Intelligent control method and system for dry land supplemental irrigation and topdressing based on internet of things

Abstract

This invention discloses an intelligent control method and system for supplemental irrigation and topdressing in dryland areas based on the Internet of Things (IoT), belonging to the field of program control system technology. First, the dryland is divided into sub-regions, which helps to accurately monitor and collect soil environmental data and plant growth data for each sub-region. This allows for a more accurate understanding of the soil moisture status and plant growth status of each sub-region. Then, the soil moisture regulation index and soil fertility regulation index of each sub-region are analyzed, providing a scientific basis for formulating precise fertilization and irrigation strategies, reducing insufficient fertilization and irrigation, saving resources, and minimizing environmental impact. Finally, the supplemental irrigation and topdressing parameters for each sub-region are analyzed, and the soil moisture and soil fertility of each sub-region are regulated and controlled. This can be dynamically adjusted based on real-time meteorological data to ensure optimal management and control of soil moisture and fertility in each sub-region.

Description

Technical Field

[0001] This invention relates to the field of program control system technology, specifically to an intelligent control method and system for supplementary irrigation and topdressing in dryland areas based on the Internet of Things. Background Technology

[0002] In agricultural production, insufficient soil moisture may lead to restricted crop growth, decreased soil quality, and even ecological degradation. Insufficient fertilizer will directly affect crop yield. Therefore, supplemental irrigation and topdressing of the soil are particularly important. However, supplemental irrigation and topdressing methods usually rely on farmers' experience and intuition, lacking scientific basis and data support, which can easily lead to problems of excessive or insufficient nutrients. Conventional regular irrigation and fertilization can easily lead to waste of resources.

[0003] The existing technology, such as the invention patent announcement CN104460582B, discloses an IoT-based intelligent irrigation and fertilization control method and system based on fuzzy control. The method includes: (1) data acquisition and processing: based on the crop's water requirement pattern and fertilizer formula, as well as the collected data on soil temperature and humidity, soil nutrients, air temperature and humidity, wind speed, rainfall, flow rate, water tank level, and pipeline pressure, setting limits for soil moisture, nutrients, and water tank level, as well as the planned irrigation and fertilization time, and storing them in a 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; intelligently controlling the start and stop of the water pump by comparing the current water tank level with the set water tank level limit; and using PID control algorithm to speed control the water pump. This invention has the characteristics of excellent performance, complete functions, strong scalability, and easy operation and management, and uses fuzzy control to achieve intelligent management and control.

[0004] Existing technology, such as the invention patent announcement CN109116827B, discloses an IoT-based method and device for integrated water and fertilizer irrigation control in a solar greenhouse. This method includes: acquiring greenhouse meteorological parameters and target substrate moisture information, whereby the greenhouse meteorological parameters include air temperature, air humidity, and light intensity; 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 being trained based on the greenhouse meteorological parameters; acquiring a lower limit value for substrate moisture in the solar greenhouse, which is obtained based on the target substrate moisture information and transpiration rate; acquiring real-time substrate moisture values ​​in the solar greenhouse; and determining a target irrigation scheme based on the lower limit value and real-time substrate moisture values ​​for integrated water and fertilizer irrigation control of crops within the solar greenhouse.

[0005] Based on the above findings, current methods for controlling supplemental irrigation and topdressing typically involve analyzing the growth environment and then controlling water and fertilizer application. This may result in insufficient fertilization and irrigation, failing to improve the precision of soil fertilization and irrigation. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent control method and system for supplementary irrigation and topdressing in dryland areas based on the Internet of Things, which can effectively solve the problems mentioned in the background technology.

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

[0008] Soil environmental data and plant growth data of each dryland sub-region were analyzed to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region.

[0009] Based on the soil moisture regulation index and soil fertility regulation index of each dryland sub-region, meteorological data of the dryland region in the cloud server are extracted simultaneously, and the supplementary irrigation and topdressing parameters of each dryland sub-region are obtained through comprehensive analysis. The soil moisture and soil fertility of each dryland sub-region are then regulated and controlled.

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

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

[0012] Furthermore, the analysis of soil environmental data and plant growth data for each dryland sub-region includes analyzing soil environmental data and plant growth data for each dryland sub-region.

[0013] The specific analysis process for analyzing the soil environmental data of each dryland sub-region is as follows: several soil monitoring points are randomly set up in each dryland sub-region to monitor and collect the soil temperature and humidity of each soil monitoring point, and the average values ​​are processed to obtain the average soil humidity and average soil temperature of each dryland sub-region.

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

[0015] Furthermore, the analysis of plant growth data in each dryland sub-region involves the following specific process: 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.

[0016] Soil pH values ​​were collected from each soil monitoring point and averaged to obtain the average soil pH value for each dryland sub-region.

