Intelligent control method and system for supplementary irrigation and topdressing of dry land based on Internet of Things

Abstract

The invention discloses an intelligent control method and system for dry land supplementary irrigation and topdressing based on the Internet of Things, and relates to the technical field of program control systems. According to the method, firstly, dry land subordinate areas are divided, so that accurate monitoring and acquisition of soil environment data and plant growth data of each dry land sub-area are facilitated, and the soil moisture condition and the plant growth state of each dry land sub-area can be known more accurately; the soil humidity adjusting index and the soil fertility adjusting index of each dry land sub-region are obtained through analysis, a scientific basis can be provided for formulating an accurate fertilization and irrigation strategy, the situation of insufficient fertilization and irrigation is reduced, resources are saved, and the environmental influence is reduced. And finally, performing analysis to obtain supplementary irrigation and topdressing parameters of each dry land sub-region and adjusting and controlling the soil humidity and the soil fertility of each dry land sub-region, and dynamically adjusting according to real-time meteorological data to ensure that the soil humidity and the soil fertility of each dry land sub-region are optimally managed and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of program control systems, and in particular to an intelligent control method and system for supplementary irrigation and fertilization of dry land based on the Internet of Things. Background Art

[0002] In agricultural production, insufficient soil moisture may lead to restricted crop growth, deterioration of soil quality, and possibly deterioration of the ecological environment. Insufficient fertilizer will directly affect crop yields, so supplementary irrigation and topdressing of the soil are particularly important. Supplementary irrigation and topdressing methods usually rely on farmers' experience and intuition, lack scientific basis and data support, and are prone to cause problems of excessive or insufficient nutrients. Conventional regular irrigation and fertilization can easily lead to waste of resources.

[0003] The prior art, such as the invention patent announcement with announcement number: CN104460582B, discloses an IoT intelligent irrigation and fertilization control method and system based on fuzzy control. The method includes: (1) data collection and processing: according to the water demand law of crops and fertilization formula, as well as the collected soil temperature and humidity, soil nutrients, air temperature and humidity, wind speed, rainfall, flow, pool water level and pipe network pressure data, the limits of soil moisture, nutrients and pool water level and the irrigation and fertilization plan time are set and stored in the database; (2) intelligent control: reading the corresponding data in the database, using fuzzy control algorithm and water-fertilizer coupling model to intelligently control the irrigation valve and fertilization valve; by comparing the current pool water level with the set pool water level limit, the start and stop of the water pump are intelligently controlled; and the PID control algorithm is used to control the speed of the water pump. The invention has the characteristics of excellent performance, complete functions, strong scalability, easy operation and management, and uses fuzzy control to achieve intelligent management and control.

[0004] The prior art, such as the invention patent announcement with announcement number: CN109116827B, discloses a solar greenhouse water-fertilizer integrated irrigation control method and device based on the Internet of Things. The method includes: obtaining greenhouse meteorological parameters and target substrate moisture information of the solar greenhouse, the greenhouse meteorological parameters include air temperature information, air humidity information and light intensity information; inputting the greenhouse meteorological parameters into a trained transpiration rate model to obtain the transpiration rate of the solar greenhouse, the trained transpiration rate model is trained based on the greenhouse meteorological parameters; obtaining the lower limit value of the substrate moisture of the solar greenhouse, the lower limit value of the substrate moisture is obtained according to the target substrate moisture information and the transpiration rate; obtaining the real-time substrate moisture value of the solar greenhouse, and deciding a target irrigation plan according to the lower limit value of the substrate moisture and the real-time substrate moisture value, so as to provide water-fertilizer integrated irrigation control for crops in the solar greenhouse.

[0005] Combining the above schemes, it is found that currently in the field of supplementary irrigation and fertilization control, usually only the growth environment is analyzed, and then the water and fertilizer irrigation is controlled. This may result in insufficient fertilization and irrigation, and it is impossible to improve the accuracy of soil fertilization and soil irrigation. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent control method and system for dryland supplementary irrigation and fertilization based on the Internet of Things, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solutions: an intelligent control method for dryland irrigation and fertilization based on the Internet of Things, including establishing an agricultural monitoring platform, dividing the dryland areas, obtaining dryland sub-areas, and monitoring and collecting soil environmental data and plant growth data of each dryland sub-area.

[0008] The soil environment data and plant growth data of each dryland sub-region are analyzed to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region.

[0009] According to the soil moisture regulation index and soil fertility regulation index of each dryland sub-region, the meteorological data of the dryland area in the cloud server are synchronously extracted, and the supplementary irrigation and topdressing parameters of each dryland sub-region are obtained through comprehensive analysis, and the soil moisture and soil fertility of each dryland sub-region are regulated and controlled.

[0010] Furthermore, the soil environmental data of each dryland sub-region includes the clay particle percentage of the soil of each dryland sub-region and the average total organic matter content of the soil of each dryland sub-region.

[0011] The plant growth data of each dryland sub-region includes the vegetation coverage rate of each dryland sub-region and the average vegetation chlorophyll content of each dryland sub-region.

[0012] Further, the analyzing the soil environment data and the plant growth data of each dryland sub-region includes analyzing the soil environment data of each dryland sub-region and analyzing the plant growth data of each dryland sub-region.

[0013] The specific analysis process of analyzing the soil environmental data of each dryland sub-region is to randomly arrange a number of soil monitoring points in each dryland sub-region, monitor and collect the soil temperature and humidity of each soil monitoring point, and perform mean processing to obtain the average soil humidity and the average soil temperature of each dryland sub-region.

[0014] The percentage of clay particles in the soil of each dryland sub-region and the average total organic matter content of the soil of each dryland sub-region were statistically analyzed. Combined with the average soil moisture and the average soil temperature of each dryland sub-region, the soil moisture regulation index of each dryland sub-region was obtained through comprehensive analysis.

[0015] Furthermore, the plant growth data of each dryland sub-region is analyzed, and the specific analysis process is: randomly collecting plant samples from each dryland sub-region, and analyzing to obtain the average total plant nutrient content of each dryland sub-region.

[0016] The soil pH value of each soil monitoring point was monitored and collected, and averaged to obtain the average soil pH value of each dryland sub-area.

[0017] The vegetation coverage rate of each dryland sub-region and the average vegetation chlorophyll content of each dryland sub-region were extracted from the plant growth data of each dryland sub-region. The soil fertility regulation index of each dryland sub-region was obtained by analysis based on the average plant nutrient content and the average soil pH value of each dryland sub-region.

