining the current soil humidity, the current weather information and the crop growth stage information of the target area includes:
dividing the target area into a plurality of area grids;
and acquiring the current soil humidity, the current meteorological information and the crop growth stage information of each regional grid.
Optionally, the determining the predicted information of the soil humidity of the target area in the preset time step according to the current soil humidity of the target area and the information of the change of the soil humidity of the target area in the preset time step includes:
determining a soil humidity space influence result of the target area according to the current soil humidity of each area grid;
and determining the soil humidity prediction information of the target area in a preset time step according to the soil humidity space influence result of the target area and the soil humidity change information of the target area in the preset time step.
Optionally, the determining the predicted result of the soil humidity of the target area according to the predicted information of the soil humidity of the target area in the preset time step and the water demand of the crops in the current growth stage includes:
determining growth stage change information of the crops in a preset time step according to soil humidity prediction information of the target area in the preset time step and water demand of the crops in a current growth stage;
determining the water demand change information of the crops in the preset time step according to the growth stage change information of the crops in the preset time step;
and determining a soil humidity prediction result of the target area according to the water demand change information of the crops in the preset time step and the soil humidity prediction information.
Optionally, the method further comprises:
determining a target soil humidity range of the crops in the preset time step according to the growth stage change information of the crops in the preset time step;
and determining a crop irrigation strategy of the target area according to the soil humidity prediction result of the target area and the target soil humidity range.
Optionally, the determining, according to the growth stage change information of the crop in the preset time step, the target soil humidity range of the crop in the preset time step includes:
judging whether the crop enters the next growth stage in the preset time step according to the growth stage change information of the crop in the preset time step;
if the crops enter the next growth stage within the preset time step, determining the proper soil humidity range of the next growth stage as a target soil humidity range of the crops within the preset time step;
and if the crops do not enter the next growth stage within the preset time step, determining the proper soil humidity range of the current growth stage as the target soil humidity range of the crops within the preset time step.
A second aspect of the present application provides a soil moisture prediction apparatus comprising:
the acquisition module is used for acquiring the current soil humidity, the current meteorological information and the crop growth stage information of the target area;
the first determining module is used for determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area;
the second determining module is used for determining the water demand of the crops in the current growth stage according to the crop growth stage information;
and the prediction module is used for determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in a preset time step and the water demand of the crops in the current growth stage.
Optionally, the current weather information includes current weather data and current circulation data, and the first determining module is specifically configured to:
determining soil humidity change information of the target area in a preset time step according to the current meteorological data and the current circulation data of the target area;
and determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity of the target area and the soil humidity change information of the target area in the preset time step.
Optionally, the acquiring module is specifically configured to:
dividing the target area into a plurality of area grids;
and acquiring the current soil humidity, the current meteorological information and the crop growth stage information of each regional grid.
Optionally, the first determining module is specifically configured to:
determining a soil humidity space influence result of the target area according to the current soil humidity of each area grid;
and determining the soil humidity prediction information of the target area in a preset time step according to the soil humidity space influence result of the target area and the soil humidity change information of the target area in the preset time step.
Optionally, the prediction module is specifically configured to:
determining growth stage change information of the crops in a preset time step according to soil humidity prediction information of the target area in the preset time step and water demand of the crops in a current growth stage;
determining the water demand change information of the crops in the preset time step according to the growth stage change information of the crops in the preset time step;
and determining a soil humidity prediction result of the target area according to the water demand change information of the crops in the preset time step and the soil humidity prediction information.
Optionally, the prediction module is further configured to:
determining a target soil humidity range of the crops in the preset time step according to the growth stage change information of the crops in the preset time step;
and determining a crop irrigation strategy of the target area according to the soil humidity prediction result of the target area and the target soil humidity range.
Optionally, the prediction module is specifically configured to:
judging whether the crop enters the next growth stage in the preset time step according to the growth stage change information of the crop in the preset time step;
if the crops enter the next growth stage within the preset time step, determining the proper soil humidity range of the next growth stage as a target soil humidity range of the crops within the preset time step;
and if the crops do not enter the next growth stage within the preset time step, determining the proper soil humidity range of the current growth stage as the target soil humidity range of the crops within the preset time step.
