A method for predicting plateau agricultural drought

Abstract

The present invention relates to the field of new information technology service technology, and discloses a method for predicting plateau agricultural drought, including the following steps: collecting and managing data on precipitation, temperature, air humidity and wind intensity in plateau planting areas through the Internet, and predicting the degree of drought in plateau planting areas through calculation of drought indicators; uploading the collected data and drought index to the decision-making layer of agricultural management, and the decision-making layer issues early warnings and controls agriculture; the crop management and control station manages the soil, crops and irrigation in the plateau planting areas in a targeted manner according to the early warning issued by the decision-making layer. The present invention samples and detects crops and soil after irrigation and fertilization by technicians, and uploads the detection data to the Internet management layer, so that the agricultural management decision-making layer can avoid making irrigation and fertilization decisions that are not conducive to crop growth and soil environment based on the data of the meteorological center and monitoring equipment.

Description

Technical Field

[0001] The present invention relates to the field of novel information technology service technology, and more specifically to a method for predicting plateau agricultural drought. Background Art

[0002] Plateau agriculture mainly refers to the development of agriculture in plateau areas. Drought prediction in plateau agriculture usually needs to take into account the special climate and geographical conditions of the plateau areas. Its drought prediction needs to comprehensively consider meteorological, hydrological, geological, agricultural and other factors, and use multi-source data and advanced technical means to provide timely and accurate early warning information for agricultural production, helping farmers and agricultural managers to make effective response measures.

[0003] The existing patent discloses a method for dynamic prediction of agricultural drought in the Tibetan Plateau (publication number CN116882534A), which involves the field of dynamic prediction of agricultural drought in the plateau. The patent realizes the prediction of drought in plateau agricultural areas, but when establishing a prediction model and using the prediction model to make decisions on irrigation and fertilization of planting areas, there will be irrigation and fertilization decisions that are not conducive to crops and the soil environment of the planting area, and the degree of this disadvantage is difficult to predict. Summary of the invention

[0004] The main technical problem solved by the present invention is to provide a method for predicting drought in plateau agriculture, which solves the problems in the above-mentioned background technology.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, a method for predicting highland agricultural drought comprises the following steps:

[0006] S1. Collect and manage data on precipitation, temperature, air humidity and wind intensity in plateau planting areas through the Internet, and predict the degree of drought in plateau planting areas by calculating drought indicators;

[0007] S2. Upload the collected data and drought index to the decision-making level of agricultural management, and the decision-making level will issue early warnings and control agriculture;

[0008] S3. The crop management and control station manages the soil, crops and irrigation in the plateau planting areas in a targeted manner according to the early warning issued by the decision-making level;

[0009] S4. In addition, the climate monitoring station in the plateau planting area will also conduct real-time monitoring of the temperature, wind speed and precipitation in the planting area, and the monitoring results will be directly fed back to the crop management and control station;

[0010] S5. The climate monitoring station in the planting area will establish a climate prediction model based on the climate information collected in real time;

[0011] S6. Climate monitoring stations in plateau planting areas use remote sensing detection technology to monitor vegetation index, soil moisture and surface temperature in the planting areas, and feed back the vegetation index, soil moisture and surface temperature data monitored in the planting areas together with the established climate prediction model to the Internet of Things Data Management Office;

[0012] S7. Technical personnel shall conduct sampling tests on the crops and soil quality after irrigation in the planting area, and upload the results of the sampling tests to the Internet Data Management Office.

[0013] Furthermore, in step S1, the data collected through the Internet include external data from the National Meteorological Center and remote sensing detection of plateau planting areas, internal data from climate monitoring, and feedback data from sampling and testing by technicians, including:

[0014] 1) The drought index monitored by the National Meteorological Center is compared with the drought index of the high-voltage planting area equipment itself to obtain the irrigation amount, irrigation frequency and fertilization frequency that are more suitable for the planting area;

[0015] 2) The technicians will test the crops and soil in the planting area and feedback the test results to the data management department;

[0016] 3) When the Internet data management terminal makes data predictions based on the received external and internal data and finds that the data is in line with climate factors but is not conducive to crop growth and soil environment in the planting area, the test data fed back by the technicians is transmitted to the decision-making layer for processing.