[0018] Furthermore, the analysis obtains supplementary irrigation and fertilization parameters for each dryland sub-region, and the specific analysis process is: the supplementary irrigation and fertilization parameters for each dryland sub-region include soil moisture adjustment index values ​​and soil fertility adjustment index values ​​for each dryland sub-region.

[0019] The meteorological data of the dryland area in the cloud server is extracted to obtain the meteorological data set of the dryland area, the meteorological comparison data set of the dryland area stored in the agricultural monitoring database is extracted, and the meteorological characterization value of the dryland area is obtained through comprehensive analysis.

[0020] According to the soil moisture regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the soil moisture regulation index value of each dryland sub-region is analyzed and obtained.

[0021] According to the soil fertility regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the soil fertility regulation index value of each dryland sub-region is analyzed and obtained.

[0022] Furthermore, the regulating and controlling the soil moisture and soil fertility of each dryland sub-region includes regulating and controlling the soil moisture of each dryland sub-region and regulating and controlling the soil fertility of each dryland sub-region.

[0023] The soil moisture of each dryland sub-region is regulated and controlled in a specific process of comparing the soil moisture regulation index value of each dryland sub-region with the set soil moisture regulation index threshold value of each dryland sub-region; if the soil moisture regulation index value of a dryland sub-region is higher than the set soil moisture regulation index threshold value of the dryland sub-region, the soil moisture of the dryland sub-region is regulated and controlled.

[0024] The difference between the soil moisture adjustment index value of the dryland sub-region and the set soil moisture adjustment index threshold of the dryland sub-region is recorded as the soil moisture correction index of the dryland sub-region. The soil irrigation parameters of the dryland sub-region are obtained according to the soil moisture correction index matching, and the dryland sub-region is irrigated according to the soil irrigation parameters of the dryland sub-region.

[0025] Furthermore, the soil fertility of each dryland sub-region is regulated and controlled, and the specific process is: comparing the soil fertility regulation index value of each dryland sub-region with the set soil fertility regulation index threshold of each dryland sub-region; if the soil fertility regulation index value of a dryland sub-region is higher than the set soil fertility regulation index threshold of the dryland sub-region, the soil fertility of the dryland sub-region is regulated and controlled.

[0026] The difference between the soil fertility adjustment index value of the dryland sub-region and the set soil fertility adjustment index threshold value of the dryland sub-region is recorded as the soil fertility correction index of the dryland sub-region. The soil fertilization parameters of the dryland sub-region are obtained according to the soil fertilization correction index matching, and the dryland sub-region is fertilized according to the soil fertilization parameters of the dryland sub-region.

[0027] Furthermore, the soil moisture adjustment index value of each dryland sub-region has the following specific analysis conditions:

[0028] In the formula, α i represents the soil moisture regulation index value of the i-th dryland sub-region, β i represents the soil moisture regulation index of the i-th dryland sub-region, represents the weight factor corresponding to the set soil moisture adjustment index, γ represents the meteorological characterization value of the dryland area, represents the weight factor corresponding to the set meteorological characterization value, i represents the number of each dryland sub-region, i=1,2,3,…n, and n represents the total number of dryland sub-regions.

[0029] Furthermore, the soil fertility adjustment index value of each dryland sub-region has the following specific analysis conditions:

[0030] In the formula, ρ i represents the soil fertility regulation index value of the i-th dryland sub-region, δ i represents the soil fertility regulation index of the i-th dryland sub-region, represents the weight factor corresponding to the set soil fertility regulation index, γ represents the meteorological characterization value of the dryland area, represents the weight factor corresponding to the set meteorological characterization value, i represents the number of each dryland sub-region, i=1,2,3,…n, n represents the total number of dryland sub-regions, and e represents a natural constant.

[0031] The second aspect of the present invention provides an intelligent control system for dryland irrigation and fertilization based on the Internet of Things, including: an agricultural monitoring platform for establishing an agricultural monitoring platform, dividing the dryland areas to obtain dryland sub-areas, and monitoring and collecting soil environmental data and plant growth data of each dryland sub-area.

[0032] The supplementary irrigation and topdressing analysis module is used to analyze the soil environment data and plant growth data of each dryland sub-region to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region.

[0033] The supplementary irrigation and fertilization adjustment module is used to synchronously extract the meteorological data of the dryland area in the cloud server according to the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-area, comprehensively analyze and obtain the supplementary irrigation and fertilization parameters of each dryland sub-area, and adjust and control the soil moisture and soil fertility of each dryland sub-area.

[0034] The present invention has the following beneficial effects:

[0035] (1) The present invention provides an intelligent control method and system for supplementary irrigation and fertilization in dryland based on the Internet of Things. First, the dryland area is divided, and then the soil environment data and plant growth data of each dryland sub-area are collected and analyzed. The soil moisture and plant growth status of each dryland sub-area can be understood more accurately, and then the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-area are obtained. The index is combined with the meteorological data of the dryland area for adjustment and control, thereby reducing the situation of insufficient fertilization and irrigation.

[0036] (2) The present invention divides the dryland areas into dryland sub-areas, monitors and collects soil environment data and plant growth data of each dryland sub-area, and timely monitors soil moisture and fertility conditions, which helps to detect possible drought or soil degradation problems at an early stage. It can also accurately analyze the amount of supplementary irrigation and topdressing parameters required for each dryland sub-area, which helps to reduce the situation of insufficient fertilization and irrigation, and maximize the efficiency and yield of crop growth.

[0037] (3) The present invention obtains the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-region through analysis, which can improve the accuracy of configuring and utilizing water resources and fertilizer resources, reduce waste and improve the utilization rate of water resources and fertilizer resources. Through refined management, the dependence on water resources and fertilizers in the agricultural production process can be reduced, while reducing the environmental load and improving the yield and quality of crops.

[0038] (4) The present invention obtains the supplementary irrigation and topdressing parameters of each dryland sub-region through analysis, and adjusts and controls the soil moisture and soil fertility of each dryland sub-region. The analysis combined with meteorological data can provide more scientific and accurate guidance for the soil fertility and humidity regulation of each dryland sub-region, thereby improving the accuracy and effect of soil fertilization and irrigation, and further helping to optimize the growth environment of crops, improve yield and quality, and reduce unnecessary waste of resources.