[0017] The vegetation coverage and average chlorophyll content of each dryland sub-region were extracted from the plant growth data of each dryland sub-region. Combined with the average plant nutrient content and average soil pH of each dryland sub-region, the soil fertility regulation index of each dryland sub-region was analyzed.

[0018] Furthermore, the analysis yielded irrigation and fertilization parameters for each dryland sub-region. The specific analysis process is as follows: the irrigation and fertilization parameters for each dryland sub-region include soil moisture regulation index values ​​and soil fertility regulation index values ​​for each dryland sub-region.

[0019] Meteorological data of arid regions are extracted from cloud servers to obtain meteorological datasets of arid regions. Meteorological comparison datasets of arid regions are extracted from agricultural monitoring databases, and comprehensive analysis is performed to obtain meteorological characterization values ​​of arid regions.

[0020] Based on the soil moisture regulation index of each dryland sub-region and combined with the meteorological characterization values ​​of the dryland region, the soil moisture regulation index values ​​of each dryland sub-region were analyzed and obtained.

[0021] Based on the soil fertility regulation index of each dryland sub-region and combined with the meteorological characterization values ​​of the dryland region, the soil fertility regulation index values ​​of each dryland sub-region were analyzed and obtained.

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

[0023] The specific process for regulating and controlling the soil moisture in each dryland sub-region is as follows: the soil moisture regulation index value of each dryland sub-region is compared with the set soil moisture regulation index threshold value of each dryland sub-region. If the soil moisture regulation index value of a certain dryland sub-region is higher than the set soil moisture regulation index threshold value of that dryland sub-region, then the soil moisture of that dryland sub-region is regulated and controlled.

[0024] The difference between the soil moisture regulation index value of the dryland sub-region and the set soil moisture regulation 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 by matching the soil moisture correction index, and the dryland sub-region is irrigated according to the soil irrigation parameters of the dryland sub-region.

[0025] Furthermore, the specific process of regulating and controlling the soil fertility of each dryland sub-region is as follows: the soil fertility regulation index value of each dryland sub-region is compared with the set soil fertility regulation index threshold value of each dryland sub-region. If the soil fertility regulation index value of a certain dryland sub-region is higher than the set soil fertility regulation index threshold value of that dryland sub-region, then the soil fertility of that 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 of the dryland sub-region is recorded as the soil fertility correction index of the dryland sub-region. Soil fertilization parameters of the dryland sub-region are obtained by matching the soil fertility correction index, and fertilization is carried out on the dryland sub-region according to the soil fertilization parameters of the dryland sub-region.

[0027] Furthermore, the specific analysis conditions for the soil moisture regulation index values ​​of each dryland sub-region are as follows:

[0028] In the formula, α i β represents the soil moisture regulation index value of the i-th dryland sub-region. i This represents the soil moisture regulation index of the i-th dryland sub-region. This represents the weighting factor corresponding to the set soil moisture regulation index, where γ represents the meteorological characteristic value of the arid region. The value represents the weighting 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 specific analysis conditions for the soil fertility regulation index values ​​of each dryland sub-region are as follows:

[0030] In the formula, ρ i δ represents the soil fertility regulation index value of the i-th dryland sub-region. i This represents the soil fertility adjustment index of the i-th dryland sub-region. This represents the weighting factor corresponding to the set soil fertility adjustment index, and γ represents the meteorological characteristic value of the arid region. The weight factor corresponding to the set meteorological characterization value is represented by i, which represents the number of each dryland sub-region, i = 1, 2, 3, ... n, where n represents the total number of dryland sub-regions, and e represents the natural constant.

[0031] The second aspect of the present invention provides an intelligent control system for supplemental irrigation and topdressing of dryland based on the Internet of Things, comprising: an agricultural monitoring platform for establishing an agricultural monitoring platform, dividing the dryland area into sub-regions, and monitoring and collecting soil environmental data and plant growth data of each sub-region.

[0032] The supplementary irrigation and topdressing analysis module is used to analyze soil environmental 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 topdressing adjustment module is used to simultaneously extract meteorological data of dryland areas from the cloud server based on the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-region, comprehensively analyze and obtain the supplementary irrigation and topdressing parameters of each dryland sub-region, and adjust and control the soil moisture and soil fertility of each dryland sub-region.

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

[0035] (1) This invention provides an intelligent control method and system for supplementary irrigation and topdressing of dry land based on the Internet of Things. First, the dry land is divided into regions. Then, soil environmental data and plant growth data of each dry land sub-region are collected and analyzed. This allows for a more accurate understanding of the soil moisture and plant growth status of each dry land sub-region. Subsequently, the soil moisture regulation index and soil fertility regulation index of each dry land sub-region are obtained and combined with the meteorological data of the dry land region for regulation and control, thereby reducing the situation of insufficient fertilization and irrigation.