[0017] Furthermore, the crop growth and soil quality test data fed back by the technicians are used to judge the comprehensive index of the drought index of the National Meteorological Center data and the drought index of the climate monitoring station data. After being output to the decision-making level of agricultural management, whether it will be unfavorable to crop growth and soil environment, the judgment criteria are:

[0018]

[0019] Among them, g represents the correlation between soil and crop growth in the planting area:

[0020]

[0021] In the formula, x1 represents the chlorophyll content of the crops in the planting area; x2 represents the total content of nitrogen, phosphorus and potassium in the soil per unit area of ​​the planting area; k1 represents the drought index of the data from the National Meteorological Center; k2 represents the drought index of the data monitored by the equipment inside the planting area;

[0022] Where P represents the level of whether the drought index of the decision is unfavorable to crop production:

[0023] When P ≥ 7.4, it means that the drought index adopted by the decision-makers will be unfavorable to crop growth;

[0024] When P < 7.4, it means that the drought index adopted by decision-makers will be beneficial to crop growth.

[0025] Furthermore, in step S2, the decision-making layer of agricultural management issues warnings based on the set drought index thresholds and high temperature warnings, and the decision-making layer of agricultural management will also control the irrigation amount, irrigation frequency and fertilization frequency of the planting area based on the results of Internet data processing.

[0026] Furthermore, in step S5, each planting area is equipped with a separate climate monitoring station, which monitors the temperature, humidity, wind speed and precipitation of the planting area, and establishes a climate prediction model for the planting area based on these monitoring data. The specific prediction methods are:

[0027] 1) Recorded precipitation data to monitor precipitation conditions;

[0028] 2) Measure and monitor air humidity and temperature to assess soil moisture evaporation rate and vegetation transpiration;

[0029] 3) Monitor changes and intensity of wind speed and direction to predict the impact on precipitation distribution and evaporation rate.

[0030] Furthermore, the temperature, humidity, wind speed and precipitation monitored by the monitoring equipment in the planting area can be used to calculate the internal monitoring drought index of the planting area. The calculation of the drought index is as follows:

[0031]

[0032] In the formula, v represents the precipitation collected in the planting area; v0 represents the annual average precipitation collected in the planting area; u represents the humidity collected in the planting area; t represents the temperature collected in the planting area; f represents the wind speed collected in the planting area; K represents the drought index obtained by the temperature, humidity, wind speed and precipitation of the planting area. The larger the value of K, the drier the planting area, and the smaller the value of K, the wetter the planting area.

[0033] Furthermore, in step S6, the specific methods of using remote sensing detection technology are:

[0034] 1) Use multispectral image data obtained by drones to assess the health and coverage of vegetation. Changes in vegetation index can reflect vegetation demand for water and soil moisture conditions, and thus predict drought risks;

[0035] 2) Measure the temperature of the soil surface. The temperature change can reflect the moisture status of the soil, thereby indirectly evaluating the soil moisture content;

[0036] 3) Use thermal infrared remote sensing technology to obtain surface temperature data. Changes in surface temperature can reflect the soil moisture content and the intensity of vegetation transpiration, and thus determine the occurrence and extent of drought.

[0037] The beneficial effects of the plateau agricultural drought prediction method of the present invention are:

[0038] 1. The present invention collects data from the meteorological center and collates it with the data collected by the equipment in the planting area, and transmits the collating results to the agricultural management decision-making so as to issue early warnings, and carry out targeted irrigation and fertilization according to the content of the early warnings.

