Drip irrigation parameter dynamic regulation and control method combined with multi-point sensor monitoring

Abstract

The invention discloses a drip irrigation parameter dynamic regulation and control method combined with multi-point sensor monitoring, and relates to the technical field of irrigation regulation and control, the method comprises the following steps: monitoring a multi-point sensor arranged in a target area to obtain target monitoring data; performing actual demand analysis on the target crop to obtain actual demand data; analyzing the target soil permeability of the target area to obtain a target irrigation period; and performing irrigation processing according to the actual demand data and the target irrigation period to obtain irrigation data. The technical problems that in the prior art, real-time requirements of crops cannot be accurately obtained, irrigation is difficult to implement according to soil characteristics, and the agricultural production efficiency and sustainability are limited are solved. The effects that multiple types of monitoring data are obtained by arranging the multi-point sensors in the target area, precise irrigation is achieved, the crop growth environment is optimized, good physicochemical properties of soil are maintained, and the technical effects of high efficiency and sustainability of agricultural production are improved are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of irrigation control, and in particular to a method for dynamically controlling drip irrigation parameters in combination with multi-point sensor monitoring. Background Art

[0002] In the field of agricultural irrigation, traditional irrigation methods often rely on experience and fixed irrigation schedules, lacking an accurate grasp of the actual needs of crops and soil characteristics. For example, in many areas, farmers irrigate only based on past experience or fixed irrigation cycles, without fully considering the dynamic changes in the demand for water and nutrients of crops at different growth stages, and without in-depth analysis of key characteristics such as soil permeability, water retention, and salt accumulation. This has led to a serious waste of water resources, and a large amount of irrigation water has not been effectively absorbed and utilized by crops, but has been lost through surface runoff or deep infiltration. At the same time, due to inaccurate irrigation, the salt in the soil cannot be effectively regulated, which can easily lead to an increase in the degree of soil salinization, which has a serious inhibitory effect on the root growth and nutrient absorption of crops, thereby affecting the yield and quality of crops, and cannot meet the requirements of modern agriculture for efficient and sustainable production.

[0003] Existing technologies have technical problems such as being unable to accurately obtain the real-time needs of crops, difficulty in implementing irrigation in accordance with soil characteristics, and limited agricultural production efficiency and sustainability. Summary of the invention

[0004] The present application provides a method for dynamically regulating drip irrigation parameters in combination with multi-point sensor monitoring, which is used to solve the technical problems in the prior art that the real-time needs of crops cannot be accurately obtained, irrigation is difficult to implement in accordance with soil characteristics, and agricultural production efficiency and sustainability are limited.

[0005] In view of the above problems, the present application provides a method for dynamically controlling drip irrigation parameters in combination with multi-point sensor monitoring, the method comprising:

[0006] Target monitoring data is obtained by monitoring the target area with multiple point sensors arranged therein, wherein the target monitoring data includes temperature and humidity monitoring data, salt monitoring data and crop growth monitoring data; actual demand analysis is performed on the target crops in the target area based on the temperature and humidity monitoring data, salt monitoring data and crop growth monitoring data to obtain actual demand data; target soil permeability in the target area is analyzed to obtain a target irrigation cycle; irrigation is performed according to the actual demand data and the target irrigation cycle to obtain irrigation data.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The target monitoring data is obtained by monitoring the target area with multiple sensors; the actual demand analysis of the target crops in the target area is performed based on the temperature and humidity monitoring data, salt monitoring data and crop growth monitoring data to obtain the actual demand data; the target soil permeability of the target area is analyzed to obtain the target irrigation cycle; irrigation is performed based on the actual demand data and the target irrigation cycle to obtain irrigation data. The effect of achieving precise irrigation, optimizing the crop growth environment, and maintaining good physical and chemical properties of the soil by acquiring multiple types of monitoring data through the deployment of multiple sensors in the target area is achieved, and the technical effect of improving the efficiency and sustainability of agricultural production is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a method for dynamically controlling drip irrigation parameters in combination with multi-point sensor monitoring provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a process for obtaining target monitoring data in a method for dynamically controlling drip irrigation parameters in combination with multi-point sensor monitoring provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application provides a method for dynamically controlling drip irrigation parameters in combination with multi-point sensor monitoring, which is used to solve the technical problems in the prior art that the real-time needs of crops cannot be accurately obtained, irrigation is difficult to implement in accordance with soil characteristics, and agricultural production efficiency and sustainability are limited.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Examples, such as Figure 1 As shown, the present application provides a method for dynamically controlling drip irrigation parameters in combination with multi-point sensor monitoring, the method comprising:

[0015] Step S100: obtaining target monitoring data by monitoring the target area with multiple sensors deployed therein, wherein the target monitoring data includes temperature and humidity monitoring data, salt monitoring data and crop growth monitoring data.

[0016] Specifically, monitoring actions are carried out in the target area, that is, key target monitoring data is obtained by reasonably deploying multi-point sensors. First, based on the topography, soil texture distribution, and crop planting layout of the target area, a scientific spatial sampling method is used to determine the location of the sensor to ensure that different soil layers and crop growth areas can be covered, thereby forming a strict and representative monitoring network. The selected sensors are all of high precision and high stability. Among them, the temperature and humidity sensors can accurately sense the temperature and humidity changes in the air and shallow soil in the target area. These data are crucial for understanding the dynamics of heat and water exchange in the crop growth environment. For example, they can reflect the impact of the day and night temperature difference on crop transpiration and the rate of soil water evaporation, providing a basic basis for subsequent judgment of crop water requirements. Salt sensors are precisely placed in key locations in the soil to monitor the salt content in the soil and its distribution changes at different depths in real time. This is critical for assessing the degree of soil salinization and predicting the potential stress of salt on crop roots, because excessive salt may interfere with the absorption of water and nutrients by crops and affect the normal growth and development of crops. Crop growth sensors use advanced technologies such as optical imaging and laser ranging to obtain non-contact growth parameters such as plant height, stem thickness, number of leaves, and leaf area index. These data intuitively show the growth trend of crops. For example, by continuously monitoring the changes in plant height, the growth rate of crops can be determined. Combining the fluctuations in the leaf area index, the photosynthesis efficiency of crops can be understood, and the response of crops to environmental conditions at different growth stages can be fully reflected. Through the collaborative work of these multi-point sensors, temperature and humidity monitoring data, salt monitoring data, and crop growth monitoring data are continuously, stably, and accurately collected, laying a solid data foundation for subsequent in-depth analysis of agricultural production conditions in the target area and the formulation of precise irrigation and management strategies.

[0017] Step S200: performing actual demand analysis on target crops in the target area based on the temperature and humidity monitoring data, the salt monitoring data and the crop growth monitoring data to obtain actual demand data.