[0036] (2) This invention divides the dryland area into sub-regions, monitors and collects soil environmental data and plant growth data of each sub-region, and monitors soil moisture and fertility in a timely manner to help detect potential drought or soil degradation problems in the early stage. It can accurately analyze the amount of supplemental irrigation water and topdressing parameters required for each sub-region, which helps to reduce the situation of insufficient fertilization and irrigation, and maximizes the efficiency and yield of crop growth.

[0037] (3) By analyzing the soil moisture regulation index and soil fertility regulation index of each dryland sub-region, this invention can improve the accuracy of water and fertilizer resource allocation and utilization, reduce waste and improve the utilization rate of water and fertilizer resources. Through refined management, it can reduce the dependence on water and fertilizer in agricultural production, reduce environmental load, and improve crop yield and quality.

[0038] (4) This invention obtains the supplementary irrigation and topdressing parameters for each dryland sub-region by analysis, and regulates and controls the soil moisture and soil fertility of each dryland sub-region. Combined with meteorological data analysis, it can provide more scientific and accurate guidance for the regulation of soil fertility and moisture in each dryland sub-region, thereby improving the accuracy and effect of soil fertilization and irrigation, and thus helping to optimize the crop growth environment, increase yield and quality, and reduce unnecessary waste of resources.

[0039] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

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

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

[0042] Figure 3 Example image showing soil fertility adjustment index values. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1 As shown, the first aspect of the present invention provides a technical solution: an intelligent control method for supplementary irrigation and topdressing of dry land based on the Internet of Things, including establishing an agricultural monitoring platform, dividing the dry land into regions to obtain each dry land sub-region, and monitoring and collecting soil environmental data and plant growth data of each dry land sub-region.

[0045] It should be explained that establishing an agricultural monitoring platform is a system that integrates modern technology and data analysis techniques to monitor and evaluate soil environmental data and plant growth data in drylands in real time and with high accuracy.

[0046] Soil environmental data and plant growth data of each dryland sub-region were analyzed to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region.

[0047] Based on the soil moisture regulation index and soil fertility regulation index of each dryland sub-region, meteorological data of the dryland region in the cloud server are extracted simultaneously, and the supplementary irrigation and topdressing parameters of each dryland sub-region are obtained through comprehensive analysis. The soil moisture and soil fertility of each dryland sub-region are then regulated and controlled.

[0048] Specifically, the soil environmental data for each dryland sub-region includes the percentage of clay particles in the soil of each dryland sub-region and the average total organic matter content of the soil in 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 clay particles in the soil of each dryland sub-region to the total number of particles in the soil of each dryland sub-region. Soil samples were collected from multiple locations in each dryland sub-region, and the samples were mixed to obtain a whole sample. The total mass of the whole sample was analyzed, and the mass of clay particles in the whole sample was obtained using the sedimentation method. The ratio of the mass of clay particles in the whole sample to the total mass of the whole sample was used 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, and the samples are mixed to obtain a whole sample. The whole sample is heated to burn off the organic matter, and then the difference between the mass of the residual whole sample after combustion and the whole sample is measured and recorded as the total organic matter content of the whole sample. The total organic matter content is obtained by collecting and analyzing samples multiple times, 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 for each dryland sub-region includes the vegetation coverage rate and the average chlorophyll content of the vegetation in each dryland sub-region.

[0052] It should be added that the vegetation coverage rate of each dryland sub-region refers to the percentage of vegetation coverage in each dryland sub-region. High-resolution aerial images were taken using drones, and the vegetation coverage rate of each dryland sub-region 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 within 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 using the solvent extraction method. The average value is recorded as the average vegetation chlorophyll content of each dryland sub-region.

[0054] Specifically, the analysis of soil environmental data and plant growth data for each dryland sub-region includes the analysis of soil environmental data and plant growth data for each dryland sub-region.

[0055] The specific analysis process for analyzing the soil environmental data of each dryland sub-region is as follows: several soil monitoring points are randomly set up in each dryland sub-region to monitor and collect the soil temperature and humidity of each soil monitoring point, and the average values ​​are processed to obtain the average soil humidity and average soil temperature of each dryland sub-region.

[0056] The percentage of clay particles and the average total organic matter content of the soil in each dryland sub-region were statistically analyzed. Combined with the average soil moisture and average soil temperature in 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 the numerical quantitative result obtained by analyzing the soil environmental data of each dryland sub-region, and is used to quantify the degree of influence of the soil environment of each dryland sub-region on soil moisture regulation.