[0039] 2. The present invention allows technicians to sample and test crops and soil after irrigation and fertilization, and upload the test data to the Internet management level. This can prevent the agricultural management decision-making level from making irrigation and fertilization decisions that are not conducive to crop growth and soil environment based on data from meteorological centers and monitoring equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0041] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0043] like Figure 1 According to one aspect of the present invention, a method for predicting drought in plateau agriculture is provided, comprising the following steps:

[0044] Step 1: Collect and manage data on precipitation, temperature, air humidity and wind intensity in the plateau planting area through the Internet, and predict the degree of drought in the plateau planting area by calculating drought indicators;

[0045] Step 2: Upload the collected data and drought index to the decision-making level of agricultural management, and the decision-making level will issue early warnings and control agriculture;

[0046] Step 3: The crop management and control station manages the soil, crops and irrigation in the plateau planting areas in a targeted manner according to the early warning issued by the decision-making level;

[0047] Step 4: The climate monitoring station in the plateau planting area will also monitor the temperature, wind speed and precipitation in the planting area in real time, and the monitoring results will be directly fed back to the crop management and control station;

[0048] Step 5: The climate monitoring station in the planting area will establish a climate prediction model based on the climate information collected in real time;

[0049] Step 6: The climate monitoring station in the plateau planting area uses remote sensing detection technology to monitor the vegetation index, soil moisture and surface temperature of the planting area, and feeds back the vegetation index, soil moisture and surface temperature data monitored in the planting area together with the established climate prediction model to the Internet of Things Data Management Office;

[0050] Step 7. The technical staff shall conduct sampling tests on the crops and soil quality after irrigation in the planting area, and upload the results of the sampling tests to the Internet Data Management Office.

[0051] In this embodiment, the data collected through the Internet include external data from the National Meteorological Center, remote sensing detection of plateau planting areas, internal data from climate monitoring, and feedback data from sampling and detection by technicians, including:

[0052] 1) The drought index monitored by the National Meteorological Center is compared with the drought index of the high-voltage planting area equipment itself to obtain the irrigation amount, irrigation frequency and fertilization frequency that are more suitable for the planting area;

[0053] 2) The technicians will test the crops and soil in the planting area and feedback the test results to the data management department;

[0054] 3) When the Internet data management terminal makes data predictions based on the received external and internal data and finds that the data is in line with climate factors but is not conducive to crop growth and soil environment in the planting area, the test data fed back by the technicians is transmitted to the decision-making layer for processing.

[0055] This method involves technicians sampling and testing crops and soil after irrigation and fertilization, and uploading the test data to the Internet management level. This can prevent agricultural management decision-makers from making irrigation and fertilization decisions that are not conducive to crop growth and soil environment based on data from meteorological centers and monitoring equipment.

[0056] In this embodiment, the crop growth and soil quality test data fed back by the technicians are used to judge the comprehensive index after the drought index of the National Meteorological Center data and the drought index of the climate monitoring station data are collated. After being output to the decision-making level of agricultural management, whether it will be unfavorable to crop growth and soil environment, the judgment criteria are as follows:

[0057]

[0058] In the formula, x1 represents the chlorophyll content of the crops in the planting area; x2 represents the total content of nitrogen, phosphorus and potassium in the soil per unit area of ​​the planting area; g represents the correlation between soil and crop growth in the planting area; k1 represents the drought index of the data from the National Meteorological Center; k2 represents the drought index of the data monitored by the equipment inside the planting area;

[0059] Where P represents the level of whether the drought index of the decision is unfavorable to crop production:

[0060] When P ≥ 7.4, it means that the drought index adopted by the decision-makers will be unfavorable to crop growth;

[0061] When P < 7.4, it means that the drought index adopted by decision-makers will be beneficial to crop growth.

[0062] The drought index k1=93 can be calculated by collecting meteorological data from the National Meteorological Center; the drought index k2=102.2 can be calculated by collecting meteorological data from monitoring equipment in the planting area; among them, the chlorophyll content of the plant detected by the technicians is x1=25, and the total content of nitrogen, phosphorus and potassium in the soil detected is x2=9.4, so:

[0063]

[0064] Then, through the sampling of the above samples and the proofreading of the data, when P=6.1, the agricultural management decision-makers can directly adopt the data of monitoring equipment and meteorological center for irrigation and fertilization without any phenomenon that is not conducive to the growth of crops.