[0018] Specifically, first, for the temperature and humidity monitoring data, the crop transpiration model based on the Penman-Monteith equation is used to estimate the crop transpiration. This model comprehensively considers meteorological factors such as temperature, relative humidity, wind speed, solar radiation, and the physiological characteristics of crops (such as crop coefficients). After parameter calibration through a large amount of field test data, it can more accurately calculate the amount of water lost by crops due to transpiration under different temperature and humidity conditions, thereby preliminarily determining the crop's demand for water supplementation. At the same time, the linear regression algorithm is used to analyze the relationship between temperature and crop photosynthesis rate, and a temperature-photosynthesis rate model is established based on historical data. According to the real-time monitored temperature data, the current photosynthesis efficiency of the crop is predicted, and combined with the light demand characteristics of the crop growth stage, it is determined whether the crop is in a suitable temperature environment. If it deviates from the optimal temperature range, it may be necessary to adjust the soil temperature through irrigation or take measures such as shading and ventilation to improve it, which also indirectly affects the water and nutrient demand strategy.

[0019] For salt monitoring data, the salt diffusion-convection equation is combined with the soil water movement model (such as the Richards equation) to simulate the migration and distribution of salt in the soil. Through the real-time monitoring data of soil salt content at different depths, the parameters in the inversion model, such as salt diffusion coefficient, convection velocity, etc., can accurately predict the changing trend of salt over time and irrigation process. The support vector machine (SVM) algorithm is used to establish a classification model between soil salt and the degree of crop growth inhibition. It is trained based on a large number of sample data of crop growth conditions under known salt conditions. The currently monitored soil salt data is input into the model to judge the degree of salt stress to the crop, and then determine the additional water flushing required to alleviate the stress and the types and quantities of nutrients that may need to be supplemented to balance the impact of salt on the absorption function of crop roots.

[0020] For crop growth monitoring data, deep learning algorithms based on image recognition technology (such as convolutional neural networks, CNN) are used to analyze crop morphological characteristics, such as changes in parameters such as plant height, leaf area, and leaf color. By training on a large amount of labeled data of crop images at different growth stages, the CNN model can accurately identify the current growth stage of the crop, and determine the approximate range of the crop's demand for major nutrients such as nitrogen, phosphorus, and potassium at this stage based on the established knowledge base of nutrient requirements of crops at different growth stages (obtained through historical data statistical analysis and agricultural experts' experience summary). At the same time, the ARIMA model is combined to analyze the historical data of crop growth indicators, predict the growth trend of crops in the future, and adjust irrigation and fertilization plans in advance to ensure that the needs of sustained crop growth are met.

[0021] Finally, the above demand analysis results based on temperature, humidity, salinity and crop growth conditions obtained by different algorithms are integrated. The analytic hierarchy process (AHP) is used to determine the weight of each factor in the overall demand assessment. For example, at a certain growth stage, based on experience and actual data, the weight of temperature on crop growth may be 0.3, the weight of salt is 0.2, and the weight of nutrient demand corresponding to the characteristics of the crop growth stage is 0.5. Through the reasonable allocation of weights of each factor by AHP, the preliminary demand data obtained by different algorithms are weighted and summed to obtain comprehensive, accurate and actual demand data that can reflect the actual situation of crops. These data will provide solid data support for subsequent precise irrigation decisions, ensure that the target crops grow under the most suitable environmental conditions, and achieve efficient and sustainable development of agricultural production.

[0022] Step S300: Analyze the target soil permeability of the target area to obtain a target irrigation cycle.

[0023] Specifically, irrigation and water retention simulation is carried out for the target soil in the target area, and various physical property parameters of the target soil are collected in detail, including soil texture (such as the proportion of sand, loam and clay), soil structure (aggregate size and stability), porosity distribution and initial water content. These parameters are input into a professional soil moisture movement model, and different irrigation scenarios are set to simulate the infiltration, redistribution and retention process of soil moisture under actual irrigation conditions. In this process, the moisture content data of each soil depth at different time points are extracted from the simulation results. For the infiltration rate, when the moisture content at the same depth at adjacent time points is found to increase, the infiltration rate of the depth is calculated based on the soil porosity, the area of ​​the simulation area and the time interval; for the drainage rate, when the moisture content decreases, the drainage rate of each depth is calculated based on the soil porosity and related regional and time parameters, and then the importance of each depth level in the entire soil drainage is considered and the corresponding weight is assigned. For example, the weight of deep soil is relatively low, and the weight of shallow root activity areas is high. The drainage rate of each depth is multiplied by its weight and then added up to obtain the water discharge rate that can reflect the overall drainage capacity of the soil. At the same time, the moisture content information corresponding to different soil depth layers at each time node is analyzed. The total water storage capacity of the soil at that moment is obtained by multiplying the moisture content of each depth layer at each time point with the soil volume of that layer and then adding them up. Then, based on the root distribution characteristics of the target crops and their ability to absorb water, the proportion of moisture content in each depth layer that can be effectively utilized by crops is determined, multiplied by the moisture content of the corresponding depth layer and then added up to obtain the total effective moisture in the soil. Finally, the water storage capacity and effective moisture content calculated at each time point from the initial moment to the final simulation moment are added up respectively to obtain the accumulated moisture. Next, according to Darcy's law, the water discharge rate is regarded as the approximate average speed of water flow in the soil. The hydraulic gradient is approximately estimated by analyzing the changes in soil water content at different depths (i.e., the distribution changes of the accumulated water at different depths). For example, the difference between the accumulated water at adjacent depth levels is calculated and divided by the depth interval to obtain an approximate hydraulic gradient value. The water discharge rate and the estimated hydraulic gradient are then substituted into the Darcy's law formula (permeability coefficient = water flow velocity / hydraulic gradient) to preliminarily calculate a permeability coefficient value. Considering the influence of soil texture, pore structure, degree of compaction and other factors on the permeability coefficient, a correction coefficient model is established. For example, a higher correction coefficient is given to sandy soil due to its large pores, while a lower correction coefficient is given to compact clay. The preliminarily calculated permeability coefficient is multiplied by the corresponding correction coefficient. After multiple iterative calculations and parameter adjustments, an accurate target soil permeability index is finally obtained.Afterwards, based on the obtained target soil permeability, combined with the climatic conditions of the target area (such as precipitation, evaporation), the initial soil salt content and the salt tolerance of the target crops planted, a salt dynamic balance model was constructed. Different irrigation cycle durations (such as 3 days, 5 days, 7 days, etc.) were set in the model to simulate the changing trend of soil salinity under different irrigation cycles. The focus was on observing the changes in soil salt concentration in the crop root activity layer within each cycle. At the same time, combined with the sensitivity of the crop growth stage to salt, an irrigation cycle that can both meet the water demand of crop growth and effectively control soil salt accumulation within the crop salt tolerance threshold is determined as the target irrigation cycle, thereby providing a key time dimension basis for precise irrigation decision-making, ensuring that while meeting the crop water needs, a good salt balance in the soil is maintained to promote healthy crop growth.