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

[0059]

[0060] In the formula, β i b represents the soil moisture regulation index of the i-th dryland sub-region. i Let represent the percentage of clay particles in the soil of the i-th dryland sub-region, Δb represent the set reference percentage of clay particles, θ1 represent the influencing factor corresponding to the set percentage of clay particles, and c i Let represent the average total organic matter content of the soil in the i-th dryland sub-region, Δc represent the set reference total organic matter content, θ2 represent the influencing factor corresponding to the set total organic matter content, and d i Let represent the average soil moisture of the i-th dryland sub-region, Δd represent the set reference soil moisture, θ3 represent the influencing factor corresponding to the set soil moisture, and f i denoted as 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 the natural constant.

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

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

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

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

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

[0066] It should be explained that the analysis method in this embodiment combines the percentage of clay particles, the average total organic matter content, the average soil moisture, and the average soil temperature of each dryland sub-region to obtain the soil moisture regulation index of each dryland sub-region. This helps to improve the accuracy of soil moisture status assessment in dryland sub-regions and thus provides data support for soil irrigation.

[0067] It should be noted that the percentage of clay particles, average total organic matter content, average soil moisture, and average soil temperature in the soil of each dryland sub-region are related and not independent. For example, the percentage of clay particles is usually positively correlated with the total organic matter content; soils with a high percentage of clay particles usually have a 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 humid environment is conducive to the decomposition and accumulation of organic matter. By comprehensively analyzing the percentage of clay particles, average total organic matter content, average soil moisture, and average soil temperature in the soil of each dryland sub-region, the influence of soil moisture on soil moisture regulation can be assessed more accurately.

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

[0069] Specifically, the plant growth data of each dryland sub-region were analyzed. The specific analysis process was as follows: plant samples were randomly collected from each dryland sub-region and analyzed 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 of each dryland sub-region. This is achieved by collecting multiple plant tissue samples, such as leaves, stems, and roots, from each dryland sub-region and analyzing them using digestion methods.

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

[0072] Soil pH values ​​were collected from each soil monitoring point and averaged to obtain the average soil pH value for each dryland sub-region.

[0073] The vegetation coverage and average chlorophyll content of each dryland sub-region were extracted from the plant growth data of each dryland sub-region. Combined with the average plant nutrient content and average soil pH of each dryland sub-region, the soil fertility regulation index of each dryland sub-region was analyzed.

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

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

[0076]

[0077] In the formula, δ i G represents the soil fertility regulation index of the i-th dryland sub-region. i Let Δg represent the vegetation cover rate of the i-th dryland sub-region, Δg represent the set reference vegetation cover rate, ω1 represent the influencing factor corresponding to the set vegetation cover rate, and h represent the vegetation cover rate. i Let represent the average vegetation chlorophyll content of the i-th dryland sub-region, Δh represent the set reference vegetation chlorophyll content, ω2 represent the influencing factor corresponding to the set vegetation chlorophyll content, and p i Let represent the average plant nutrient content of the i-th dryland sub-region, Δp represent the set reference plant nutrient content, ω3 represent the influencing factor corresponding to the set plant nutrient content, and s i denoted as the average soil pH value of the i-th dryland sub-region, Δs represents the set boundary 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 the natural constant.

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

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

[0080] In one 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 corresponding influencing factors of the 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 corresponding influencing factors of the vegetation chlorophyll content.

[0081] In one 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 corresponding influencing factors of the 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 corresponding influencing factors of the plant nutrient content.

[0082] In one 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 corresponding soil pH value influencing factors is constructed, and the real-time average soil pH value of each dryland sub-region is input into the mapping set to obtain the corresponding soil pH value influencing factors.

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

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

[0085] In this implementation plan, analyzing the vegetation coverage of each dryland sub-region allows for the development of more precise fertilization plans. Analyzing the average chlorophyll content of vegetation in each dryland sub-region helps adjust fertilization strategies to ensure that plants can obtain sufficient nutrients. Analyzing the average plant nutrient content in each dryland sub-region helps improve the accuracy of soil nutrient status assessment, thereby improving the accuracy of fertilization. Analyzing the average soil pH value of each dryland sub-region allows for precise analysis of soil acidity and alkalinity, thereby optimizing fertilization plans. Analyzing and obtaining the soil fertility adjustment index for each dryland sub-region can maximize fertilizer utilization and reduce costs.

[0086] Specifically, the irrigation and fertilization parameters for each dryland sub-region were obtained through analysis. The specific analysis process is as follows: the irrigation and fertilization parameters for each dryland sub-region include the soil moisture regulation index value and the soil fertility regulation index value for each dryland sub-region.

[0087] Meteorological data of arid regions are extracted from cloud servers to obtain meteorological datasets of arid regions. Meteorological comparison datasets of arid regions are extracted from agricultural monitoring databases, and comprehensive analysis is performed to obtain meteorological characterization values ​​of arid regions.

[0088] It should be noted that the meteorological characterization values ​​of the arid regions represent the numerical quantification results obtained by analyzing meteorological data of the arid regions, and are used to quantify the degree of influence of meteorology on soil moisture and fertility regulation in arid regions.