A third aspect of the present application provides an electronic device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes the computer-executable instructions stored by the memory such that the at least one processor performs the method as described above in the first aspect and the various possible designs of the first aspect.
A fourth aspect of the present application provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the method as described above in the first aspect and the various possible designs of the first aspect.
The technical scheme of the application has the following advantages:
the application provides a soil humidity prediction method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring current soil humidity, current meteorological information and crop growth stage information of a target area; determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area; determining the water demand of crops in the current growth stage according to the information of the growth stage of the crops; and determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in the preset time step and the water demand of crops in the current growth stage. According to the method provided by the scheme, the crop water demand represented by the crop growth stage information is used as an important influence factor for predicting the soil moisture, so that the absorption of the crop to the soil moisture is considered when the soil moisture is predicted in the target area, and the accuracy of the soil moisture prediction result is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a schematic structural diagram of a soil humidity prediction system according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a soil humidity prediction method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a soil humidity prediction apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but to illustrate the concepts of the present application to those skilled in the art with reference to the specific embodiments.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. In the following description of the embodiments, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the prior art, the soil humidity of several days in the future is usually predicted according to local meteorological data, in recent years, an artificial intelligent algorithm is gradually applied to the field of soil humidity prediction, but a traditional machine learning model mainly focuses on the influence of a target value (soil humidity) on the time dependence of meteorological elements, on one hand, the soil humidity dynamically changes along with time, the growth state and the water demand of crops also change, on the other hand, the drought degree is predicted by the existing technology, the water demand of crops is not predicted directly, in addition, the influence of the spatial information of the meteorological elements on the soil humidity is not considered by the traditional machine learning model, so that the soil humidity cannot be predicted accurately, and the accurate drought early warning cannot be provided for the growth of crops such as wheat.
Aiming at the problems, the soil humidity prediction method, the soil humidity prediction device, the electronic equipment and the storage medium provided by the embodiment of the application acquire the current soil humidity, the current meteorological information and the crop growth stage information of a target area; determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area; determining the water demand of crops in the current growth stage according to the information of the growth stage of the crops; and determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in the preset time step and the water demand of crops in the current growth stage. According to the method provided by the scheme, the crop water demand represented by the crop growth stage information is used as an important influence factor for predicting the soil moisture, so that the absorption of the crop to the soil moisture is considered when the soil moisture is predicted in the target area, and the accuracy of the soil moisture prediction result is improved.
The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
First, a structure of a soil moisture prediction system according to the present application will be described:
the soil humidity prediction method, the device, the electronic equipment and the storage medium are suitable for predicting the soil humidity of the crop planting land. Fig. 1 is a schematic structural diagram of a soil humidity prediction system according to an embodiment of the present application, which mainly includes a data acquisition device and a soil humidity prediction device. Specifically, the data acquisition device is used for acquiring current soil humidity, current meteorological information, crop growth stage information and the like of a crop planting field, and sending the acquired information to the soil humidity prediction device, and the device predicts the soil humidity of the crop planting field according to the acquired information.
The embodiment of the application provides a soil humidity prediction method which is used for predicting the soil humidity of a crop planting field. The execution main body of the embodiment of the application is electronic equipment, such as a server, a desktop computer, a notebook computer, a tablet computer and other electronic equipment which can be used for predicting the soil humidity of a crop planting field.
As shown in fig. 2, a flow chart of a soil humidity prediction method according to an embodiment of the present application is shown, where the method includes:
The target area specifically refers to a crop planting area, and the crop can be wheat or corn and the like.
Wherein the preset time step may be 7 days or 30 days, etc.
It should be noted that, the soil humidity is mainly affected by meteorological elements such as the water-reducing amount and the evapotranspiration amount, so the current meteorological information can be used as a main basis for predicting the soil humidity.
Specifically, according to weather elements such as precipitation, evapotranspiration and the like of the current weather data representing the future days, the soil humidity change condition of the target area in the preset time step can be determined, and then the current soil humidity is combined to determine the soil humidity prediction information of the target area in the preset time step. Wherein, the soil humidity prediction information characterizes the prediction result under the condition that the soil humidity is only influenced by the meteorological factors.