[0065] In this embodiment, the decision-making layer of agricultural management issues warnings based on the set drought index threshold and high temperature warning, and the decision-making layer of agricultural management will also control the irrigation volume, irrigation frequency and fertilization frequency of the planting area based on the results of Internet data processing. Each planting area is equipped with a separate climate monitoring station, which monitors the temperature, humidity, wind speed and precipitation of the planting area, and establishes a climate prediction model for the planting area based on these monitoring data. The specific prediction methods are:

[0066] 1) Recorded precipitation data to monitor precipitation conditions;

[0067] 2) Measure and monitor air humidity and temperature to assess soil moisture evaporation rate and vegetation transpiration;

[0068] 3) Monitor changes and intensity of wind speed and direction to predict the impact on precipitation distribution and evaporation rate.

[0069] The data collected by the equipment in the planting area is proofread through the monitoring station, and the proofreading results are transmitted to the agricultural management decision-making in order to issue early warnings and carry out targeted irrigation and fertilization according to the content of the early warnings.

[0070] In this embodiment, the internal monitoring drought index of the planting area can be calculated by the temperature, humidity, wind speed and precipitation monitored by the monitoring equipment of the planting area itself. The calculation of the drought index is:

[0071]

[0072] In the formula, v represents the precipitation collected in the planting area; v0 represents the annual average precipitation collected in the planting area; u represents the humidity collected in the planting area; t represents the temperature collected in the planting area; f represents the wind speed collected in the planting area; K represents the drought index obtained by the temperature, humidity, wind speed and precipitation of the planting area. The larger the value of K, the drier the planting area, and the smaller the value of K, the wetter the planting area.

[0073] The monitoring equipment of the testing station is used to monitor the climate of the planting area. It is found that the humidity of the planting area is u = 0.15; the precipitation of the planting area is v = 350; the annual average humidity of the planting area is v0 = 380; the temperature of the planting area is t = 34.5; the wind speed of the device area is f = 4.6, and:

[0074]

[0075] That, the drought index K of the planting area monitored by this monitoring station is 102.2.

[0076] In this embodiment, the specific methods of using remote sensing detection technology are:

[0077] 1) Use multispectral image data obtained by drones to assess the health and coverage of vegetation. Changes in vegetation index can reflect vegetation demand for water and soil moisture conditions, and thus predict drought risks;

[0078] 2) Measure the temperature of the soil surface. The temperature change can reflect the moisture status of the soil, thereby indirectly evaluating the soil moisture content;

[0079] 3) Use thermal infrared remote sensing technology to obtain surface temperature data. Changes in surface temperature can reflect the soil moisture content and the intensity of vegetation transpiration, and thus determine the occurrence and extent of drought.

[0080] The application of remote sensing detection technology can enable agricultural managers and decision makers to obtain extensive surface information in plateau areas in a timely manner, helping them to better monitor and predict the possibility of drought, so as to take corresponding response measures to ensure the stability and sustainability of agricultural production.

[0081] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention also fall within the protection scope of the present invention.

Claims

1. A method for predicting highland agricultural drought, characterized in that: The following steps are involved: S1. Collect and manage data on precipitation, temperature, air humidity and wind intensity in plateau planting areas through the Internet, and predict the degree of drought in plateau planting areas by calculating drought indicators; S2. Upload the collected data and drought index to the decision-making level of agricultural management, and the decision-making level will issue early warnings and control agriculture; S3. The crop management and control station manages the soil, crops and irrigation in the plateau planting areas in a targeted manner according to the early warning issued by the decision-making level; S4. In addition, the climate monitoring station in the plateau planting area will also conduct real-time monitoring of the temperature, wind speed and precipitation in the planting area, and the monitoring results will be directly fed back to the crop management and control station; S5. The climate monitoring station in the planting area will establish a climate prediction model based on the climate information collected in real time; S6. Climate monitoring stations in plateau planting areas use remote sensing detection technology to monitor vegetation index, soil moisture and surface temperature in the planting areas, and feed back the vegetation index, soil moisture and surface temperature data monitored in the planting areas together with the established climate prediction model to the Internet of Things Data Management Office; S7. Technical personnel shall conduct sampling tests on the crops and soil quality after irrigation in the planting area, and upload the results of the sampling tests to the Internet Data Management Office.