[0089] It should be noted that the meteorological dataset for the arid region includes meteorological data for the arid region, including the average precipitation, the maximum relative humidity, the average wind speed, and the water evaporation rate of the arid region.

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

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

[0092] It should be added that the comparison values ​​of meteorological data in arid areas are usually obtained by statistical analysis of historical data. The specific steps are to collect the specific values ​​of historical meteorological data in arid areas and calculate the average value of these values ​​to obtain the comparison values. For example, assuming that the average precipitation in arid areas in historical data is 3 mm, the comparison value of precipitation in arid areas can be obtained as 3 mm based on the statistical results.

[0093] It should be added that the meteorological dataset for arid regions and the meteorological comparison dataset for arid regions correspond to each other.

[0094] In this embodiment, the meteorological characterization values ​​of the arid region are obtained using the following analytical conditions:

[0095]

[0096] In the formula, γ represents the meteorological characteristic value of arid regions, and τ j Let Δτ represent the j-th meteorological data in the arid region, Δτ represent the comparison value of the j-th meteorological data in the arid region, j represent the number of each meteorological data, j = 1, 2, 3, ... m, and m represent the total number of meteorological data.

[0097] Based on the soil moisture regulation index of each dryland sub-region and combined with the meteorological characterization values ​​of the dryland region, the soil moisture regulation index values ​​of each dryland sub-region were analyzed and obtained.

[0098] It should be noted that the soil moisture regulation index values ​​of each dryland sub-region represent the numerical quantitative results obtained by analyzing soil environmental data and meteorological data of the dryland region, and are used to quantify the degree of influence of soil environment and meteorology on soil moisture regulation in the dryland region.

[0099] Based on the soil fertility regulation index of each dryland sub-region and combined with the meteorological characterization values ​​of the dryland region, the soil fertility regulation index values ​​of each dryland sub-region were analyzed and obtained.

[0100] It should be noted that the soil fertility regulation index values ​​of each dryland sub-region represent the numerical quantitative results obtained by analyzing plant growth data and meteorological data of the dryland region, and are used to quantify the degree of influence of plant growth and meteorology on soil fertility regulation in the dryland region.

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

[0102] The specific process for regulating and controlling the soil moisture in each dryland sub-region is as follows: the soil moisture regulation index value of each dryland sub-region is compared with the set soil moisture regulation index threshold value of each dryland sub-region. If the soil moisture regulation index value of a certain dryland sub-region is higher than the set soil moisture regulation index threshold value of that dryland sub-region, then the soil moisture of that dryland sub-region is regulated and controlled.

[0103] The difference between the soil moisture regulation index value of the dryland sub-region and the set soil moisture regulation 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 by matching the soil moisture correction index, 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 for this dryland sub-region based on the soil moisture correction index is as follows: the soil moisture correction index is compared with the set range of each soil moisture correction index 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. Based on the interval, the corresponding soil irrigation parameter is selected for irrigation operation.

[0106] It should be added that soil irrigation parameters include the number of soil irrigations and the amount of soil irrigation water.

[0107] Specifically, the soil fertility of each dryland sub-region is regulated and controlled. The specific process is as follows: the soil fertility regulation index value of each dryland sub-region is compared with the set soil fertility regulation index threshold value of each dryland sub-region. If the soil fertility regulation index value of a certain dryland sub-region is higher than the set soil fertility regulation index threshold value of that dryland sub-region, the soil fertility of that 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 of the dryland sub-region is recorded as the soil fertility correction index of the dryland sub-region. Soil fertilization parameters of the dryland sub-region are obtained by matching the soil fertility correction index, and fertilization is carried out on the dryland sub-region according to the soil fertilization parameters of the dryland sub-region.

[0109] It should be added that the specific process of obtaining the soil fertility parameters for this dryland sub-region based on the soil fertility correction index is as follows: the soil fertility correction index is compared with the set soil fertility correction index range to obtain the soil fertility parameters.

[0110] It should be added that each soil fertility correction index interval corresponds to specific soil fertilization parameters. 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. Based on the interval, the corresponding soil fertilization parameters are selected for fertilization.

[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 ​​for each dryland sub-region are analyzed under the following conditions:

[0113]

[0114] In the formula, α i β represents the soil moisture regulation index value of the i-th dryland sub-region. i This represents the soil moisture regulation index of the i-th dryland sub-region. This represents the weighting factor corresponding to the set soil moisture regulation index, where γ represents the meteorological characteristic value of the arid region. The value represents the weighting 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 one specific embodiment, Table 1 shows the soil moisture regulation index values ​​corresponding to different soil moisture regulation indices and meteorological characterization values. In this embodiment, the weighting factor corresponding to the soil moisture regulation index is 0.6, and the weighting 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 characterization 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, as the soil moisture regulation index and meteorological characterization value increase, the soil moisture regulation index value also increases. Table 1 lists the values ​​of three different soil moisture regulation indices and meteorological characterization values. The corresponding soil moisture regulation index values ​​are derived through formulas. These soil moisture regulation index values ​​provide the degree of influence of different soil moisture regulation indices and meteorological characterization values ​​on the soil moisture regulation index value. The soil moisture regulation index value comprehensively considers the soil moisture regulation index and meteorological characterization value, providing data support for the assessment of soil moisture regulation index values ​​in various dryland sub-regions and improving the accuracy of soil moisture regulation index value assessment.