[0024] Step S400: performing irrigation processing according to the actual demand data and the target irrigation cycle to obtain irrigation data.

[0025] Specifically, based on the ratio of crop water demand and nutrient demand in the actual demand data, combined with the target irrigation cycle, the intelligent irrigation control system is used to accurately adjust the amount and quality of irrigation water, and the variable frequency water pump is used to accurately control the water flow speed and flow rate to ensure that the amount of water for each irrigation strictly meets the requirements in the actual demand data, avoiding the waste of water resources and the loss of soil nutrients or root hypoxia caused by excessive irrigation. At the same time, based on the nutrient demand ratio, with the help of fertilization and irrigation integrated equipment, the appropriate amount of water-soluble fertilizer is accurately mixed into the irrigation water to ensure that the crops can evenly absorb the required nutrients while obtaining water, meeting their nutritional needs for growth and development.

[0026] During the irrigation process, various sensors installed in the field (such as soil moisture sensors, soil conductivity sensors, weather stations, etc.) monitor key parameters such as soil moisture content, salt concentration, and meteorological conditions in real time, and feed these data back to the irrigation control system. The control system dynamically adjusts the irrigation strategy based on pre-set thresholds and real-time monitoring data, such as suspending irrigation when soil moisture is close to saturation, and reducing or stopping irrigation plans when encountering sudden rainfall, to ensure that the irrigation process can closely follow the actual situation and achieve precise irrigation.

[0027] In addition, through flow sensors, pressure sensors, data collectors and other equipment, the start time, end time, irrigation water volume, water flow pressure, fertilizer application amount and other information of each irrigation are recorded in detail, and these records constitute irrigation data. These data not only provide detailed operation records for this irrigation process, which is convenient for subsequent tracing and analysis of irrigation effects, but also serve as important historical data for optimizing subsequent irrigation plans and parameter settings. For example, by statistically analyzing the irrigation data of multiple irrigations, the calculation model of the target irrigation cycle and actual demand data can be further optimized, making irrigation decisions more scientific and reasonable, and continuously improving the intelligence level and adaptability of the irrigation system, thereby creating a more stable and suitable growth environment for the target crops, and achieving efficient water conservation, high yield, high quality and sustainable development of agricultural production.

[0028] In one possible implementation, Figure 2 As shown, step S100 also includes:

[0029] Step S110: traversing and deploying sensors in a hierarchical grid according to the preset deployment density of the target area to obtain preliminary points.

[0030] Step S120: extracting the crop growth stage-water demand relationship obtained by water demand analysis based on the target crop samples of the target crop in the hierarchical grid, wherein the target crop samples are crop samples of the same variety as the target crop.

[0031] Step S130: Based on the relationship between the crop growth stage and the water demand, the sensors at the preliminary points are adjusted vertically to obtain the adjustment points.

[0032] Step S140: Performing horizontal density adjustment of sensors at the adjustment points through soil water loss analysis of the layered grid to obtain the deployment points.

[0033] Step S150: deploying sensors at the deployment points to monitor and obtain the target monitoring data.

[0034] Specifically, based on the topography, soil characteristics (obtained through soil texture analysis, soil fertility testing, etc.), meteorological conditions (using historical meteorological data and on-site meteorological monitoring station data) and target crop planting layout (through field mapping and crop planting planning maps) of the target area, a comprehensive analysis and evaluation is conducted using geographic information systems (GIS) and professional agricultural analysis software. According to the analysis results, the appropriate sensor deployment density is determined to ensure that the environmental changes in the target area can be accurately reflected. For example, in areas with complex terrain and large differences in soil properties, the deployment density is appropriately increased to obtain more detailed monitoring data. Using GIS technology, the target area is divided into multi-level hierarchical grids according to the preset deployment density. Each grid unit has a unique identifier, and its size and shape are adaptively adjusted according to the actual terrain and crop distribution to ensure the uniformity and representativeness of the grid in space. According to the pre-established sensor deployment rules and technical standards, automated sensor deployment equipment (such as drones or intelligent robots equipped with sensor installation devices) is used to deploy sensors in order of the grid. For soil environment monitoring sensors such as soil moisture sensors and salt sensors, they are precisely buried in the soil at different depths (such as 0-20cm, 20-40cm, 40-60cm, etc.) according to the soil depth stratification requirements to ensure that environmental information of different soil layers can be obtained. Meteorological sensors are installed in open, unobstructed locations to accurately measure meteorological parameters such as temperature, relative humidity, wind speed, and precipitation. Crop growth monitoring sensors (such as multispectral imagers, laser rangefinders, etc.) reasonably adjust the installation position and angle according to the spacing between crop rows and growth height to ensure that the growth status of crops can be effectively monitored. Through this process, preliminary points are obtained and a sensor monitoring network covering the target area is initially established.