[0039] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0041] Figure 2 It is a schematic diagram of system module connection of the present invention;

[0042] Figure 3 Example image of adjusted indicator values ​​for soil fertility. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 As shown, the first aspect of the embodiment of the present invention provides a technical solution: an intelligent control method for dryland irrigation and fertilization based on the Internet of Things, including establishing an agricultural monitoring platform, dividing the dryland area to obtain dryland sub-areas, and monitoring and collecting soil environment data and plant growth data of each dryland sub-area.

[0045] It needs to be explained that the establishment of an agricultural monitoring platform is a system that integrates modern science and technology and data analysis technology to monitor and evaluate soil environmental data and plant growth data in drylands in real time and accurately.

[0046] The soil environment data and plant growth data of each dryland sub-region are analyzed to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region.

[0047] According to the soil moisture regulation index and soil fertility regulation index of each dryland sub-region, the meteorological data of the dryland area in the cloud server are synchronously extracted, and the supplementary irrigation and topdressing parameters of each dryland sub-region are obtained through comprehensive analysis, and the soil moisture and soil fertility of each dryland sub-region are regulated and controlled.

[0048] Specifically, the soil environmental data of each dryland sub-region includes the clay particle percentage of the soil of each dryland sub-region and the average total organic matter content of the soil of each dryland sub-region.

[0049] It should be added that the percentage of clay particles in the soil of each dryland sub-region represents the ratio of the clay particles in the soil of each dryland sub-region to the total particles in the soil of each dryland sub-region, soil samples are collected from multiple locations in each dryland sub-region, mixed samples are obtained to obtain an integral sample, the total mass of the integral sample is obtained by analysis, the mass of the clay particles in the integral sample is obtained by sedimentation method, and the ratio of the mass of the clay particles in the integral sample to the total mass of the integral sample is taken as the percentage of clay particles in the soil of each dryland sub-region.

[0050] It should be added that the average total organic matter content of the soil in each dryland sub-region refers to the total amount of organic matter in the soil. Soil samples are collected from multiple locations in each dryland sub-region, mixed and sampled to obtain an integral sample, the integral sample is heated to burn the organic matter, and then the difference between the mass of the residual integral sample after burning and the mass of the integral sample is measured and recorded as the total organic matter content of the integral sample. Samples are collected and analyzed multiple times to obtain the total organic matter content, and the average value is taken to obtain the average total organic matter content of the soil in each dryland sub-region.

[0051] The plant growth data of each dryland sub-region includes the vegetation coverage rate of each dryland sub-region and the average vegetation chlorophyll content of each dryland sub-region.

[0052] It should be added that the vegetation coverage rate of each dryland sub-area refers to the percentage of vegetation coverage in each dryland sub-area. High-resolution aerial images were taken using drones, and the vegetation coverage rate of each dryland sub-area was analyzed using image processing software.

[0053] It should be added that the average vegetation chlorophyll content of each dryland sub-region refers to the average mass or concentration of chlorophyll in the vegetation in the dryland sub-region. Plant samples are collected from different locations in each dryland sub-region, and the chlorophyll content in each plant sample is determined by solvent extraction method. The average value is recorded as the average vegetation chlorophyll content of each dryland sub-region.

[0054] Specifically, analyzing the soil environment data and the plant growth data of each dryland sub-region includes analyzing the soil environment data of each dryland sub-region and analyzing the plant growth data of each dryland sub-region.

[0055] The specific analysis process of analyzing the soil environmental data of each dryland sub-region is to randomly arrange a number of soil monitoring points in each dryland sub-region, monitor and collect the soil temperature and humidity of each soil monitoring point, and perform mean processing to obtain the average soil humidity and the average soil temperature of each dryland sub-region.

[0056] The percentage of clay particles in the soil of each dryland sub-region and the average total organic matter content of the soil of each dryland sub-region were statistically analyzed. Combined with the average soil moisture and the average soil temperature of each dryland sub-region, the soil moisture regulation index of each dryland sub-region was obtained through comprehensive analysis.

[0057] It should be noted that the soil moisture regulation index of each dryland sub-region represents a numerical quantification result obtained by analyzing the soil environment data of each dryland sub-region, and is used to quantify the influence of the soil environment of each dryland sub-region on soil moisture regulation.

[0058] In this embodiment, the soil moisture adjustment index of each dryland sub-region is obtained using the following analysis conditions, specifically:

[0059]

[0060] In the formula, β i represents the soil moisture regulation index of the i-th dryland sub-region, b i represents the clay particle percentage of the soil in the i-th dryland sub-region, Δb represents the set reference clay particle percentage, θ1 represents the influence factor corresponding to the set clay particle percentage, c i represents the average total organic matter content of the soil in the i-th dryland sub-region, Δc represents the set reference total organic matter content, θ2 represents the influencing factor corresponding to the set total organic matter content, and d i represents the average soil moisture of the i-th dryland sub-region, Δd represents the set reference soil moisture, θ3 represents the influencing factor corresponding to the set soil moisture, and f i represents the average soil temperature of the i-th dryland sub-region, Δf represents the set reference soil temperature, θ4 represents the influencing factor corresponding to the set soil temperature, i represents the number of each dryland sub-region, i=1,2,3,…n, n represents the total number of dryland sub-regions, and e represents a natural constant.

[0061] It should be noted that, in this embodiment, the influencing factors corresponding to the preset clay particle percentage, the influencing factors corresponding to the total content of organic matter, the influencing factors corresponding to soil moisture, and the influencing factors corresponding to soil temperature are obtained from the agricultural monitoring database, and the influencing factors corresponding to the clay particle percentage, the influencing factors corresponding to the total content of organic matter, the influencing factors corresponding to soil moisture, and the influencing factors corresponding to soil temperature are all numbers between 0 and 1.

[0062] In a specific embodiment, based on the relationship between the historical clay particle percentage of the soil of each dryland sub-region and the soil moisture adjustment index of each dryland sub-region, a mapping set of the clay particle percentage of the soil of each dryland sub-region and the influencing factors corresponding to the corresponding clay particle percentage is constructed, and the real-time clay particle percentage of the soil of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding clay particle percentage.

[0063] In a specific embodiment, based on the relationship between the historical average total organic matter content of the soil of each dryland sub-region and the soil moisture regulation index of each dryland sub-region, a mapping set of the average total organic matter content of the soil of each dryland sub-region and the influencing factors corresponding to the corresponding total organic matter content is constructed, and the real-time average total organic matter content of the soil of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding total organic matter content.

[0064] In a specific embodiment, based on the relationship between the historical average soil moisture of each dryland sub-region and the soil moisture adjustment index of each dryland sub-region, a mapping set of the average soil moisture of each dryland sub-region and the influencing factors corresponding to the corresponding soil moisture is constructed, and the real-time average soil moisture of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding soil moisture.