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

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

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

[0122] It should be explained that the analysis method in this embodiment combines the soil moisture regulation index of each dryland sub-region with the meteorological characterization value of the dryland region to obtain the soil moisture regulation index value of each dryland sub-region. This helps to more accurately understand the soil moisture status of different dryland sub-regions, thereby reducing unnecessary irrigation and reducing water consumption.

[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 region, and they do not exist independently. For example, the precipitation in the meteorological characterization value directly affects the soil moisture. Higher precipitation usually leads to an increase in soil moisture. Changes in precipitation will directly affect the value of the soil moisture regulation index. By comprehensively analyzing the soil moisture regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the degree of influence of soil moisture on soil moisture regulation can be more accurately assessed.

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

[0125]

[0126] In the formula, ρ i δ represents the soil fertility regulation index value of the i-th dryland sub-region. i This represents the soil fertility adjustment index of the i-th dryland sub-region. This represents the weighting factor corresponding to the set soil fertility adjustment index, and γ represents the meteorological characteristic value of the arid region. The weight factor corresponding to the set meteorological characterization value is represented by i, which represents the number of each dryland sub-region, i = 1, 2, 3, ... n, where n represents the total number of dryland sub-regions, and e represents the natural constant.

[0127] It needs to be explained that, such as Figure 3 As shown, Figure 3 The image shows an example of soil fertility regulation index values, where the x-axis represents the soil fertility regulation index and the y-axis represents the soil fertility regulation index value. In this embodiment, the weighting factor for the soil fertility regulation index is 0.3, and the weighting factor for the meteorological characterization value is 0.7. Three different sets of example parameters are defined in the image, corresponding to different cases of the three curves, and are represented by solid lines, dashed lines, and dotted dashed lines, respectively. The corresponding curve labels are a, b, and c, respectively.

[0128] It should 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 is shown as curve a; when the meteorological characterization value is 1.8, the functional relationship is shown as curve b; and when the meteorological characterization value is 2.5, the functional relationship is shown as curve c. The soil fertility regulation index increases with the increase of the soil fertility regulation index, and the soil fertility regulation index increases accordingly when the meteorological characterization value increases. The curves allow for the rapid and accurate determination of the soil fertility regulation index value, solving the problem in existing technologies where the analysis process is not detailed enough to accurately measure the soil fertility regulation index value, thereby achieving precise regulation of soil fertility.

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

[0130] In one 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 soil fertility adjustment index of each dryland sub-region and the corresponding weight factor of 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 corresponding weight factor of the soil fertility adjustment index.

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

[0132] It should be explained that the analysis method in this embodiment combines the soil fertility regulation index of each dryland sub-region with the meteorological characterization value of the dryland region to obtain the soil fertility regulation index value of each dryland sub-region. This helps to formulate a more accurate fertilization plan. By combining the soil fertility regulation index to understand the nutrient content in the soil, and considering the impact of meteorological factors such as precipitation on nutrient loss, the timing and amount of fertilization can be adjusted to ensure that crops can receive appropriate nutritional support during their 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 region, and they do 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, increase the nutrient content in the soil, and thus reduce the soil fertility regulation index value. By comprehensively analyzing the soil fertility regulation index of each dryland sub-region and the meteorological characterization value of the dryland region, the degree of influence of plant growth on soil fertility regulation can be more accurately assessed.

[0134] Please see Figure 2 As shown, the second aspect of the present invention provides an intelligent control system for supplemental irrigation and topdressing of dryland based on the Internet of Things, including: an agricultural monitoring platform, used to establish an agricultural monitoring platform, divide the dryland area into dryland sub-regions, and monitor and collect soil environmental data and plant growth data of each dryland sub-region.

[0135] The supplementary irrigation and topdressing analysis module is used to analyze soil environmental 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 topdressing adjustment module is used to simultaneously extract meteorological data of dryland areas from the cloud server based on the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-region, comprehensively analyze and obtain the supplementary irrigation and topdressing parameters of each dryland sub-region, and adjust and control the soil moisture and soil fertility of each dryland sub-region.