[0035] In the stratified grid, stratified sampling technology is used to select multiple representative areas, and samples of target crops of the same variety as the target crops are collected from these areas. The selection of samples fully considers factors such as the growth stage, health status, soil and climate environment of the crops, to ensure that the samples can accurately reflect the general growth characteristics of the target crops in the target area. The collected samples are processed under a strictly controlled experimental environment, using advanced plant physiological monitoring equipment (such as high-precision transpiration rate meter, stomatal conductance meter, leaf area meter, plant growth monitoring system, etc.), precision weighing system, soil moisture monitoring equipment and environmental simulation control system. Before the experiment begins, all equipment is calibrated and debugged to ensure the accuracy and reliability of the measurement data. Starting from the germination period of the crop, by accurately controlling the environmental temperature, humidity, light intensity and other conditions (using the environmental simulation control system), the high-precision weighing method is used to measure the water absorption of seeds under different humidity conditions, and the key nodes of seed germination (such as the time when the radicle breaks through the seed coat, the growth of the embryo, etc.) are recorded in combination with microscopic observation techniques (such as microscopes) to determine the minimum water requirement and suitable humidity range for this stage. At the same time, plant physiological monitoring equipment is used to measure physiological indicators such as respiratory rate and enzyme activity during seed germination, and analyze their relationship with water demand. Entering the seedling stage, as the crop root system begins to grow, the leaves gradually unfold, and transpiration gradually increases. At this time, an advanced transpiration rate meter is used to monitor the changes in the transpiration rate of the seedlings in real time under different combinations of light intensity (by adjusting the intensity of artificial light sources), temperature (using a temperature control system) and soil moisture (adjusted by a soil moisture control system). At the same time, combined with soil moisture sensor data, the energy balance principle and water vapor diffusion theory are used to calculate the amount of water supply required for the seedlings to maintain normal physiological activities. The plant growth monitoring system is used to record the growth parameters of the seedlings (such as plant height, stem thickness, number and area of ​​leaves, etc.), analyze their dynamic relationship with water demand, and clarify the changing rules of water demand in the seedling stage with environmental factors. When the crop grows to the vegetative growth period, the plant grows rapidly, the leaf area increases, photosynthesis is vigorous, and the water demand increases significantly. With the help of a large-scale plant growth monitoring system, we closely track the morphological changes of plants (such as the dynamic growth of parameters such as stem thickness, plant height, number and area of ​​leaves) and changes in physiological indicators (such as photosynthetic rate, chlorophyll content, etc.). At the same time, using an improved algorithm based on the Penman-Montes formula, combined with real-time meteorological data (obtained by meteorological sensors) and soil moisture content data (measured by soil moisture sensors), we calculate the potential evapotranspiration at this stage and convert it into actual crop water demand through the crop coefficient method. A database of the correspondence between crop growth stages and crop coefficients established through a large amount of field test data ensures the accuracy of the calculation results and accurately grasps the water demand law during the vegetative growth period. Entering the flowering period, crops have extremely high requirements for the stability and timeliness of water supply.Using high-resolution multispectral imaging and microscopic observation techniques, the development process of flower organs (such as flower bud differentiation, bud formation, petal unfolding, etc.) is monitored, and changes in key reproductive physiological processes such as flower bud differentiation, pollen germination, and pollen tube elongation under different water conditions are analyzed. At the same time, combined with soil moisture dynamic monitoring data, the optimal water demand range of crops during the flowering period is determined to ensure that sufficient and appropriate water supply is provided to crops during this critical period to ensure the smooth progress of the reproductive process. In the fruiting period, the focus of crop energy allocation shifts to the growth and development of fruits. The expansion and quality formation of fruits require a large amount of water to participate in the material transportation and metabolic processes. Through fruit growth monitors (such as laser rangefinders or image analysis systems to measure fruit volume changes) and water metabolism analysis techniques (such as using stable isotope tracer technology to study the transport and metabolic pathways of water in fruits), the relationship between the fruit volume growth rate and soil water supply is studied, and the accumulation process of quality components (such as sugar and vitamin content) in the fruit is monitored in relation to water supply. Using the multiple linear regression analysis algorithm, taking fruit growth-related parameters as dependent variables and soil moisture content, meteorological factors, etc. as independent variables, a fruit development stage-water demand model was established to clarify the changing trend of water demand at different stages of the fruiting period, providing an accurate basis for water management to ensure fruit yield and quality. Through systematic and in-depth analysis and data collation of the water requirements of target crop samples at various growth stages, a detailed and accurate relationship between crop growth stage and water demand was successfully extracted, demonstrating the dynamic changes in water demand of crops at different growth stages, providing an indispensable scientific basis for subsequent precise irrigation decisions based on actual monitoring data.

[0036] According to the established crop growth stage-water demand relationship, the initial points are adjusted vertically using automated sensor adjustment equipment (such as a liftable sensor mounting bracket or an intelligent sensor burial depth adjustment device). In the early stage of crop root growth, the roots are mainly distributed in the shallow soil layer. At this time, the focus is on the shallow soil (such as 0-20cm) to appropriately increase the density of soil moisture sensors and nutrient sensors to more sensitively monitor soil moisture and nutrient changes and ensure that a suitable growth environment can be provided for crops in a timely manner. For example, a humidity sensor is added every 5cm in the shallow soil, and a nutrient sensor is added every 10cm. As the crop grows, the root system gradually extends downward, and the number of sensors is reasonably increased in the deeper soil layer (such as 20-40cm, 40-60cm, etc.). Through soil porosity analysis and root growth model prediction, the main distribution depth range of the root system at different growth stages is determined, and the sensor layout is encrypted in these key depth layers. At the same time, combined with the changes in the demand for soil moisture and nutrients at different depths of crops at different growth stages, the type of sensor and monitoring parameters are adjusted. For example, during the period of vigorous root growth, additional sensors are added to monitor key nutrients such as nitrate nitrogen and available potassium in the deep soil, and the measurement accuracy of soil moisture sensors is improved to meet the crop's demand for deep soil resources. Through this vertical encryption adjustment based on crop growth needs, more precisely positioned adjustment points are obtained in the vertical dimension, further improving the accuracy and comprehensiveness of soil moisture and nutrient monitoring, so that it can better reflect the actual resource supply situation faced by crop roots.

[0037] Use professional equipment such as soil conductivity meters and soil moisture characteristic curve meters to conduct a detailed analysis of the soil water loss in each area of ​​the layered grid. By measuring the change in soil conductivity at different water contents, the water retention and water loss rate information of the soil is obtained. At the same time, the change in soil moisture content under different suction forces is analyzed in combination with the soil moisture characteristic curve to determine the soil water conductivity characteristics. According to these analysis results, the adjustment points are adjusted horizontally using an intelligent sensor mobile platform (such as a mobile robot with autonomous navigation and positioning functions). For areas with strong soil water loss, such as sandy soils or areas with high terrain and good drainage, more intensive sensor monitoring is required because their water is easily lost. In these areas, the horizontal spacing between sensors is reduced, for example, the original spacing of 10 meters is adjusted to 5 meters to ensure that the rapid changes in soil moisture can be captured in a timely and accurate manner. At the same time, the density of salt sensors is increased to monitor salt accumulation or leaching that may be caused by frequent changes in moisture. For areas with weak soil water loss, such as clay soil or low-lying areas prone to water accumulation, although the moisture changes relatively slowly, the distribution of sensors needs to be adjusted appropriately according to the actual situation. In these areas, the focus is on monitoring the long-term dynamic changes in soil moisture and possible water saturation, and appropriately increasing soil aeration and redox potential sensors to assess the potential impact of the soil environment on crop growth. Through this fine-grained encryption adjustment in the horizontal direction, the spatial differences in soil characteristics are comprehensively considered, making the distribution of the deployment points on the horizontal plane more reasonable, thereby comprehensively improving the monitoring network's perception of soil moisture and related environmental factors in the target area.

[0038] Carry out the sensor deployment operation according to the deployment points determined in the previous steps. Use professional installation equipment, such as high-precision soil drilling equipment, to accurately drill holes at various points according to the preset depth (such as 0-20cm, 20-40cm, 40-60cm, etc.), place the soil moisture sensor smoothly and fill it tightly to ensure seamless contact with the soil and accurately capture soil moisture changes. When installing the salt sensor, pay attention to the location selection to prevent external interference and ensure accurate salt measurement. The meteorological sensor is installed in an open and unobstructed place, fixed with a stable bracket to accurately obtain meteorological data. The crop growth monitoring sensor reasonably determines the position and angle according to the crop growth trend. After the installation is completed, fully debug the system to check the stability of the sensor power supply, the smoothness of data transmission and the measurement accuracy. After startup, the sensor automatically collects data at set time intervals (such as 15 minutes), covering information such as soil moisture, salinity, weather and crop growth, and transmits it to the data processing center in real time via wireless or wired networks. After quality inspection (eliminating outliers and supplementing missing data) and preprocessing at the center, the data is stored by category and spatiotemporal dimensions, providing a reliable basis for subsequent analysis and precise irrigation decisions.