And 203, determining the water demand of the crops in the current growth stage according to the crop growth stage information.
When the crop is wheat, the growth stage of the crop is at least divided into a sowing stage, a leaf pulling stage, a flowering stage and a maturing stage.
Specifically, the water demand of the crops in the current growth stage can be determined according to the current growth stage of the crops represented by the crop growth stage information and the water demand corresponding to each growth stage.
And 204, determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in the preset time step and the water demand of crops in the current growth stage.
Specifically, the soil moisture absorption amount of the crops in the preset time step can be determined according to the water demand of the crops in the current growth stage, and then the soil moisture prediction result of the target area is determined by combining the soil moisture prediction information of the target area in the preset time step and the soil moisture absorption amount of the crops in the preset time step.
On the basis of the foregoing embodiment, in order to further improve the accuracy of the final obtained soil humidity prediction result, as an implementation manner, in one embodiment, the current weather information includes current weather data and current circulation data, and determining, according to the current soil humidity and current weather information of the target area, the soil humidity prediction information of the target area within a preset time step includes:
step 2021, determining soil humidity change information of the target area within a preset time step according to the current meteorological data and current circulation data of the target area;
in step 2022, according to the current soil humidity of the target area and the soil humidity change information of the target area within the preset time step, determining the soil humidity prediction information of the target area within the preset time step.
The current meteorological data specifically may include precipitation amount, month highest air temperature, month lowest air temperature, month average relative humidity, month average air pressure, month average radiation and the like, and the current circulation data specifically refers to large-scale circulation data such as potential altitude fields of 200hPa, 500hPa and 1000 hPa and an air temperature field of 850 hPa.
Specifically, the soil humidity prediction information of the target area under the current meteorological data and the current circulation data can be determined based on a preset soil humidity prediction information prediction model. The preset soil humidity prediction information prediction model can be a PredRNN++ model, the PredRNN++ model provides an end-to-end modeling workflow, the feature extraction process is integrated into the modeling process, the model is allowed to autonomously learn features, and compared with a traditional machine learning model, the PredRNN++ model is not limited by the features designed in advance. The internal structure is changed from Stack-LSTM to Causer-LSTM, and is changed from parallel computing to cascade computing. This newly designed cascade memory enables a predictive model to model greater short-term mutations. Compared with the Stack ConvLSTM time sequence model, the method has the advantages that each layer is connected in a common process of time steps (step to step), and no extra modeling capacity exists; the predrnn++ model is innovative in that each time is propagated to have connection of each layer, and is propagated from the deepest layer (layer L) back to layer 1, so that the propagation depth of time steps is increased, a Gradient Highway Unit (GHU) structure is added to the first layer, and the condition that gradient vanishes in the depth transmission of a neural network is avoided.
Specifically, for the training process of the soil humidity prediction information prediction model, a data set is constructed in advance, the data set includes meteorological data, circulation data and soil humidity data of a target area in the past several years, and then the data set is divided into a training set and a test set. Based on the influence of meteorological data and circulation data represented by each piece of data in the training set on soil humidity data, establishing a corresponding relation between meteorological information and soil humidity prediction information, taking a loss function reduction as a target, and performing model parameter adjustment until the loss is minimum, so as to obtain a trained soil humidity prediction information prediction model. And then testing the trained soil humidity prediction information prediction model based on the test set to verify the accuracy of the model.
In the data set construction process, the meteorological data, the circulation data and the soil humidity data can be normalized according to the following normalization formula:
X scaled ＝ std ×(max-min)+min
wherein X is min Is the row vector of the minimum value in each column; min max Is the row vector consisting of the maxima in each column; max is the maximum value of the mapped interval, and is 1 by default; min is the minimum value of the mapped interval, and defaults to 0; x is X std Is a standardized result; x is X scaled Is a normalization result.
Specifically, during the model test, the error between the predicted value and the measured value output by the model may be calculated according to the following evaluation function:
wherein,,
indicating predicted soil moisture,/->
Indicating the measured soil humidity; the RMSE ranges from 0 to positive infinity, the smaller the RMSE, the more indicative of errorThe smaller the difference; the NSE ranges from minus infinity to 1, and the closer the NSE is to 1, the better the process agreement of the predicted value and the measured value is indicated.