2. The method for predicting highland agricultural drought according to claim 1, characterized in that: In step S1, the data collected through the Internet include external data from the National Meteorological Center, remote sensing detection of plateau planting areas, internal data from climate monitoring, and feedback data from sampling and detection by technicians, including: 1) The drought index monitored by the National Meteorological Center is compared with the drought index of the high-voltage planting area equipment itself to obtain the irrigation amount, irrigation frequency and fertilization frequency that are more suitable for the planting area; 2) The technicians will test the crops and soil in the planting area and feedback the test results to the data management department; 3) When the Internet data management terminal makes data predictions based on the received external and internal data and finds that the data is in line with climate factors but is not conducive to crop growth and soil environment in the planting area, the test data fed back by the technicians is transmitted to the decision-making layer for processing.

3. The method for predicting highland agricultural drought according to claim 2, characterized in that: The crop growth and soil quality test data fed back by the technicians are used to judge the comprehensive index after the drought index of the National Meteorological Center data and the drought index of the climate monitoring station data are collated. After being output to the decision-making level of agricultural management, whether it will be unfavorable to crop growth and soil environment is determined. The judgment criteria are: Among them, g represents the correlation between soil and crop growth in the planting area: In the formula, x1 represents the chlorophyll content of the crops in the planting area; x2 represents the total content of nitrogen, phosphorus and potassium in the soil per unit area of ​​the planting area; k1 represents the drought index of the data from the National Meteorological Center; k2 represents the drought index of the data monitored by the equipment inside the planting area; Where P represents the level of whether the drought index of the decision is unfavorable to crop production: When P ≥ 7.4, it means that the drought index adopted by the decision-makers will be unfavorable to crop growth; When P < 7.4, it means that the drought index adopted by decision-makers will be beneficial to crop growth.

4. The method for predicting highland agricultural drought according to claim 1, characterized in that: In step S2, the decision-making layer of agricultural management issues warnings based on the set drought index thresholds and high temperature warnings, and the decision-making layer of agricultural management will also control the irrigation amount, irrigation frequency and fertilization frequency of the planting area based on the results of Internet data processing.

5. The method for predicting highland agricultural drought according to claim 1, characterized in that: In step S5, each planting area is equipped with a separate climate monitoring station, which monitors the temperature, humidity, wind speed and precipitation of the planting area, and establishes a climate prediction model for the planting area based on these monitoring data. The specific prediction methods are: 1) Recorded precipitation data to monitor precipitation conditions; 2) Measure and monitor air humidity and temperature to assess soil moisture evaporation rate and vegetation transpiration; 3) Monitor changes and intensity of wind speed and direction to predict the impact on precipitation distribution and evaporation rate.

6. The method for predicting highland agricultural drought according to claim 5, characterized in that: The internal monitoring drought index of the planting area can be calculated by the temperature, humidity, wind speed and precipitation monitored by the monitoring equipment in the planting area. The calculation of the drought index is as follows: In the formula, v represents the precipitation collected in the planting area; v0 represents the annual average precipitation collected in the planting area; u represents the humidity collected in the planting area; t represents the temperature collected in the planting area; f represents the wind speed collected in the planting area; K represents the drought index obtained by the temperature, humidity, wind speed and precipitation of the planting area. The larger the value of K, the drier the planting area, and the smaller the value of K, the wetter the planting area.

7. The method for predicting highland agricultural drought according to claim 1, characterized in that: In step S6, the specific methods of using remote sensing detection technology are: 1) Use multispectral image data obtained by drones to assess the health and coverage of vegetation. Changes in vegetation index can reflect vegetation demand for water and soil moisture conditions, and thus predict drought risks; 2) Measure the temperature of the soil surface. The temperature change can reflect the moisture status of the soil, thereby indirectly evaluating the soil moisture content; 3) Use thermal infrared remote sensing technology to obtain surface temperature data. Changes in surface temperature can reflect the soil moisture content and the intensity of vegetation transpiration, and thus determine the occurrence and extent of drought.

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