[0065] In a specific embodiment, based on the relationship between the historical average soil temperature of each dryland sub-region and the soil moisture adjustment index of each dryland sub-region, a mapping set of the average soil temperature of each dryland sub-region and the influencing factors corresponding to the corresponding soil temperature is constructed, and the real-time average soil temperature of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding soil temperature.

[0066] It should be explained that the analysis method of this embodiment obtains the soil moisture regulation index of each dryland sub-region by combining the clay particle percentage of the soil of each dryland sub-region, the average total organic matter content of the soil, the average soil moisture and the average soil temperature for analysis, which helps to improve the accuracy of the soil moisture condition assessment of the dryland sub-region and provide data support for soil irrigation.

[0067] It should be added that the clay particle percentage of the soil in each dryland sub-region, the average total organic matter content of the soil, the average soil moisture and the average soil temperature in this formula are related and do not exist independently. For example, the clay particle percentage is usually positively correlated with the total organic matter content. Soils with high clay particle percentages usually have higher total organic matter content because clay particles have a larger surface area, which is conducive to the adsorption and retention of organic matter. The total organic matter content is usually positively correlated with soil moisture because a moist environment is conducive to the decomposition and accumulation of organic matter. Comprehensive analysis of the clay particle percentage of the soil in each dryland sub-region, the average total organic matter content of the soil, the average soil moisture and the average soil temperature can more accurately evaluate the influence of soil moisture on soil moisture regulation.

[0068] In this embodiment, analyzing the percentage of clay particles in the soil of each dryland sub-region helps to improve the accuracy of soil irrigation and the utilization rate of water in the soil. The total organic matter content can improve the soil structure and increase the porosity and water retention capacity of the soil. Analyzing the average total organic matter content of the soil of each dryland sub-region helps to more accurately analyze the soil's demand for water. Analyzing the average soil moisture of each dryland sub-region can directly guide irrigation scheduling and reduce the situation where the soil is too dry or too wet. Analyzing the average soil temperature of each dryland sub-region can help determine the irrigation time and method. Analyzing the soil moisture adjustment index of each dryland sub-region can provide a scientific basis for soil irrigation.

[0069] Specifically, the plant growth data of each dryland sub-region is analyzed, and the specific analysis process is: randomly collecting plant samples from each dryland sub-region, and analyzing them to obtain the average total plant nutrient content of each dryland sub-region.

[0070] It should be added that the average total plant nutrient content of each dryland sub-region refers to the total mass or concentration of all nutrients in the soil or vegetation in each dryland sub-region. The average total plant nutrient content of each dryland sub-region is obtained by collecting multiple plant tissue samples, such as leaves, stems, and roots, from each dryland sub-region and analyzing them through digestion methods.

[0071] It should be added that common digestion methods include acid digestion, alkaline digestion or oxidative digestion.

[0072] The soil pH value of each soil monitoring point was monitored and collected, and averaged to obtain the average soil pH value of each dryland sub-area.

[0073] The vegetation coverage rate of each dryland sub-region and the average vegetation chlorophyll content of each dryland sub-region were extracted from the plant growth data of each dryland sub-region. The soil fertility regulation index of each dryland sub-region was obtained by analysis based on the average plant nutrient content and the average soil pH value of each dryland sub-region.

[0074] It should be noted that the soil fertility regulation index of each dryland sub-region represents a numerical quantification result obtained by analyzing the plant growth data of each dryland sub-region, and is used to quantify the influence of plant growth in each dryland sub-region on soil fertility regulation.

[0075] In this embodiment, the soil fertility adjustment index of each dryland sub-region is obtained using the following analysis conditions, specifically:

[0076]

[0077] In the formula, δ i represents the soil fertility regulation index of the i-th dryland sub-region, g i represents the vegetation coverage rate of the i-th dryland sub-region, Δg represents the set reference vegetation coverage rate, ω1 represents the influencing factor corresponding to the set vegetation coverage rate, and h i represents the average vegetation chlorophyll content of the i-th dryland sub-region, Δh represents the set reference vegetation chlorophyll content, ω2 represents the influencing factor corresponding to the set vegetation chlorophyll content, and p i represents the average plant nutrient content of the i-th dryland sub-region, Δp represents the set reference plant nutrient content, ω3 represents the influencing factor corresponding to the set plant nutrient content, and s i represents the average soil pH value of the i-th dryland sub-region, Δs represents the set defined soil pH value, ω4 represents the influencing factor corresponding to the set soil pH value, i represents the number of each dryland sub-region, i=1,2,3,…n, n represents the total number of dryland sub-regions, and e represents a natural constant.

[0078] It should be noted that, in this embodiment, the influencing factors corresponding to the preset vegetation coverage rate, the influencing factors corresponding to the vegetation chlorophyll content, the influencing factors corresponding to the plant nutrient content and the influencing factors corresponding to the soil pH value are obtained from the agricultural monitoring database, and the influencing factors corresponding to the vegetation coverage rate, the influencing factors corresponding to the vegetation chlorophyll content, the influencing factors corresponding to the plant nutrient content and the influencing factors corresponding to the soil pH value are all numbers between 0 and 1.

[0079] In a specific embodiment, based on the relationship between the historical vegetation coverage rate of each dryland sub-region and the soil fertility regulation index of each dryland sub-region, a mapping set of the vegetation coverage rate of each dryland sub-region and the influencing factors corresponding to the corresponding vegetation coverage rate is constructed, and the real-time vegetation coverage rate of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding vegetation coverage rate.

[0080] In a specific embodiment, based on the relationship between the historical average vegetation chlorophyll content of each dryland sub-region and the soil fertility regulation index of each dryland sub-region, a mapping set of the average vegetation chlorophyll content of each dryland sub-region and the influencing factors corresponding to the corresponding vegetation chlorophyll content is constructed, and the real-time average vegetation chlorophyll content of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding vegetation chlorophyll content.

[0081] In a specific embodiment, based on the relationship between the historical average plant nutrient content of each dryland sub-region and the soil fertility regulation index of each dryland sub-region, a mapping set of the average plant nutrient content of each dryland sub-region and the influencing factors corresponding to the corresponding plant nutrient content is constructed, and the real-time average plant nutrient content of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding plant nutrient content.