Specifically, in an embodiment, how to further deeply integrate the influence of the spatial variation information of the related elements on the prediction is a problem to be solved on the basis of considering the time dependence of the soil humidity prediction on the meteorological elements. To solve this problem, the target region may be divided into several region meshes; and acquiring the current soil humidity, the current meteorological information and the crop growth stage information of each regional grid.
Further, in an embodiment, a soil humidity space influence result of the target area may be determined according to the current soil humidity of each area grid; and determining the soil humidity prediction information of the target area in the preset time step according to the soil humidity space influence result of the target area and the soil humidity change information of the target area in the preset time step.
It should be noted that, in the target area, each area grid is affected by spatial changes such as topography, and there is a certain difference in soil humidity of different area networks.
Specifically, the target area can be divided into a plurality of area grids according to a preset area grid specification, and the current soil humidity, the current weather information and the crop growth stage information of each area grid are obtained. Further, according to the differences of the current soil humidity characterization of each regional grid, a soil humidity space influence result of the target region can be determined, wherein the soil humidity space influence result at least characterizes the soil humidity influence among adjacent regional grids, and the soil humidity prediction information of the target region in a preset time step is determined by combining the soil humidity space influence result of the target region and the soil humidity change information of the target region in the preset time step, so that the obtained soil humidity prediction information considers the regional space change, the reliability of the soil humidity prediction information is improved, and the accuracy of the finally obtained soil humidity prediction result is improved.
Wherein, each regional net can all set up a soil humidity actual measurement equipment, and this equipment includes wireless network signal device and detecting head at least, and the detecting head can set for the degree of depth (0 ~ 30 cm) that stretches into soil as required for detect the soil humidity of corresponding degree of depth, and wireless network signal device is used for reporting the soil humidity that the detecting head detected. The obtained measured data of the soil humidity are arranged into a data format meeting the model requirement, and the data format is mainly the measurement unit conversion of the soil humidity.
Specifically, for the soil humidity prediction information prediction model provided by the embodiment, the model can receive data with different spatial resolutions, namely, soil humidity, weather information and crop growth stage information with different regional grid specifications, so that the input data is not required to be processed in a downscaling way for maintaining the same resolution, and the data information integrity is ensured, which is also a representation of model compatibility.
On the basis of the above embodiment, as an implementation manner, in an embodiment, determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area within a preset time step and the water demand of the crops in the current growth stage includes:
step 2041, determining growth stage change information of crops in a preset time step according to soil humidity prediction information of a target area in the preset time step and water demand of the crops in a current growth stage;
step 2042, determining the water demand change information of the crops in a preset time step according to the growth stage change information of the crops in the preset time step;
and 2043, determining a soil humidity prediction result of the target area according to the water demand change information and the soil humidity prediction information of the crops in the preset time step.
Wherein the growth phase change information at least comprises the time of entering the next growth phase.
Specifically, according to the soil humidity prediction information of the target area in the preset time step and the water demand of the crops in the current growth stage, whether the crops enter the next growth stage in the preset time step or not can be judged, the time for entering the next growth stage is predicted, and the growth stage change information of the crops in the preset time step is obtained. And then determining the growth stage of the crops at each time point in the preset time step according to the growth stage change information of the crops in the preset time step, and further obtaining the water demand change information of the crops in the preset time step according to the water demand of the crops in different growth stages. And finally, on the basis of the soil humidity prediction information, combining the water demand change information of the crops in a preset time step to obtain a soil humidity prediction result of the target area.
Further, in an embodiment, the target soil humidity range of the crops in the preset time step can be determined according to the growth stage change information of the crops in the preset time step; and determining a crop irrigation strategy of the target area according to the soil humidity prediction result and the target soil humidity range of the target area.
Specifically, in an embodiment, it may be determined whether the crop will enter the next growth stage within a preset time step according to the growth stage change information of the crop within the preset time step; if the crops enter the next growth stage within the preset time step, determining the proper soil humidity range of the next growth stage as a target soil humidity range of the crops within the preset time step; if the crops do not enter the next growth stage within the preset time step, determining the proper soil humidity range of the current growth stage as the target soil humidity range of the crops within the preset time step.