[0039] In a possible implementation, step S110 further includes:

[0040] Step S111: Divide the target area into regions based on the topography and slope to obtain climate monitoring units.

[0041] Step S112: extracting a target high point and a target low point according to the climate monitoring unit as meteorological points.

[0042] Step S113: deploying temperature sensors and humidity sensors at the meteorological points, and obtaining the deployment density.

[0043] Specifically, the topography and slope of the target area are first comprehensively and accurately measured and analyzed, and detailed topographic data are obtained using advanced topographic mapping technologies (such as satellite remote sensing mapping, drone topography mapping, high-precision GPS measurement, etc.). These data cover key information such as elevation changes, slope steepness, and topographic undulation characteristics in the target area. Based on these topographic data, the target area is divided into several sub-areas with similar topographic and climatic characteristics using the powerful spatial analysis function of the geographic information system (GIS). These sub-areas are climate monitoring units. In the division process, the influence of topography and slope on the distribution of climate elements (such as temperature, humidity, precipitation, light, etc.) is fully considered. Areas with higher terrain usually have lower temperatures and higher wind speeds, while low-lying areas may be more prone to cold air accumulation and relatively higher humidity. The size and direction of the slope will affect the amount of solar radiation received and the runoff of precipitation, thereby affecting the local climate. Through GIS analysis tools, areas with similar topographic features (such as similar elevation ranges, slope levels, etc.) and expected similar climate performance are merged and divided to ensure that the climate conditions within each climate monitoring unit are relatively consistent, providing a reliable geographical basis for subsequent accurate meteorological monitoring and irrigation decision-making.

[0044] In each climate monitoring unit that has been divided, the terrain data is deeply analyzed to extract representative target high points and target low points as meteorological points. The selection of the target high point is based on its relatively high altitude in the unit. This location can often reflect the extreme conditions of the temperature, wind speed, light and other climate elements affected by the altitude in the area, and it also has an indicative effect on the flow and accumulation of cold air. By monitoring the high-point meteorological data, we can understand the meteorological extremes such as the lowest temperature and maximum wind speed that may occur in the climate monitoring unit, providing a basis for assessing the growth risk of crops under adverse climatic conditions such as low temperature and strong wind. The determination of the target low point focuses on its low-lying characteristics. Such locations are easily affected by cold air deposition, water vapor accumulation and water flow convergence. They are usually humid, temperature changes are relatively slow, and there may be local waterlogging. The deployment of meteorological sensors at low points can monitor the upper limit of humidity in the unit and the relatively stable state of temperature, which is helpful for analyzing the accumulation and evaporation of water in the area. It is of great significance for studying soil moisture balance, crop root moisture environment and potential flood risks. The combined meteorological data of these target high and low points can comprehensively characterize the climate gradient changes within the climate monitoring unit and provide key data support for precision agricultural meteorological services.

[0045] For the selected meteorological points, carefully arrange the temperature sensors and humidity sensors. At the target high point and target low point, select the appropriate sensor installation location and method according to the technical specifications and requirements of meteorological monitoring. Generally speaking, the temperature sensor should be installed in a well-ventilated location that avoids direct sunlight and can represent the ambient air temperature. It is usually fixed at a certain height (such as 1.5-2 meters) from the ground using a special meteorological sensor bracket to ensure the accuracy of the measurement. The humidity sensor needs to be installed in a location with smooth air circulation and not interfered by local water vapor sources. At the same time, care should be taken to avoid direct exposure of the sensor to rainwater. It can be protected by a protective cover or other devices. After completing the sensor arrangement at the meteorological point, the sensor arrangement density is calculated by counting the number and distribution of meteorological points in each climate monitoring unit and combining the area size of the unit. The arrangement density is expressed as the number of meteorological points per square kilometer. For example, if a climate monitoring unit covers an area of ​​5 square kilometers and has 10 meteorological points (including high points and low points), the sensor arrangement density of the unit is 2 / square kilometer. This deployment density indicator not only reflects the coverage of meteorological monitoring in the target area, but also provides an important basis for the subsequent evaluation of the representativeness and reliability of the monitoring data. It also provides a reference standard for optimizing the sensor layout under different terrain and climatic conditions, ensuring that sufficiently accurate and spatially representative meteorological data can be obtained to support precision irrigation decisions and agricultural production management.

[0046] In a possible implementation, step S140 further includes:

[0047] Step S141: analyzing the irrigation stage according to the temperature and humidity monitoring data of the temperature sensor and the humidity sensor to obtain the target irrigation stage.

[0048] Step S142: soil water loss identification is performed according to the humidity monitoring data and the soil water loss humidity threshold to obtain a target water loss point.

[0049] Step S143: performing sensor encryption adjustment according to the target water loss point to obtain the deployment point.

[0050] Specifically, the estimated transpiration rate of crops is calculated based on temperature data, and the evaporation capacity of the atmosphere is evaluated in combination with humidity data. When the temperature is high and the humidity is low, the transpiration of crops is enhanced, and the evaporation demand of the atmosphere is also large. At this time, it is in the vigorous growth period with more water demand or the drought warning stage. It is necessary to pay close attention to the soil moisture condition and consider increasing the irrigation amount in time. On the contrary, when the temperature is low and the humidity is high, the transpiration of crops and atmospheric evaporation are relatively weak. In the slow growth period of crops or the stage with relatively low water demand, the irrigation strategy can be appropriately adjusted to reduce the irrigation amount or extend the irrigation interval. By comprehensively considering the change trend, fluctuation amplitude and correlation of temperature and humidity data on different time scales with the crop growth cycle, the current irrigation stage of the target area can be accurately judged, such as the small amount of multiple irrigation stage in the germination period, the sufficient irrigation stage in the growth period, and the moderate control irrigation stage in the maturity period, etc., thereby providing an important basis in the time dimension for subsequent soil water loss identification and precise irrigation decision-making.

[0051] The real-time humidity monitoring data obtained by the humidity sensor is compared and analyzed with the pre-set soil moisture loss threshold to identify soil moisture loss. The soil moisture loss threshold is a set of critical values ​​determined based on the minimum soil moisture demand of the target crop at different growth stages and the water retention characteristics of the soil. These thresholds can be obtained through a large number of field experiments, soil moisture characteristic curve determination, and crop growth response experiments. At each monitoring point, the current soil moisture measurement value is compared with the moisture loss threshold of the corresponding growth stage one by one. If the soil moisture at a certain point continues to be lower than the threshold for a certain period of time (such as 2-3 consecutive monitoring time intervals), it is determined that the point has obvious soil moisture loss and is marked as a potential target moisture loss point. At the same time, considering the spatial distribution differences of soil moisture, combined with factors such as the topography and soil texture of the target area, the target moisture loss point is further screened and confirmed. For example, in areas with higher terrain and coarser soil texture (such as sandy soil), soil moisture is more likely to be lost. Even if the humidity is slightly higher than the threshold, if its downward trend is obvious and the moisture content is lower than that of the surrounding areas, it may also be identified as a target moisture loss point. In this way, by integrating time and space factors, the target water loss points in the target area that really need to be focused on and measures taken can be accurately identified, providing spatial positioning information for targeted sensor encryption adjustment.