[0082] In a specific embodiment, based on the relationship between the historical average soil pH value of each dryland sub-region and the soil fertility regulation index of each dryland sub-region, a mapping set of the average soil pH value of each dryland sub-region and the influencing factors corresponding to the corresponding soil pH value is constructed, and the real-time average soil pH value of each dryland sub-region is input into the mapping set to obtain the influencing factors corresponding to the corresponding soil pH value.

[0083] It should be explained that the analysis method of this embodiment obtains the soil fertility regulation index of each dryland sub-region by combining the vegetation coverage rate, average vegetation chlorophyll content, average plant nutrient content and average soil pH value of each dryland sub-region, which helps to improve the accuracy of the soil fertility status assessment of the dryland sub-region and provide data support for soil fertilization.

[0084] It should be added that the vegetation coverage rate, average vegetation chlorophyll content, average plant nutrient content and average soil pH value of each dryland sub-region in this formula are correlated and do not exist independently. For example, the average vegetation chlorophyll content is usually positively correlated with the vegetation coverage rate. A higher average vegetation chlorophyll content indicates active plant photosynthesis, which may be related to dryland sub-regions with high vegetation coverage rates. Comprehensive analysis of the vegetation coverage rate, average vegetation chlorophyll content, average plant nutrient content and average soil pH value of each dryland sub-region can more accurately evaluate the impact of plant growth data on soil fertility regulation.

[0085] In this implementation plan, analyzing the vegetation coverage rate of each dryland sub-region can formulate a more accurate fertilization plan, analyzing the average vegetation chlorophyll content of each dryland sub-region helps to adjust the fertilization strategy to ensure that plants can obtain enough required nutrients, analyzing the average plant nutrient content of each dryland sub-region helps to improve the accuracy of soil nutritional status assessment, thereby improving the accuracy of fertilization, analyzing the average soil pH value of each dryland sub-region can accurately analyze the acidity and alkalinity of the soil, thereby optimizing the fertilization plan, and analyzing the soil fertility regulation index of each dryland sub-region can maximize fertilizer utilization and reduce costs.

[0086] Specifically, the supplementary irrigation and fertilization parameters of each dryland sub-region are analyzed and obtained, and the specific analysis process is: the supplementary irrigation and fertilization parameters of each dryland sub-region include the soil moisture adjustment index value of each dryland sub-region and the soil fertility adjustment index value of each dryland sub-region.

[0087] The meteorological data of the dryland area in the cloud server is extracted to obtain the meteorological data set of the dryland area, the meteorological comparison data set of the dryland area stored in the agricultural monitoring database is extracted, and the meteorological characterization value of the dryland area is obtained through comprehensive analysis.

[0088] It should be noted that the meteorological characterization value of the dryland area represents a numerical quantification result obtained by analyzing the meteorological data of the dryland area, and is used to quantify the influence of the meteorology of the dryland area on the regulation of soil moisture and fertility.

[0089] It should be noted that the meteorological data set of the arid area includes meteorological data of the arid area, and the meteorological data of the arid area includes average precipitation in the arid area, maximum relative humidity in the arid area, average wind speed in the arid area and water evaporation rate in the arid area.

[0090] It should be added that the average precipitation in the arid area refers to the average precipitation received per unit area in the arid area within a certain period of time, which is usually measured and recorded using a rain gauge. The maximum relative humidity in the arid area refers to the maximum relative humidity reached in the arid area within a certain period of time, that is, the maximum percentage of water vapor in the air, which is usually measured by a humidity sensor. The average wind speed in the arid area refers to the average wind speed in the arid area within a certain period of time, that is, the average speed at which the air moves per unit time, and the wind speed is usually measured and recorded using an anemometer. The water evaporation rate in the arid area refers to the water evaporation rate per unit area under certain conditions, which is usually measured using an evaporation dish or an evaporimeter.

[0091] It should be noted that the meteorological comparison data set of the arid area includes the comparison values ​​of the meteorological data of the arid area, and the comparison values ​​of the meteorological data of the arid area include the precipitation comparison value of the arid area, the maximum relative humidity comparison value of the arid area, the wind speed comparison value of the arid area and the water evaporation rate comparison value of the arid area.

[0092] It should be added that each comparison value in the comparison value of the meteorological data of the arid area is usually obtained by statistical analysis of historical data. The specific steps are to count the specific values ​​of the meteorological data of the historical arid area, and calculate the average value of these values ​​to obtain each comparison value. By way of example, it is assumed that the average value of the precipitation in the arid area in the historical data is 3 mm. According to the statistical results, the precipitation comparison value of the arid area can be obtained to be 3 mm.

[0093] It should be added that what needs to be understood here is that the meteorological data set of the arid area and the data in the meteorological comparison data set of the arid area correspond to each other.

[0094] In this embodiment, the meteorological characteristic value of the dryland area is obtained using the following analysis conditions, specifically:

[0095]

[0096] In the formula, γ represents the meteorological characteristic value of the dryland area, τ j represents the j-th meteorological data of the dryland area, Δτ represents the comparison value of the j-th meteorological data of the dryland area, j represents the number of each meteorological data, j=1,2,3,…m, and m represents the total number of meteorological data.

[0097] According to the soil moisture regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the soil moisture regulation index value of each dryland sub-region is analyzed and obtained.

[0098] It should be noted that the soil moisture regulation index value of each dryland sub-region represents a numerical quantification result obtained by analyzing the soil environment data and meteorological data of the dryland region, and is used to quantify the influence of the soil environment and meteorology of the dryland region on soil moisture regulation.

[0099] According to the soil fertility regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the soil fertility regulation index value of each dryland sub-region is analyzed and obtained.

[0100] It should be noted that the soil fertility regulation index value of each dryland sub-region represents a numerical quantification result obtained by analyzing the plant growth data and meteorological data of the dryland region, and is used to quantify the influence of plant growth and meteorology on soil fertility regulation in the dryland region.

[0101] Specifically, regulating and controlling the soil moisture and soil fertility of each dryland sub-region includes regulating and controlling the soil moisture of each dryland sub-region and regulating and controlling the soil fertility of each dryland sub-region.

[0102] The soil moisture of each dryland sub-region is regulated and controlled in a specific process of comparing the soil moisture regulation index value of each dryland sub-region with the set soil moisture regulation index threshold value of each dryland sub-region; if the soil moisture regulation index value of a dryland sub-region is higher than the set soil moisture regulation index threshold value of the dryland sub-region, the soil moisture of the dryland sub-region is regulated and controlled.