[0137] It should be noted that the intelligent control method and system for supplementary irrigation and fertilization in dryland based on the Internet of Things also includes an agricultural monitoring database. This database stores reference clay particle percentage, reference total organic matter content, reference soil moisture, reference soil temperature, influencing factors corresponding to clay particle percentage, total organic matter content, soil moisture, and soil temperature, as well as reference vegetation coverage, reference vegetation chlorophyll content, reference plant nutrient content, defined soil pH, influencing factors corresponding to vegetation coverage, vegetation chlorophyll content, plant nutrient content, and soil pH, a meteorological comparison dataset for dryland areas, soil moisture regulation index thresholds for each dryland sub-region, soil fertility regulation index thresholds for each dryland sub-region, weighting factors corresponding to soil moisture regulation index, weighting factors corresponding to meteorological characterization values, weighting factors corresponding to soil fertility regulation index, and weighting factors corresponding to meteorological characterization values.

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0139] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent control method for supplemental irrigation and topdressing in dryland based on the Internet of Things, characterized in that, include: An agricultural monitoring platform was established to divide the dryland areas into sub-regions, and soil environmental data and plant growth data of each sub-region were collected and monitored. Soil environmental data and plant growth data of each dryland sub-region were analyzed to obtain the soil moisture regulation index and soil fertility regulation index of each dryland sub-region. Based on the soil moisture regulation index and soil fertility regulation index of each dryland sub-region, meteorological data of the dryland region in the cloud server are extracted simultaneously, and the supplementary irrigation and topdressing parameters of each dryland sub-region are obtained through comprehensive analysis. The soil moisture and soil fertility of each dryland sub-region are then regulated and controlled. The soil moisture regulation index for each dryland sub-region was obtained using the following analytical conditions: ； In the formula, This represents the soil moisture regulation index of the i-th dryland sub-region. This represents the percentage of clay particles in the soil of the i-th dryland sub-region. This indicates the percentage of the set reference clay particles. This represents the influence factor corresponding to the set percentage of clay particles. This represents the average total organic matter content of the soil in the i-th dryland sub-region. This indicates the set reference total organic matter content. This represents the influencing factor corresponding to the set total organic matter content. This represents the average soil moisture in the i-th dryland sub-region. This indicates the set reference soil moisture. This indicates the influencing factors corresponding to the set soil moisture. This represents the average soil temperature of the i-th dryland sub-region. This indicates the set reference soil temperature. This indicates the influencing factors corresponding to the set soil temperature. Indicates the number of each dryland sub-region. , This represents the total number of dryland sub-regions. Represents the natural constant; The soil fertility adjustment index was obtained using the following analytical conditions: ； In the formula, This represents the soil fertility adjustment index of the i-th dryland sub-region. This represents the vegetation cover rate of the i-th dryland sub-region. This indicates the set reference vegetation coverage rate. This represents the influencing factors corresponding to the set vegetation coverage rate. This represents the average chlorophyll content of vegetation in the i-th dryland sub-region. This indicates the chlorophyll content of the reference vegetation. This represents the influencing factor corresponding to the set vegetation chlorophyll content. This represents the average plant nutrient content of the i-th dryland sub-region. This indicates the nutrient content of the reference plant. This indicates the influencing factors corresponding to the set plant nutrient content. This represents the average soil pH value of the i-th dryland sub-region. This indicates the defined soil pH value. This indicates the influencing factors corresponding to the set soil pH value. Indicates the number of each dryland sub-region. , This represents the total number of dryland sub-regions. Represents the natural constant; Meteorological data of arid regions are extracted from cloud servers to obtain meteorological datasets of arid regions. Meteorological comparison datasets of arid regions are extracted from agricultural monitoring databases and analyzed comprehensively to obtain meteorological characterization values ​​of arid regions. The meteorological characteristics of the arid region were obtained using the following analytical conditions: ； In the formula, Meteorological characterization values ​​representing arid regions This represents the j-th meteorological data point in an arid region. This represents the comparison value of the j-th meteorological data point in the arid region, where j represents the data point number. , This represents the total number of meteorological data. The parameters for supplementary irrigation and topdressing in each dryland sub-region include soil moisture regulation index values ​​and soil fertility regulation index values ​​for each dryland sub-region. Based on the soil moisture regulation index of each dryland sub-region and combined with the meteorological characterization values ​​of the dryland region, the soil moisture regulation index values ​​of each dryland sub-region were analyzed and obtained. Based on the soil fertility regulation index of each dryland sub-region and combined with the meteorological characterization values ​​of the dryland region, the soil fertility regulation index values ​​of each dryland sub-region were analyzed. The soil moisture regulation index value of each dryland sub-region is compared with the set soil moisture regulation index threshold value of each dryland sub-region. If the soil moisture regulation index value of a certain dryland sub-region is higher than the set soil moisture regulation index threshold value of that dryland sub-region, the soil moisture of that dryland sub-region is regulated and controlled. The difference between the soil moisture regulation index value of that dryland sub-region and the set soil moisture regulation index threshold value of that dryland sub-region is recorded as the soil moisture correction index of that dryland sub-region. The soil irrigation parameters of that dryland sub-region are obtained according to the soil moisture correction index value, and the dryland sub-region is irrigated according to the soil irrigation parameters of that dryland sub-region. 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 certain dryland sub-region is higher than the set soil fertility adjustment index threshold value of that dryland sub-region, the soil fertility of that dryland sub-region is adjusted and controlled. The difference between the soil fertility adjustment index value of that dryland sub-region and the set soil fertility adjustment index threshold value of that dryland sub-region is recorded as the soil fertility correction index of that dryland sub-region. Soil fertilization parameters of that dryland sub-region are obtained by matching the soil fertility correction index, and fertilization is carried out on the dryland sub-region according to the soil fertilization parameters of that dryland sub-region.