[0052] For the determined target water loss points, implement sensor encryption adjustment measures to obtain the final deployment points. According to the distribution of the target water loss points and the spatial gradient of soil moisture changes, determine the method and density of sensor encryption. In areas where soil moisture changes drastically and the risk of water loss is high (such as near the top of the slope, transitional zones with large differences in soil texture, etc.), increase the density of sensor deployment, for example, reduce the original spacing from 10 meters to 5 meters or even less; in areas where moisture changes relatively slowly, moderately increase the number of sensors, but maintain a relatively large spacing, and optimize the economy and rationality of sensor layout while ensuring monitoring accuracy. For newly added sensors, select a suitable installation location and depth to ensure that the dynamic changes of soil moisture in the target water loss point and its surrounding areas can be accurately monitored. The installation location should be as close as possible to the crop root activity layer, and the depth is determined according to the distribution characteristics of the crop root system and the law of soil moisture changes. Generally, it is set in layers within the soil layer range of 0-60cm, such as installing sensors at depths of 0-20cm, 20-40cm, 40-60cm, etc., to obtain moisture information in different soil layers. Through this sensor encryption adjustment process based on the target water loss point, the layout points of the entire monitoring network are optimized and upgraded, making the sensor layout more in line with the actual distribution status and changing trends of soil moisture, improving the accuracy and timeliness of monitoring data, and providing more targeted and reliable data support for precision irrigation systems, ensuring that the water needs of crops at different growth stages can be met in a timely and effective manner, and achieving the goals of efficient water-saving irrigation and high-yield and high-quality crop growth.

[0053] In a possible implementation, step S200 further includes:

[0054] Step S210: Based on the target crop samples of the target crop in the target area, the crop growth stage-water demand relationship and the crop growth stage-salt demand relationship obtained by water requirement and salt tolerance analysis are extracted, wherein the target crop samples are crop samples of the same variety as the target crop.

[0055] Step S220: performing actual water demand analysis according to the crop growth stage-water demand relationship and the temperature and humidity monitoring data to obtain actual water demand data.

[0056] Step S230: performing actual salt requirement analysis according to the crop growth stage-salt requirement relationship and the salt monitoring data to obtain actual salt requirement data.

[0057] Step S240: combining the actual water demand data and the actual salt demand data to obtain the actual demand data.

[0058] Specifically, target crop samples of the same variety as the target crop are carefully selected in the target area. The selection of these samples fully considers the representativeness of their growth environment, including soil type, terrain conditions, light duration, and the influence of surrounding vegetation, to ensure that they can accurately reflect the actual growth characteristics of the target crop in the area. The collected samples are subjected to a comprehensive and in-depth analysis of water demand and salt tolerance under strictly controlled experimental conditions using advanced plant physiological monitoring equipment and precision analytical instruments. For water demand analysis, various climatic conditions and soil moisture conditions that crops may encounter during their natural growth are simulated. By precisely controlling the ambient temperature, humidity, light intensity, and soil moisture content, a high-precision weighing system and transpiration rate meter are used to measure key indicators such as water absorption, transpiration loss, and water use efficiency of crops at different growth stages (from seed germination, seedling stage, vegetative growth stage, flowering stage to fruiting stage). At the same time, combined with the dynamic monitoring data of morphological changes (such as plant height, stem thickness, number and area of ​​leaves, etc.) and physiological parameters (such as stomatal conductance, cell turgor pressure, etc.) during crop growth, mathematical models and statistical analysis methods are used to deeply explore the intrinsic relationship between crop growth and water demand, so as to establish a detailed and accurate crop growth stage-water demand relationship model. In terms of salt tolerance analysis, a series of soil solutions with different salt concentrations are configured, and crop samples are planted in these solution environments to simulate different degrees of salinized soil conditions. Closely observe the growth performance of crops at various growth stages, including indicators such as germination rate, survival rate, growth rate, leaf color and morphological changes, and root development. Use ion chromatographs and other equipment to measure changes in salt content in plants, analyze the absorption, transportation and accumulation mechanisms of crops for salt, determine the maximum salt concentration threshold that crops can tolerate at different growth stages, and then construct a crop growth stage-salt demand relationship model. These relationship models are presented in the form of mathematical functions or charts, providing a scientific basis for subsequent actual demand analysis. Based on the established crop growth stage-water demand relationship model, actual water demand analysis is carried out in combination with real-time temperature and humidity monitoring data, and the potential transpiration rate is calculated using meteorological formulas. Taking into account the crop growth stage, atmospheric environment and soil moisture conditions, the water balance principle and water demand calculation model are used to accurately obtain actual water demand data, providing a key basis for irrigation water volume decisions.

[0059] Based on the crop growth stage-salt demand relationship model, the actual salt demand analysis is carried out in combination with real-time salt monitoring data. The salt data is first spatially interpolated to obtain a distribution map, and then compared with the appropriate salt concentration range of the current crop growth stage to determine whether the soil salt condition is suitable, providing a basis for salt regulation.

[0060] The actual water demand data is integrated with the actual salt demand data to form the actual demand data. This data fully reflects the water and salt requirements of crops, provides decision support for the precision irrigation system, realizes the coordinated regulation of water, fertilizer and salt, ensures the optimal growth environment for crops, and promotes the precision and sustainable development of agricultural production.

[0061] The actual water demand data and the actual salt demand data are integrated to form the actual demand data. This data fully reflects the water and salt needs of crops, provides decision support for the precision irrigation system, realizes the coordinated regulation of water, fertilizer and salt, ensures the optimal growth environment for crops, and promotes the precision and sustainable development of agricultural production.

[0062] In a possible implementation, step S300 further includes:

[0063] Step S310: performing irrigation retention simulation based on the target soil of the target area, evaluating the drainage effectiveness of the target soil to obtain the target soil permeability.

[0064] Step S320: performing salt erosion accumulation analysis according to the target soil permeability to obtain the target irrigation cycle.