[0103] The difference between the soil moisture adjustment index value of the dryland sub-region and the set soil moisture adjustment index threshold of the dryland sub-region is recorded as the soil moisture correction index of the dryland sub-region. The soil irrigation parameters of the dryland sub-region are obtained according to the soil moisture correction index matching, and the dryland sub-region is irrigated according to the soil irrigation parameters of the dryland sub-region.

[0104] It should be added that the specific process of obtaining the soil irrigation parameters of the dryland sub-region according to the soil moisture correction index matching is to compare the soil moisture correction index with the set soil moisture correction index intervals to obtain the soil irrigation parameters.

[0105] It should be added that each soil moisture correction index interval corresponds to a specific soil irrigation parameter. The soil moisture correction index is compared with the set soil moisture correction index interval to determine the interval to which the soil moisture correction index belongs. According to the interval, the corresponding soil irrigation parameter is selected for irrigation operation.

[0106] It should be added that soil irrigation parameters include soil irrigation times and soil irrigation water volume.

[0107] Specifically, the soil fertility of each dryland sub-region is regulated and controlled. The specific process is: the soil fertility regulation index value of each dryland sub-region is compared with the set soil fertility regulation index threshold of each dryland sub-region. If the soil fertility regulation index value of a dryland sub-region is higher than the set soil fertility regulation index threshold of the dryland sub-region, the soil fertility of the dryland sub-region is regulated and controlled.

[0108] The difference between the soil fertility adjustment index value of the dryland sub-region and the set soil fertility adjustment index threshold value of the dryland sub-region is recorded as the soil fertility correction index of the dryland sub-region. The soil fertilization parameters of the dryland sub-region are obtained according to the soil fertilization correction index matching, and the dryland sub-region is fertilized according to the soil fertilization parameters of the dryland sub-region.

[0109] It should be added that the specific process of obtaining the soil fertilization parameters of the dryland sub-region according to the soil fertility correction index matching is to compare the soil fertility correction index with the set soil fertility correction index interval to obtain the soil fertilization parameters.

[0110] It should be added that each soil fertility correction index interval corresponds to a specific soil fertilization parameter. The soil fertility correction index is compared with the set soil fertility correction index interval to determine the interval to which the soil fertility correction index belongs. According to the interval, the corresponding soil fertilization parameter is selected for fertilization operation.

[0111] It should be added that soil fertilization parameters include fertilizer type, fertilizer amount and fertilization frequency.

[0112] Specifically, the soil moisture regulation index values ​​of each dryland sub-region are analyzed under the following conditions:

[0113]

[0114] In the formula, α i represents the soil moisture regulation index value of the i-th dryland sub-region, β i represents the soil moisture regulation index of the i-th dryland sub-region, represents the weight factor corresponding to the set soil moisture adjustment index, γ represents the meteorological characterization value of the dryland area, represents the weight factor corresponding to the set meteorological characterization value, i represents the number of each dryland sub-region, i=1,2,3,…n, and n represents the total number of dryland sub-regions.

[0115] In a specific embodiment, Table 1 shows soil moisture adjustment index values ​​corresponding to different soil moisture adjustment indices and meteorological characterization values. In this embodiment, the weight factor corresponding to the soil moisture adjustment index is 0.6, and the weight factor corresponding to the meteorological characterization value is 0.4.

[0116] Table 1 Soil moisture regulation index values ​​corresponding to different soil moisture regulation indices and meteorological characterization values

[0117] Soil Moisture Regulation Index Meteorological characteristic value Soil moisture regulation index value 1.1 1.3 1.4473 1.1 1.5 1.4986 1.5 1.3 1.5050

[0118] It should be explained that in Table 1, as the soil moisture adjustment index and meteorological characterization value increase, the soil moisture adjustment index value increases accordingly. Table 1 lists the values ​​of three different soil moisture adjustment indexes and meteorological characterization values, and the corresponding soil moisture adjustment index values ​​are obtained through the formula. These soil moisture adjustment index values ​​provide the degree of influence of different soil moisture adjustment indexes and meteorological characterization values ​​on the soil moisture adjustment index value. The soil moisture adjustment index value comprehensively considers the soil moisture adjustment index and meteorological characterization value, provides data support for the evaluation of the soil moisture adjustment index value of each dryland sub-region, and improves the accuracy of the soil moisture adjustment index value evaluation.

[0119] It should be noted that, in this embodiment, the weight factor corresponding to the preset soil moisture adjustment index and the weight factor corresponding to the meteorological characterization value are obtained from the agricultural monitoring database, and the weight factor corresponding to the soil moisture adjustment index and the weight factor corresponding to the meteorological characterization value are both numbers between 0 and 1.

[0120] In a specific embodiment, based on the relationship between the historical soil moisture adjustment index of each dryland sub-region and the soil moisture adjustment index value of each dryland sub-region, a mapping set of the soil moisture adjustment index of each dryland sub-region and the weight factor corresponding to the corresponding soil moisture adjustment index is constructed, and the real-time soil moisture adjustment index of each dryland sub-region is input into the mapping set to obtain the weight factor corresponding to the corresponding soil moisture adjustment index.

[0121] In a specific embodiment, based on the relationship between the historical meteorological characterization values ​​of the dryland area and the soil moisture adjustment index values ​​of each dryland sub-area, a mapping set of meteorological characterization values ​​of the dryland area and weight factors corresponding to the corresponding meteorological characterization values ​​is constructed, and the real-time meteorological characterization values ​​of the dryland area are input into the mapping set to obtain the weight factors corresponding to the corresponding meteorological characterization values.

[0122] It should be explained that the analysis method of this embodiment, by combining the soil moisture adjustment index of each dryland sub-region and the meteorological characterization value of the dryland region for analysis, obtains the soil moisture adjustment index value of each dryland sub-region, which helps to more accurately understand the soil moisture conditions of different dryland sub-regions, thereby reducing unnecessary irrigation and reducing the consumption of water resources.

[0123] It should be added that the soil moisture regulation index of each dryland sub-region in this formula is related to the meteorological characterization value of the dryland area, and does not exist independently. For example, the precipitation in the meteorological characterization value directly affects the soil moisture. Higher precipitation usually leads to increased soil moisture. Changes in precipitation will directly affect the value of the soil moisture regulation index. Comprehensive analysis of the soil moisture regulation index of each dryland sub-region and the meteorological characterization value of the dryland area can more accurately evaluate the influence of soil moisture on soil moisture regulation.