Illustratively, if the crop is wheat, the target soil moisture ranges for the crop at each growth stage are shown in the following table:
specifically, if the soil humidity of the target area represented by the soil humidity prediction result of the target area cannot reach the corresponding target soil humidity range, the drought risk of the target area is predicted, and then the crop irrigation strategy of the target area can be determined according to the difference between the soil humidity prediction result and the target soil humidity range. The crop irrigation strategy at least comprises information such as irrigation time, irrigation flow and the like.
According to the soil humidity prediction method, the current soil humidity, the current meteorological information and the crop growth stage information of the target area are obtained; determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area; determining the water demand of crops in the current growth stage according to the information of the growth stage of the crops; and determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in the preset time step and the water demand of crops in the current growth stage. According to the method provided by the scheme, the crop water demand represented by the crop growth stage information is used as an important influence factor for predicting the soil moisture, so that the absorption of the crop to the soil moisture is considered when the soil moisture is predicted in the target area, and the accuracy of the soil moisture prediction result is improved.
In addition, the embodiment of the application breaks through the limitation that the time sequence model cannot effectively consider variable space information by adopting the PredRNN++ model, has nonlinear characteristics and flexible fitting capability, overcomes the defects of the traditional model, ensures the depth of the model and simultaneously avoids gradient disappearance. On the basis of considering the time dependence of soil humidity prediction on meteorological elements, the influence of the spatial variation of related elements on the prediction result is deeply fused, and meanwhile, the atmospheric circulation data are added. The method is expected to solve the prediction difficulty of the soil humidity in a large range, a long scale and seasonings, and provides accurate drought prediction for the growth of local crops. Meanwhile, the real-time rolling prediction capability is improved by locally installing soil humidity actual measurement equipment as a model, the soil humidity in each growth stage is predicted, and drought early warning is provided for the growth of crops in time according to the target soil humidity range of the crops in different growth stages.
The embodiment of the application provides a soil humidity prediction device for executing the soil humidity prediction method provided by the embodiment.
Fig. 3 is a schematic structural diagram of a soil humidity prediction apparatus according to an embodiment of the present application. The soil moisture prediction apparatus 30 includes: an acquisition module 301, a first determination module 302, a second determination module 303, and a prediction module 304.
The acquisition module is used for acquiring the current soil humidity, the current meteorological information and the crop growth stage information of the target area; the first determining module is used for determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area; the second determining module is used for determining the water demand of the crops in the current growth stage according to the information of the growth stage of the crops; and the prediction module is used for determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in the preset time step and the water demand of crops in the current growth stage.
Specifically, in an embodiment, the current weather information includes current weather data and current circulation data, and the first determining module is specifically configured to:
determining soil humidity change information of the target area in a preset time step according to the current meteorological data and the current circulation data of the target area;
and determining the soil humidity prediction information of the target area in the preset time step according to the current soil humidity of the target area and the soil humidity change information of the target area in the preset time step.
Specifically, in an embodiment, the obtaining module is specifically configured to:
dividing a target area into a plurality of area grids;
and acquiring the current soil humidity, the current meteorological information and the crop growth stage information of each regional grid.
Specifically, in an embodiment, the first determining module is specifically configured to:
determining a soil humidity space influence result of the target area according to the current soil humidity of each area grid;
and determining the soil humidity prediction information of the target area in the preset time step according to the soil humidity space influence result of the target area and the soil humidity change information of the target area in the preset time step.
Specifically, in one embodiment, the prediction module is specifically configured to:
determining growth stage change information of crops in a preset time step according to soil humidity prediction information of a target area in the preset time step and water demand of the crops in a current growth stage;
determining the water demand change information of the crops in a preset time step according to the growth stage change information of the crops in the preset time step;
and determining a soil humidity prediction result of the target area according to the water demand change information and the soil humidity prediction information of the crops in the preset time step.
Specifically, in an embodiment, the prediction module is further configured to:
determining a target soil humidity range of crops in a preset time step according to the growth stage change information of the crops in the preset time step;
and determining a crop irrigation strategy of the target area according to the soil humidity prediction result and the target soil humidity range of the target area.