[0065] Specifically, first, the physical property parameters of the target soil are collected in detail, including soil texture (such as the proportion of sand, loam, and clay), soil structure (aggregate size and stability), porosity distribution, and initial water content. These parameters are the basis for accurately simulating soil water movement. The accuracy and representativeness of the parameters obtained are ensured by combining field sampling and laboratory analysis. These parameters are input into the soil water movement model, and different irrigation scenarios are set to simulate the infiltration, redistribution, and retention of soil water under actual irrigation conditions. During the simulation, the effects of irrigation intensity, irrigation time, surface cover, and soil anisotropy on water movement are considered. After the simulation, the drainage effectiveness of the target soil is evaluated based on the distribution changes of soil water at different soil depths and at different times. The evaluation indicators of drainage effectiveness include the rate of soil water withdrawal, water accumulation time, and the frequency of different soil layers reaching saturated water content. If the soil water can withdraw in a short time and water accumulation occurs less frequently, it indicates that the soil drainage performance is good; otherwise, the drainage performance is poor. Through comprehensive analysis of these indicators, the target soil permeability index is calculated, which quantitatively reflects the ability of the soil to allow water to pass through, providing key soil hydraulic characteristic parameters for subsequent salt erosion accumulation analysis.

[0066] According to the target soil permeability obtained in the previous steps, salt scouring and accumulation analysis is carried out to determine a reasonable target irrigation cycle. First, a salt dynamic balance model is constructed based on the climatic conditions of the target area (such as precipitation and evaporation), the initial soil salt content, and the salt tolerance of the target crops planted. In the model, soil permeability is one of the key factors affecting salt migration. High permeability soil is conducive to the leaching of salt with water, but it may also lead to the rapid loss of soil nutrients; low permeability soil may cause salt to accumulate in the soil, increasing the risk of soil salt damage. Taking these factors into account, the scouring and accumulation of salt in the soil are analyzed by simulating the changing trend of soil salt under different irrigation cycles. During the simulation process, different irrigation cycle lengths (such as 3 days, 5 days, 7 days, etc.) are set to observe the changes in soil salt concentration in different soil layers in each cycle, especially the salt accumulation in the crop root activity layer. At the same time, combined with the sensitivity of crops to salt during their growth stages, an irrigation cycle that can both meet the water needs of crop growth and effectively control soil salt accumulation within the salt tolerance threshold of crops is determined as the target irrigation cycle. For example, if the simulation results show that a 7-day irrigation cycle can maintain soil salt concentration at a low and stable level while ensuring that the soil moisture content is suitable for crop growth and does not exceed the upper limit of crop salt tolerance, then 7 days can be determined as the target irrigation cycle for the target area, providing an important basis in the time dimension for precise irrigation decision-making, ensuring that while meeting crop water needs, a good salt balance in the soil is maintained to promote healthy crop growth.

[0067] In a possible implementation, step S310 further includes:

[0068] Step S311: Analyze the infiltration rate and drainage rate based on the irrigation simulation results to obtain the water drainage rate.

[0069] Step S312: analyzing the water storage capacity and the effective water content according to the irrigation simulation result to obtain the accumulated water content.

[0070] Step S313: combining the water discharge rate and the accumulated water amount to obtain the target soil permeability.

[0071] Specifically, the water content data of each soil depth at different time points are extracted from the irrigation simulation results. For the infiltration rate, when the water content at the same depth at adjacent time points is found to increase, the increase is calculated, and the infiltration rate of the depth is calculated by combining the soil porosity, the area of ​​the simulation area and the time interval. For the drainage rate, when the water content decreases, the decrease is calculated, and the drainage rate of each depth is calculated based on the soil porosity and related regional and time parameters. Next, considering the importance of each depth level in the entire soil drainage, the corresponding weight is assigned, such as the weight of the deep soil is relatively low, and the weight of the shallow root activity area is high. Finally, the drainage rate of each depth is multiplied by its weight and added up to obtain the water discharge rate that can reflect the overall drainage capacity of the soil.

[0072] Analyze the irrigation simulation results and accurately extract the moisture content information corresponding to different soil depth layers at each time node. For the analysis of water storage, multiply the moisture content of each depth layer at each time point by the soil volume of that layer, and then add up the results of all depth layers, so that the total water storage of the soil at that moment can be obtained. When calculating effective water, the proportion of moisture content that can be effectively utilized by crops in each depth layer is determined based on the root distribution characteristics of the target crop and its ability to absorb water. By multiplying this proportion with the moisture content of the corresponding depth layer, and then adding up the effective moisture content of each depth layer, the total effective moisture in the soil is obtained. Finally, the water storage and effective moisture content calculated at each time point from the initial moment to the final simulation moment are added up respectively. The cumulative result of the two is the required cumulative moisture, which comprehensively reflects the water storage and effective utilization of the soil during the entire irrigation process.

[0073] According to Darcy's law, soil permeability is related to water flow velocity and hydraulic gradient. Given the water drainage velocity, it can be regarded as the approximate average velocity of water flow in the soil. For the hydraulic gradient, it can be estimated approximately by analyzing the changes in soil water content at different depths (i.e., the distribution changes of the accumulated water at different depths). For example, the difference between the accumulated water at adjacent depth levels is calculated and divided by the depth interval to obtain an approximate hydraulic gradient value. Then, the water drainage velocity and the estimated hydraulic gradient are substituted into the Darcy's law formula (permeability coefficient = water flow velocity / hydraulic gradient) to preliminarily calculate a permeability coefficient value. Next, considering the influence of factors such as soil texture (such as classification based on the proportion of sand, loam, and clay), pore structure (based on parameters such as porosity and pore size distribution), and compaction degree (measured by indicators such as soil bulk density) on the permeability coefficient, a correction coefficient model is established. For example, for sandy soil, a higher correction coefficient is given because of its large pores and relatively smooth water flow; for compact clay, the correction coefficient is lower. The preliminarily calculated permeability coefficient is multiplied by the corresponding correction coefficient. After multiple iterative calculations and parameter adjustments, the accurate target soil permeability index is finally obtained. This index can reflect the actual ability of the soil to allow water to pass through, and provide key parameter basis for subsequent related analysis.

[0074] In a possible implementation, step S400 further includes:

[0075] Step S410: performing irrigation configuration according to the actual demand data under the target irrigation cycle, associating irrigation parameters with the target monitoring data, and obtaining control parameters.

[0076] Step S420: generating an irrigation control strategy according to the control parameters, performing irrigation processing based on the irrigation control strategy, and obtaining the irrigation data.

[0077] Specifically, irrigation configuration is performed for actual demand data according to the target irrigation cycle. The actual demand data includes the actual water demand data and actual salt demand data obtained from the previous analysis, which reflect the precise demand of crops for water and salt at the current growth stage. For the water demand part, the total water demand is evenly distributed to each irrigation period according to the target irrigation cycle. At the same time, the water holding characteristics and water loss of the soil are considered, and the preliminary water volume adjustment is performed through the soil water balance equation to ensure that each irrigation volume can meet the growth needs of crops without causing soil water accumulation or excessive water loss. For the salt demand part, according to the soil salt dynamic change model and combined with the target irrigation cycle, the salt content required for each irrigation or leaching is calculated to maintain the soil salt within the range suitable for crop growth. Then, the irrigation parameters (such as irrigation volume, irrigation time, irrigation frequency, salt supplementation or leaching volume, etc.) are associated with the target monitoring data (including soil moisture, salt concentration, meteorological conditions such as temperature, humidity, light, etc.). When establishing the association model, the neural network algorithm is used to train and optimize the target monitoring data as the independent variable and the irrigation parameters as the dependent variable. Through the study of a large amount of historical data, it is found that when the soil moisture is lower than a certain threshold, the irrigation amount should be increased by a certain proportion; when the temperature rises and the light is enhanced, the water evaporation speeds up, and the irrigation frequency may need to be appropriately increased. In this way, the optimal irrigation parameter combination under different monitoring data conditions is determined, and then the control parameters are obtained. These control parameters can dynamically adjust the irrigation configuration according to the actual environment and soil conditions, and improve the accuracy and adaptability of irrigation.

[0078] A specific irrigation control strategy is generated based on the control parameters obtained above. The irrigation control strategy includes details such as determining the start and end time of each irrigation, the precise control method of the irrigation amount, and the timing and method of implementing salt control measures. For example, if the control parameters show that the current soil moisture is low and the temperature is high, the irrigation strategy is set to irrigate with a large amount of water in the early morning or evening when the temperature is relatively low, and at the same time cooperate with appropriate salt leaching operations to replenish water and reduce soil salt concentration. Based on this irrigation control strategy, irrigation is carried out. During the irrigation process, sensors installed in the field are used to monitor the changes in parameters such as soil moisture and salt concentration in real time, and these real-time data are fed back to the control system. The control system dynamically adjusts the operating status of irrigation equipment (such as sprinklers, drip irrigation pipes, water pumps, etc.) based on the preset control parameters and real-time monitoring data comparison and analysis to ensure that the actual irrigation process is strictly executed in accordance with the predetermined strategy. By recording the actual operating parameters of each irrigation (such as actual irrigation volume, actual irrigation time, actual salt control amount, etc.), the irrigation data is summarized. This data can not only be used to evaluate the effect of this irrigation, but also provide data support for subsequent irrigation decision optimization, realize closed-loop control and continuous improvement of the irrigation process, so as to achieve the goals of precise irrigation, improve crop yield and quality, and save water resources.

[0079] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0081] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for dynamically controlling drip irrigation parameters using multi-point sensor monitoring, characterized in that: include: Target monitoring data is obtained by monitoring the target area with multiple sensors, wherein the target monitoring data includes temperature and humidity monitoring data, salt monitoring data and crop growth monitoring data; Performing actual demand analysis on the target crops in the target area based on the temperature and humidity monitoring data, the salt monitoring data, and the crop growth monitoring data to obtain actual demand data; Analyzing the target soil permeability of the target area to obtain a target irrigation cycle; The irrigation data is obtained by performing irrigation processing according to the actual demand data and the target irrigation cycle.

2. The method for dynamic control of drip irrigation parameters combined with multi-point sensor monitoring as claimed in claim 1, characterized in that: Target monitoring data is obtained by monitoring the target area with multiple sensors, including: Traversing and deploying sensors in a hierarchical grid according to the preset deployment density of the target area to obtain preliminary points; Based on the target crop samples of the target crop in the hierarchical grid, extracting the crop growth stage-water demand relationship obtained by water demand analysis, wherein the target crop samples are crop samples of the same variety as the target crop; Based on the relationship between the crop growth stage and the water demand, the sensors at the preliminary points are adjusted vertically to obtain adjustment points; Performing horizontal density adjustment of sensors on the adjustment points through soil water loss analysis of the layered grid to obtain the deployment points; Sensors are deployed at the deployment points to monitor and obtain the target monitoring data.

3. The method for dynamic control of drip irrigation parameters combined with multi-point sensor monitoring as claimed in claim 2, characterized in that: Preset deployment density, including: Divide the target area into regions based on the topography and slope to obtain climate monitoring units; Extracting a target high point and a target low point as meteorological points according to the climate monitoring unit; Temperature sensors and humidity sensors are deployed at the meteorological points to obtain the deployment density.

4. The method for dynamic control of drip irrigation parameters combined with multi-point sensor monitoring as claimed in claim 3, characterized in that: The horizontal density adjustment of the sensors at the adjustment points is performed through the soil water loss analysis of the layered grid to obtain the deployment points, including: The watering stage is analyzed based on the temperature and humidity monitoring data of the temperature sensor and the humidity sensor to obtain the target watering stage; Perform soil water loss identification based on the humidity monitoring data and the soil water loss humidity threshold to obtain a target water loss point; The sensor encryption adjustment is performed according to the target water loss point to obtain the deployment point.

5. The method for dynamic control of drip irrigation parameters combined with multi-point sensor monitoring as claimed in claim 1, characterized in that: Based on the temperature and humidity monitoring data, the salt monitoring data and the crop growth monitoring data, actual demand analysis is performed on the target crops in the target area to obtain actual demand data, including: Based on the target crop samples of the target crop in the target area, extracting the crop growth stage-water demand relationship and the crop growth stage-salt demand relationship obtained by water requirement and salt tolerance analysis, wherein the target crop samples are crop samples of the same variety as the target crop; Performing actual water demand analysis based on the crop growth stage-water demand relationship and the temperature and humidity monitoring data to obtain actual water demand data; Performing actual salt requirement analysis based on the crop growth stage-salt requirement relationship and the salt monitoring data to obtain actual salt requirement data; The actual water demand data and the actual salt demand data are combined to obtain the actual demand data.

6. The method for dynamic control of drip irrigation parameters combined with multi-point sensor monitoring as claimed in claim 1, characterized in that: Analyze the target soil permeability of the target area to obtain a target irrigation cycle, including: Performing irrigation retention simulation based on the target soil of the target area, evaluating the drainage effectiveness of the target soil to obtain the target soil permeability; The target irrigation cycle is obtained by performing salt erosion accumulation analysis according to the target soil permeability.

7. The method for dynamic control of drip irrigation parameters combined with multi-point sensor monitoring as claimed in claim 6, characterized in that: Evaluating the drainage effectiveness of the target soil to obtain target soil permeability includes: Based on the irrigation simulation results, the infiltration rate and drainage rate were analyzed to obtain the water discharge rate; Analyze the water storage capacity and effective water content according to the irrigation simulation results to obtain the accumulated water content; The target soil permeability is obtained by combining the water discharge rate and the accumulated water amount.

8. The method for dynamic control of drip irrigation parameters combined with multi-point sensor monitoring as claimed in claim 1, characterized in that: The irrigation process is performed according to the actual demand data and the target irrigation cycle to obtain irrigation data, including: Perform irrigation configuration based on the actual demand data under the target irrigation cycle, associate irrigation parameters with the target monitoring data, and obtain control parameters; An irrigation control strategy is generated according to the control parameters, and irrigation is performed based on the irrigation control strategy to obtain the irrigation data.

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