[0124] Specifically, the soil fertility regulation index values ​​of each dryland sub-region are analyzed under the following conditions:

[0125]

[0126] In the formula, ρ i represents the soil fertility regulation index value of the i-th dryland sub-region, δ i represents the soil fertility regulation index of the i-th dryland sub-region, represents the weight factor corresponding to the set soil fertility regulation index, γ represents the meteorological characterization value of the dryland area, represents the weight factor corresponding to the set meteorological characterization value, i represents the number of each dryland sub-region, i=1,2,3,…n, n represents the total number of dryland sub-regions, and e represents a natural constant.

[0127] It needs to be explained that Figure 3 As shown, Figure 3 : is an example image of the soil fertility adjustment index value, wherein the x-axis represents the soil fertility adjustment index, and the y-axis represents the soil fertility adjustment index value. In this embodiment, the weight factor corresponding to the soil fertility adjustment index is 0.3, and the weight factor corresponding to the meteorological characterization value is 0.7. Three different groups of example parameters are defined in the figure, corresponding to different situations of the three curves, respectively represented by solid lines, dashed lines and dotted lines, and the corresponding curve labels are a, b, and c, respectively.

[0128] It needs to be explained that when the meteorological characterization value is 1.2, the functional relationship between the soil fertility regulation index and the soil fertility regulation index value is shown in curve a, when the meteorological characterization value is 1.8, the functional relationship between the soil fertility regulation index and the soil fertility regulation index value is shown in curve b, and when the meteorological characterization value is 2.5, the functional relationship between the soil fertility regulation index and the soil fertility regulation index value is shown in curve c. The soil fertility regulation index value increases with the increase of the soil fertility regulation index, and when the meteorological characterization value increases, the soil fertility regulation index value increases accordingly. The soil fertility regulation index value can be obtained quickly and accurately through the curve, which solves the problem in the prior art that the soil fertility regulation index value cannot be accurately measured due to the lack of detailed analysis process, thereby realizing precise regulation of soil fertility.

[0129] It should be noted that, in this embodiment, the weight factor corresponding to the preset soil fertility adjustment index and the weight factor corresponding to the meteorological characterization value are obtained from the agricultural monitoring database, and the weight factor corresponding to the soil fertility adjustment index and the weight factor corresponding to the meteorological characterization value are both numbers between 0 and 1.

[0130] In a specific embodiment, based on the relationship between the historical soil fertility adjustment index of each dryland sub-region and the soil fertility adjustment index value of each dryland sub-region, a mapping set of the soil fertility adjustment index of each dryland sub-region and the weight factor corresponding to the corresponding soil fertility adjustment index is constructed, and the real-time soil fertility adjustment index of each dryland sub-region is input into the mapping set to obtain the weight factor corresponding to the corresponding soil fertility adjustment index.

[0131] In a specific embodiment, based on the relationship between the historical meteorological characterization values ​​of the dryland area and the soil fertility regulation index values ​​of each dryland sub-area, a mapping set of meteorological characterization values ​​of the dryland area and weight factors corresponding to the corresponding meteorological characterization values ​​is constructed, and the real-time meteorological characterization values ​​of the dryland area are input into the mapping set to obtain the weight factors corresponding to the corresponding meteorological characterization values.

[0132] It should be explained that the analysis method of this embodiment, by combining the soil fertility regulation index of each dryland sub-region and the meteorological characterization value of the dryland region for analysis, obtains the soil fertility regulation index value of each dryland sub-region, which helps to formulate a more accurate fertilization plan. The nutrient content in the soil can be understood by combining the soil fertility regulation index, and then considering the impact of meteorological factors such as precipitation on nutrient loss, the fertilization time and amount can be adjusted to ensure that crops can obtain appropriate nutritional support during the growth period.

[0133] It should be added that the soil fertility regulation index of each dryland sub-region in this formula is related to the meteorological characterization value of the dryland area, and does not exist independently. For example, the precipitation in the meteorological characterization value directly affects the flow and distribution of nutrients in the soil. Higher precipitation can promote the leaching of nutrients in the soil and increase the nutrient content in the soil, thereby reducing the soil fertility regulation index value. Comprehensive analysis of the soil fertility regulation index of each dryland sub-region and the meteorological characterization value of the dryland area can more accurately evaluate the impact of plant growth on soil fertility regulation.

[0134] See also Figure 2 As shown, the second aspect of the present invention provides an intelligent control system for dryland irrigation and fertilization based on the Internet of Things, including: an agricultural monitoring platform, which is used to establish an agricultural monitoring platform, divide the dryland areas into various dryland sub-areas, and monitor and collect soil environmental data and plant growth data of each dryland sub-area.

[0135] The supplementary irrigation and topdressing analysis module is used to analyze the soil environment data and plant growth data of each dryland sub-region to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region.

[0136] The supplementary irrigation and fertilization adjustment module is used to synchronously extract the meteorological data of the dryland area in the cloud server according to the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-area, comprehensively analyze and obtain the supplementary irrigation and fertilization parameters of each dryland sub-area, and adjust and control the soil moisture and soil fertility of each dryland sub-area.

[0137] It should be noted that the intelligent control method and system for dryland irrigation and fertilization based on the Internet of Things also includes an agricultural monitoring database, which is used to store the reference clay particle percentage, reference total organic matter content, reference soil moisture, reference soil temperature, the influencing factor corresponding to the clay particle percentage, the influencing factor corresponding to the total organic matter content, the influencing factor corresponding to the soil moisture, the influencing factor corresponding to the soil temperature, the reference vegetation coverage, the reference vegetation chlorophyll content, the reference plant nutrient content, the defined soil pH value, the influencing factor corresponding to the vegetation coverage, the influencing factor corresponding to the vegetation chlorophyll content, the influencing factor corresponding to the plant nutrient content, the influencing factor corresponding to the soil pH value, the meteorological comparison data set of the dryland area, the soil moisture adjustment index threshold of each dryland sub-area, the soil fertility adjustment index threshold of each dryland sub-area, the weight factor corresponding to the soil moisture adjustment index, the weight factor corresponding to the meteorological characterization value, the weight factor corresponding to the soil fertility adjustment index, and the weight factor corresponding to the meteorological characterization value.

[0138] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0139] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent control method for supplementary irrigation and fertilization in dry land based on the Internet of Things, characterized in that: include: Establish an agricultural monitoring platform, divide the arid land areas into various arid land sub-areas, and monitor and collect soil environment data and plant growth data of each arid land sub-area; Analyze the soil environment data and plant growth data of each dryland sub-region to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region; According to the soil moisture regulation index and soil fertility regulation index of each dryland sub-region, the meteorological data of the dryland area in the cloud server are synchronously extracted, and the supplementary irrigation and topdressing parameters of each dryland sub-region are obtained through comprehensive analysis, and the soil moisture and soil fertility of each dryland sub-region are regulated and controlled.

2. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 1 is characterized in that: The soil environmental data of each dryland sub-region include the clay particle percentage of the soil of each dryland sub-region and the average total organic matter content of the soil of each dryland sub-region; The plant growth data of each dryland sub-region includes the vegetation coverage rate of each dryland sub-region and the average vegetation chlorophyll content of each dryland sub-region.

3. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 1 is characterized in that: The analyzing of the soil environment data and plant growth data of each dryland sub-region includes analyzing the soil environment data of each dryland sub-region and analyzing the plant growth data of each dryland sub-region; The specific analysis process of analyzing the soil environmental data of each dryland sub-region is to randomly arrange a number of soil monitoring points in each dryland sub-region, monitor and collect the soil temperature and humidity of each soil monitoring point, and perform mean processing to obtain the average soil humidity and average soil temperature of each dryland sub-region; The percentage of clay particles in the soil of each dryland sub-region and the average total organic matter content of the soil of each dryland sub-region were statistically analyzed. Combined with the average soil moisture and the average soil temperature of each dryland sub-region, the soil moisture regulation index of each dryland sub-region was obtained through comprehensive analysis.

4. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 3 is characterized by: The plant growth data of each dryland sub-region is analyzed, and the specific analysis process is as follows: Randomly collect plant samples from each dryland sub-region and analyze them to obtain the average total plant nutrient content of each dryland sub-region; Monitor and collect the soil pH value of each soil monitoring point, and perform average processing to obtain the average soil pH value of each dryland sub-area; The vegetation coverage rate of each dryland sub-region and the average vegetation chlorophyll content of each dryland sub-region were extracted from the plant growth data of each dryland sub-region. The soil fertility regulation index of each dryland sub-region was obtained by analysis based on the average plant nutrient content and the average soil pH value of each dryland sub-region.

5. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 1 is characterized in that: The analysis results in the supplementary irrigation and fertilization parameters for each dryland sub-region. The specific analysis process is as follows: The supplementary irrigation and topdressing parameters of each dryland sub-region include a soil moisture adjustment index value and a soil fertility adjustment index value of each dryland sub-region; Extract meteorological data of the dryland area in the cloud server to obtain a meteorological data set of the dryland area, extract a meteorological comparison data set of the dryland area stored in the agricultural monitoring database, and obtain a meteorological characterization value of the dryland area through comprehensive analysis; According to the soil moisture regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the soil moisture regulation index value of each dryland sub-region is analyzed; According to the soil fertility regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the soil fertility regulation index value of each dryland sub-region is analyzed and obtained.

6. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 1 is characterized by: The regulating and controlling the soil moisture and soil fertility of each dryland sub-region includes regulating and controlling the soil moisture of each dryland sub-region and regulating and controlling the soil fertility of each dryland sub-region; The soil moisture of each dryland sub-region is regulated and controlled in a specific process of comparing the soil moisture regulation index value of each dryland sub-region with the set soil moisture regulation index threshold value of each dryland sub-region; if the soil moisture regulation index value of a dryland sub-region is higher than the set soil moisture regulation index threshold value of the dryland sub-region, the soil moisture of the dryland sub-region is regulated and controlled; The difference between the soil moisture adjustment index value of the dryland sub-region and the set soil moisture adjustment index threshold of the dryland sub-region is recorded as the soil moisture correction index of the dryland sub-region. The soil irrigation parameters of the dryland sub-region are obtained according to the soil moisture correction index matching, and the dryland sub-region is irrigated according to the soil irrigation parameters of the dryland sub-region.

7. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 6 is characterized by: The specific process of regulating and controlling the soil fertility of each dryland sub-region is as follows: The soil fertility adjustment index value of each dryland sub-region is compared with the set soil fertility adjustment index threshold value of each dryland sub-region. If the soil fertility adjustment index value of a dryland sub-region is higher than the set soil fertility adjustment index threshold value of the dryland sub-region, the soil fertility of the dryland sub-region is adjusted and controlled; The difference between the soil fertility adjustment index value of the dryland sub-region and the set soil fertility adjustment index threshold value of the dryland sub-region is recorded as the soil fertility correction index of the dryland sub-region. The soil fertilization parameters of the dryland sub-region are obtained according to the soil fertilization correction index matching, and the dryland sub-region is fertilized according to the soil fertilization parameters of the dryland sub-region.

8. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 5 is characterized by: The soil moisture adjustment index value of each dryland sub-region is specifically analyzed under the following conditions: In the formula, α i represents the soil moisture regulation index value of the i-th dryland sub-region, β i represents the soil moisture regulation index of the i-th dryland sub-region, represents the weight factor corresponding to the set soil moisture adjustment index, γ represents the meteorological characterization value of the dryland area, represents the weight factor corresponding to the set meteorological characterization value, i represents the number of each dryland sub-region, i=1,2,3,…n, and n represents the total number of dryland sub-regions.

9. The intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things according to claim 5 is characterized by: The soil fertility adjustment index values ​​of each dryland sub-region are specifically analyzed under the following conditions: In the formula, ρ i represents the soil fertility regulation index value of the i-th dryland sub-region, δ i represents the soil fertility regulation index of the i-th dryland sub-region, represents the weight factor corresponding to the set soil fertility regulation index, γ represents the meteorological characterization value of the dryland area, represents the weight factor corresponding to the set meteorological characterization value, i represents the number of each dryland sub-region, i=1,2,3,…n, n represents the total number of dryland sub-regions, and e represents a natural constant.

10. An intelligent control system for supplementary irrigation and fertilization in dry land based on the Internet of Things, characterized in that: include: Agricultural monitoring platform, used to establish an agricultural monitoring platform, divide the dryland areas, obtain dryland sub-areas, monitor and collect soil environment data and plant growth data of each dryland sub-area; The supplementary irrigation and topdressing analysis module is used to analyze the soil environment data and plant growth data of each dryland sub-region to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region; The supplementary irrigation and fertilization adjustment module is used to synchronously extract the meteorological data of the dryland area in the cloud server according to the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-area, comprehensively analyze and obtain the supplementary irrigation and fertilization parameters of each dryland sub-area, and adjust and control the soil moisture and soil fertility of each dryland sub-area.

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