Specifically, in one embodiment, the prediction module is specifically configured to:
judging whether the crop enters the next growth stage in a preset time step according to the growth stage change information of the crop in the preset time step;
if the crops enter the next growth stage within the preset time step, determining the proper soil humidity range of the next growth stage as a target soil humidity range of the crops within the preset time step;
if the crops do not enter the next growth stage within the preset time step, determining the proper soil humidity range of the current growth stage as the target soil humidity range of the crops within the preset time step.
The concrete manner in which the respective modules perform the operations in relation to the soil moisture prediction apparatus in the present embodiment has been described in detail in the embodiments concerning the method, and will not be explained in detail here.
The soil humidity prediction apparatus provided in the embodiments of the present application is configured to execute the soil humidity prediction method provided in the foregoing embodiments, and its implementation manner and principle are the same and are not repeated.
The embodiment of the application provides electronic equipment for executing the soil humidity prediction method provided by the embodiment.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 40 includes: at least one processor 41 and a memory 42.
The memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to perform the soil moisture prediction method as provided by the above embodiments.
The implementation manner and principle of the electronic device provided in the embodiment of the present application are the same, and are not repeated.
The embodiment of the application provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and when a processor executes the computer execution instructions, the soil humidity prediction method provided by any embodiment is realized.
The storage medium including the computer executable instructions in the embodiments of the present application may be used to store the computer executable instructions of the soil humidity prediction method provided in the foregoing embodiments, and the implementation manner and principle of the computer executable instructions are the same and are not repeated.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit 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, etc.) or a processor (processor) to perform part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working process of the above-described device may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A soil moisture prediction method, comprising:
acquiring current soil humidity, current meteorological information and crop growth stage information of a target area;
determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area;
determining the water demand of crops in the current growth stage according to the crop growth stage information;
and determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in a preset time step and the water demand of the crops in the current growth stage.
2. The method of claim 1, wherein the current weather information includes current weather data and current circulation data, and wherein determining the predicted soil moisture information for the target area within a preset time step based on the current soil moisture and the current weather information for the target area comprises:
determining soil humidity change information of the target area in a preset time step according to the current meteorological data and the current circulation data of the target area;
and determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity of the target area and the soil humidity change information of the target area in the preset time step.
3. The method of claim 2, wherein the obtaining current soil moisture, current weather information, and crop growth stage information for the target area comprises:
dividing the target area into a plurality of area grids;
and acquiring the current soil humidity, the current meteorological information and the crop growth stage information of each regional grid.
4. A method according to claim 3, wherein said determining the predicted information of the soil humidity of the target area within a preset time step based on the current soil humidity of the target area and the information of the change of the soil humidity of the target area within a preset time step comprises:
determining a soil humidity space influence result of the target area according to the current soil humidity of each area grid;
and determining the soil humidity prediction information of the target area in a preset time step according to the soil humidity space influence result of the target area and the soil humidity change information of the target area in the preset time step.
5. The method of claim 1, wherein the determining the predicted soil moisture content of the target area based on the predicted soil moisture content of the target area within a predetermined time step and the water demand of the crop at the current growth stage comprises:
determining growth stage change information of the crops in a preset time step according to soil humidity prediction information of the target area in the preset time step and water demand of the crops in a current growth stage;
determining the water demand change information of the crops in the preset time step according to the growth stage change information of the crops in the preset time step;
and determining a soil humidity prediction result of the target area according to the water demand change information of the crops in the preset time step and the soil humidity prediction information.
6. The method as recited in claim 5, further comprising:
determining a target soil humidity range of the crops in the preset time step according to the growth stage change information of the crops in the preset time step;
and determining a crop irrigation strategy of the target area according to the soil humidity prediction result of the target area and the target soil humidity range.
7. The method of claim 6, wherein said determining a target soil moisture range for the crop over the predetermined time step based on growth phase change information for the crop over the predetermined time step comprises:
judging whether the crop enters the next growth stage in the preset time step according to the growth stage change information of the crop in the preset time step;
if the crops enter the next growth stage within the preset time step, determining the proper soil humidity range of the next growth stage as a target soil humidity range of the crops within the preset time step;
and if the crops do not enter the next growth stage within the preset time step, determining the proper soil humidity range of the current growth stage as the target soil humidity range of the crops within the preset time step.