2. The intelligent control method for supplemental irrigation and topdressing of dryland based on the Internet of Things as described in claim 1, characterized in that: The soil environmental data for each dryland sub-region includes the percentage of clay particles in the soil of each dryland sub-region and the average total organic matter content of the soil in each dryland sub-region. The plant growth data for each dryland sub-region includes the vegetation coverage rate and the average chlorophyll content of the vegetation in each dryland sub-region.

3. The intelligent control method for supplemental irrigation and topdressing of dryland based on the Internet of Things as described in claim 1, characterized in that: The analysis of soil environmental data and plant growth data of each dryland sub-region includes analyzing soil environmental data and analyzing plant growth data of each dryland sub-region. The specific analysis process for analyzing the soil environmental data of each dryland sub-region is as follows: Several soil monitoring points are randomly set up in each dryland sub-region to monitor and collect the soil temperature and humidity of each soil monitoring point, and the average value is processed to obtain the average soil humidity and average soil temperature of each dryland sub-region. The percentage of clay particles and the average total organic matter content of the soil in each dryland sub-region were statistically analyzed. Combined with the average soil moisture and average soil temperature in 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 supplemental irrigation and topdressing of dryland based on the Internet of Things as described in claim 3, characterized in that: The analysis of plant growth data in each dryland sub-region is as follows: Plant samples were randomly collected from each dryland sub-region and analyzed to obtain the average total plant nutrient content of each dryland sub-region. Soil pH values ​​were collected from various soil monitoring points and averaged to obtain the average soil pH value for each dryland sub-region. The vegetation coverage and average chlorophyll content of each dryland sub-region were extracted from the plant growth data of each dryland sub-region. Combined with the average plant nutrient content and average soil pH of each dryland sub-region, the soil fertility regulation index of each dryland sub-region was analyzed.

5. The intelligent control method for supplemental irrigation and topdressing of dryland based on the Internet of Things as described in claim 1, characterized in that: The specific analysis conditions for the soil moisture regulation index values ​​of each dryland sub-region are as follows: ； In the formula, This represents the soil moisture regulation index value for the i-th dryland sub-region. This represents the soil moisture regulation index of the i-th dryland sub-region. This indicates the weighting factor corresponding to the set soil moisture regulation index. Meteorological characterization values ​​representing arid regions This represents the weighting factor corresponding to the set meteorological characterization value. Indicates the number of each dryland sub-region. , This represents the total number of dryland sub-regions.

6. The intelligent control method for supplemental irrigation and topdressing of dryland based on the Internet of Things as described in claim 1, characterized in that: The specific analysis conditions for the soil fertility regulation index values ​​of each dryland sub-region are as follows: ； In the formula, This represents the soil fertility adjustment index value of the i-th dryland sub-region. This represents the soil fertility adjustment index of the i-th dryland sub-region. This represents the weighting factor corresponding to the set soil fertility adjustment index. Meteorological characterization values ​​representing arid regions This represents the weighting factor corresponding to the set meteorological characterization value. Indicates the number of each dryland sub-region. , denoted by , where represents the total number of dryland sub-regions, and e represents the natural constant.

7. An intelligent control system for supplemental irrigation and topdressing of dryland land based on the Internet of Things, employing the intelligent control method for supplemental irrigation and topdressing of dryland land based on the Internet of Things as described in any one of claims 1-6, characterized in that, include: The agricultural monitoring platform is used to establish an agricultural monitoring platform, divide the dryland area into sub-regions, and monitor and collect soil environmental data and plant growth data in each sub-region. The supplementary irrigation and topdressing analysis module is used to analyze soil environmental 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 topdressing adjustment module is used to simultaneously extract meteorological data of dryland areas from the cloud server based on the soil moisture adjustment index and soil fertility adjustment index of each dryland sub-region, comprehensively analyze and obtain the supplementary irrigation and topdressing parameters of each dryland sub-region, and adjust and control the soil moisture and soil fertility of each dryland sub-region.

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