8. A soil moisture prediction apparatus, comprising:
the acquisition module is used for acquiring the current soil humidity, the current meteorological information and the crop growth stage information of the target area;
the first determining module is used for determining the soil humidity prediction information of the target area in a preset time step according to the current soil humidity and the current meteorological information of the target area;
the second determining module is used for determining the water demand of the crops in the current growth stage according to the crop growth stage information;
and the prediction module is used for determining a soil humidity prediction result of the target area according to the soil humidity prediction information of the target area in a preset time step and the water demand of the crops in the current growth stage.
9. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor implement the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310226978.9A CN116415488A (en) | 2023-03-08 | 2023-03-08 | Soil humidity prediction method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310226978.9A CN116415488A (en) | 2023-03-08 | 2023-03-08 | Soil humidity prediction method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116415488A true CN116415488A (en) | 2023-07-11 |
Family
ID=87050605
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310226978.9A Pending CN116415488A (en) | 2023-03-08 | 2023-03-08 | Soil humidity prediction method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116415488A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117933946A (en) * | 2024-03-20 | 2024-04-26 | 华兴国创(北京)科技有限公司 | Rural business management method based on big data |
-
2023
- 2023-03-08 CN CN202310226978.9A patent/CN116415488A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117933946A (en) * | 2024-03-20 | 2024-04-26 | 华兴国创(北京)科技有限公司 | Rural business management method based on big data |
| CN117933946B (en) * | 2024-03-20 | 2024-06-14 | 华兴国创(北京)科技有限公司 | Rural business management method based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11391569B2 (en) | Method and device for monitoring water volume change, computer device and storage medium | |
| EP3179319B1 (en) | Method for irrigation planning and system for its implementation | |
| Peeters et al. | Getis–Ord’s hot-and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data | |
| CN110597873A (en) | Precipitation data estimation method, precipitation data estimation device, precipitation data estimation equipment and storage medium | |
| CN112614133B (en) | Three-dimensional pulmonary nodule detection model training method and device without anchor point frame | |
| CN110134907B (en) | Rainfall missing data filling method and system and electronic equipment | |
| CN116415488A (en) | Soil humidity prediction method and device, electronic equipment and storage medium | |
| Yu et al. | Assessment of land degradation in the North China Plain driven by food security goals | |
| CN115344815B (en) | Natural runoff change attribution analysis method and system considering vegetation space change | |
| CN108021948A (en) | The method for inspecting and device of a kind of pest and disease damage | |
| CN109190827A (en) | A kind of furrow is laid out the Influencing Mechanism recognition methods to Rainfall-runoff | |
| CN113624694A (en) | Inversion method and device for atmospheric methane concentration | |
| CN113656515B (en) | Cultivated land crushing degree visualization method, system, electronic equipment and medium | |
| CN117192081A (en) | Construction method, related device and equipment of soil volume weight prediction model | |
| CN116883793A (en) | Multi-granularity space sampling method for precision evaluation of remote sensing classification result | |
| CN112560633A (en) | Plant key phenological period time point prediction method and system based on deep learning | |
| CN117236515A (en) | Method for predicting urban street tree breast diameter growth trend, prediction system and electronic equipment | |
| CN116108745A (en) | Multi-parameter calibration method for water environment model, terminal equipment and storage medium | |
| CN113240340B (en) | Soybean planting area analysis method, device, equipment and medium based on fuzzy classification | |
| CN114936765A (en) | Agricultural drought index construction method considering spatial heterogeneity | |
| CN117849908B (en) | Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field | |
| CN115600749B (en) | Groundwater level prediction method and device and electronic equipment | |
| CN110175215A (en) | A method of dividing geographical intermediate zone | |
| JP6366522B2 (en) | Radio wave environment analysis device, communication device, and radio wave environment analysis method | |
| CN117421568B (en) | Method, device, terminal and storage medium for positioning freezing and thawing monitoring points of side slope rock and soil mass |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |