A water supply adjustable forest seedling cultivation spraying method and matching system

Abstract

The application discloses a forest seedling cultivation spraying method with adjustable water supply and a matching system. The application collects multi-source data such as multi-layer soil water content and microclimate, accurately calculates real-time water deficit, irrigation efficiency loss and target water requirement of each partition based on intelligent algorithm, and generates individualized irrigation decision. The system combines constant pressure variable frequency water supply and branch pipe independent pressure regulation to ensure global stability of pipe network pressure and precise controllability of partition pressure; through cooperation of partition electromagnetic valve and flowmeter, precise water distribution of 'quantity closing' is realized. The flow and angle of the end nozzle are adjustable, and intermittent spraying and real-time monitoring are cooperated to ensure the uniformity and efficiency of irrigation. After irrigation, the system continuously optimizes the decision model through effect evaluation and historical data learning. The application realizes the change from experience irrigation to data-driven intelligent irrigation, and significantly improves the water resource utilization efficiency, seedling growth quality and system operation reliability.

Description

Technical Field

[0001] This invention belongs to the field of agricultural irrigation, and in particular relates to a water supply adjustable spray method for cultivating forest seedlings and a supporting system. Background Technology

[0002] Traditional sprinkler systems mostly rely on fixed schedules or simple soil surface moisture thresholds for control, failing to reflect the actual moisture dynamics of seedling roots and the real-time impact of meteorological conditions on water demand. This lack of scientific decision-making can easily lead to under- or over-irrigation. Conventional pump start-stop control or simple pressure tank stabilization methods are ill-suited to handle the drastic pressure fluctuations in the pipeline network caused by multi-zone rotational irrigation. This results in uneven working pressure of sprinklers at different locations, creating dead zones or insufficient overlap in sprinkler coverage, severely impacting the uniformity of seedling growth. Seedlings of different varieties, ages, and terrain areas within a nursery have varying water requirements, but traditional systems lack the ability to independently control each zone, failing to implement "zone-specific irrigation," leading to wasted water and fertilizer resources and poor localized growth.

[0003] Current irrigation systems for forest seedling cultivation lack self-optimization and refined management capabilities. Most existing systems are open-loop controls, and there is a lack of effective evaluation and feedback on irrigation effects after implementation. Irrigation parameters have long relied on experience-based settings and cannot adaptively adjust to changes in the environment, soil, and crop growth. The systems have low levels of intelligence and are managed in a crude manner.

[0004] Therefore, there is a need for a water-adjustable spray method and supporting system for cultivating forest seedlings. Summary of the Invention

[0005] The main objective of this invention is to provide a water supply adjustable spray method and supporting system for cultivating forest seedlings, aiming to solve the technical problem of "how to achieve precise zoning, stable water pressure, and adaptive optimization of intelligent spray control in a complex and ever-changing forest seedling environment".

[0006] To achieve the above objectives, the present invention provides a water-adjustable spray method and supporting system for cultivating forest seedlings, comprising the following:

[0007] The system includes a central intelligent decision-making and control layer, a zoned execution and regulation layer, and an end-point sensing and spraying layer. The method includes the following steps:

[0008] S1. Environmental Data Acquisition and System Initialization:

[0009] Through the terminal sensing and spraying layer, multi-layer soil volumetric water content data, light intensity data, air temperature and humidity data, wind speed and wind direction data of each seedling zone are collected, as well as weather forecast information is obtained; the above data are transmitted to the central controller of the central intelligent decision and control layer via wireless network.

[0010] S2. Intelligent Irrigation Decision Generation:

[0011] The central controller calculates the current water deficit of each zone based on the multi-layer soil volumetric water content data; and integrates the light intensity, air temperature and humidity, wind speed, weather forecast information and the preset seedling growth stage model to calculate the irrigation efficiency loss, actual irrigation demand, and determine the irrigation timing and sprinkler pressure for each zone, generating an irrigation plan that includes the target irrigation amount, sprinkler pressure and irrigation time.

[0012] S3. Dynamic adjustment of water supply pressure:

[0013] When irrigation begins, the constant pressure variable frequency water supply unit in the central intelligent decision and control layer starts; the pressure sensor monitors the main pipeline pressure in real time, and the PID controller compares the pressure with the set pressure to dynamically adjust the pump speed to stabilize the water supply pressure;

[0014] S4. Precise water allocation and execution by zone:

[0015] The central controller sends the irrigation plan to the corresponding partition execution and regulation layer; the partition execution and regulation layer opens the partition solenoid valve, adjusts the water pressure to the spray pressure through the branch pipe pressure regulator, and accumulates the actual water consumption of the partition through the flow meter; when the accumulated water consumption reaches the target irrigation amount, the partition solenoid valve is closed to complete the irrigation of the partition.

[0016] S5. Terminal spraying execution and real-time adjustment:

[0017] After the water flow is distributed through the partition execution and adjustment layer, it reaches the end adjustable sprinkler array for spraying; during the spraying process, the actual pressure at the sprinkler end and / or the amount of water received on the ground are monitored and compared with the set value. If the deviation exceeds the threshold, an early warning is issued.

[0018] S6. System Feedback, Learning, and Optimization:

[0019] After the scheduled irrigation time is completed, soil volumetric water content data is collected again to calculate the actual increase in water content and compare it with the expected increase in water content during the irrigation decision-making stage to evaluate the irrigation effect. Based on historical irrigation environmental data, execution parameters, and effect data, the plant water requirement model parameters and irrigation efficiency model are optimized.

[0020] Furthermore, in step S1, the multi-layer soil volumetric water content data includes the surface, root, and deep soil volumetric water content collected by measurement points arranged in each zone using a five-point sampling method; the wireless network adopts LoRaWAN technology.

[0021] Furthermore, in step S2, the current water deficit is calculated. The formula is:

[0022] in, Let i be the current water deficit of partition i. Let i be the area of ​​partition i. The target soil moisture content for zone i. The measured soil moisture content for partition i is currently... Let i be the soil bulk density. Let be the effective root depth of partition i.

[0023] Furthermore, in step S2, the actual irrigation requirement... The calculation method is as follows: ,in, This represents the actual irrigation requirement for partition i; Indicates evaporation loss; Indicates drift loss; This indicates runoff loss; the irrigation timing is preferably selected in the early morning or evening, and irrigation is postponed or reduced when the predicted rainfall exceeds a threshold within a preset time period, and irrigation is suspended when the real-time wind speed exceeds a safety threshold.

[0024] Furthermore, in step S3, the control algorithm of the PID controller is as follows: ;

[0025] Where u(t) is the control output; e(t) is the pressure deviation. , Set the pressure for the system. The actual pressure detected by the pressure sensor at time t; These are the proportional, integral, and differential coefficients, respectively.

[0026] Furthermore, in step S4, the partition execution and regulation layer also includes a distributed control node, which is used to receive instructions from the central controller, control the opening and closing of the partition solenoid valve, collect the pulse signal of the flow meter to calculate the cumulative water consumption, and feed back the execution status to the central controller.

[0027] Furthermore, in step S5, the adjustable nozzle array includes atomizing nozzles and / or rotating scattering nozzles; the nozzles have replaceable nozzles to adjust the flow rate and are mounted on universal brackets with adjustable horizontal and vertical pitch angles, and the spray ranges of adjacent nozzles are set to have 10-15% overlap.

[0028] Furthermore, in step S5, an intermittent spraying method is adopted, that is, a "spray-pause" cycle operation is performed.

[0029] Furthermore, in step S6, the methods for optimizing the plant water requirement model parameters include: establishing a water requirement prediction model based on historical data using multiple linear regression. ;

[0030] in, To predict water demand, T, RH, I, v, and θ represent air temperature, relative humidity, light intensity, wind speed, and soil moisture content, respectively. These are the regression coefficients obtained through training with historical data. This is the random error term.

[0031] Accordingly, the present invention also proposes an adjustable water supply sprinkler system for cultivating forest seedlings to implement the method described in any one of claims 1-9, the system comprising:

[0032] The central intelligent decision-making and control layer includes an environmental monitoring unit, a central controller, a constant pressure variable frequency water supply unit, and a PID controller. The environmental monitoring unit is used to collect meteorological data, the central controller is used to execute intelligent irrigation decision-making algorithms and generate irrigation plans, the constant pressure variable frequency water supply unit is used to provide a stable pressure water source, and the PID controller is used to adjust the pump speed according to pressure feedback.

[0033] The zonal execution and regulation layer is communicatively connected to the central intelligent decision-making and control layer, and includes multiple zonal control units; each zonal control unit includes a zonal solenoid valve, a branch pipe pressure regulator, a flow meter, and a distributed control node, used to receive and execute the irrigation plan, and to perform precise water distribution and usage control for its respective zonal area;

[0034] The end-point sensing and spraying layer, connected to the zoned execution and regulation layer, includes a soil moisture sensor network, micro-meteorological monitoring points, adjustable sprinkler arrays, and end-point feedback sensors arranged in each zone. The soil moisture sensor network is used to collect multi-layer soil volumetric water content data, the micro-meteorological monitoring points are used to collect local meteorological data, the adjustable sprinkler arrays are used to execute spraying operations, and the end-point feedback sensors are used to monitor the spraying execution effect.

[0035] This invention avoids ineffective irrigation and hidden waste caused by uneven pressure, pipe leaks, and sprinkler head damage through precise, on-demand irrigation. Zonal precision control and uniform spraying ensure that each seedling grows under optimal moisture conditions, reducing uneven growth and increased disease caused by water stress or waterlogging. Its fully automatic operation significantly reduces the workload of manual inspection and start-up / stop operations. Closed-loop control and real-time monitoring functions can promptly detect and warn of faults such as sprinkler head blockages, pipe leaks, and equipment malfunctions, transforming "reactive maintenance" into "predictive maintenance," thus improving system availability and lifespan. Compared to existing technologies, this invention significantly saves water and energy, greatly improves seedling quality and uniformity, and enhances system automation and reliability. Attached Figure Description

[0036] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the description of the specific embodiments will be briefly introduced below.

[0037] Figure 1 This is a flowchart illustrating the principle of an adjustable water supply spray method and supporting system for cultivating forest seedlings according to the present invention.

[0038] Figure 2 This is a schematic diagram illustrating the dual pressure regulation principle of a water supply adjustable spray method for cultivating forest seedlings and its supporting system. Detailed Implementation

[0039] 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, not all, of the embodiments of the present invention. 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.

[0040] Reference Figure 1 , Figure 1 This is a schematic diagram of a water supply adjustable spray method for cultivating forest seedlings and its supporting system according to the present invention.

[0041] This invention provides an adjustable water supply sprinkler system for cultivating forest seedlings, consisting of three interconnected levels:

[0042] The first level is the central intelligent decision-making and control layer, which includes an environmental monitoring unit, an intelligent decision-making algorithm module, a constant pressure variable frequency water supply unit, and a central controller.

[0043] The central intelligent decision-making and control layer is equivalent to the brain and heart of the system, responsible for collecting global information, formulating irrigation strategies, and controlling the total water supply pressure. The central controller adopts an industrial-grade PLC (programmable logic controller) or an IoT gateway, which has powerful data processing capabilities and reliable industrial control performance.

[0044] The second level is the zoned execution and regulation layer, which includes zoned solenoid valve assemblies, branch pressure regulators, distributed control nodes, and flow monitoring devices.

[0045] The partition execution and adjustment layer is equivalent to the neural network and joints of the system. It is responsible for decomposing the central instructions and transmitting them to each execution unit. Each seedling partition is equipped with an independent execution unit to achieve precise regional control.

[0046] The third layer: the end-point sensing and spraying layer, including adjustable sprinkler arrays, soil moisture sensor networks, micro-meteorological monitoring points, and end-point feedback sensors.

[0047] The terminal sensing and spraying layer is equivalent to the system's fingers and senses, responsible for performing spraying actions and collecting on-site data at the very end.

[0048] The information flow between system levels is as follows: First, the terminal sensing and spraying layer collects real-time raw data such as soil moisture and meteorological conditions; this real-time raw data is uploaded to the central intelligent decision-making and control layer, where it is analyzed by intelligent algorithms to generate irrigation decisions; then, the irrigation decision instructions are issued to the zoned execution and regulation layer to control the corresponding valves and regulators; finally, the spraying action is executed at the terminal, and the execution effect is uploaded again through feedback sensors, forming a closed loop. In this way, the division of labor among each level is clear, and the coupling is moderate; central decision-making ensures global optimization, distributed execution improves system reliability, and closed-loop control achieves precise regulation and continuous optimization.

[0049] The specific implementation of the technical solution of this invention will be described in detail below.

[0050] S1. Environmental Data Acquisition and System Initialization

[0051] After the system is powered on, it first executes a self-test program to ensure that all sensors and control devices are in normal working order. This process lasts approximately 30 seconds, during which the system checks whether the communication link is clear, whether sensor readings are within reasonable ranges, and whether the actuators can respond to commands correctly.

[0052] Soil moisture data collection is the core of the entire data collection process. Therefore, within each seedling section, soil moisture sensors should be arranged according to the "five-point sampling method," that is, one measurement point should be installed at each of the four corners and the center of the section. Each measurement point is further equipped with three sensors at different depths: the surface sensor is buried 5 cm below the soil surface, mainly monitoring evaporation; the root layer sensor is buried at a depth of 10-15 cm, which is the distribution layer of the main root system of most forest seedlings and best reflects the actual moisture status of the plants; and the deep layer sensor is buried at a depth of 25-30 cm to monitor water infiltration and the moisture reserves in the deeper soil layers.

[0053] Each sensor measures the soil volumetric water content θ, a dimensionless percentage value representing the volume ratio of water in a unit volume of soil. For example, θ = 25% means that in 1 cubic meter of soil, the volume of water is 0.25 cubic meters. This parameter reflects the actual moisture status of the soil more accurately than the traditional gravimetric moisture content because it is not affected by changes in soil bulk density.

[0054] The sensor works based on the measurement of dielectric constant. The dielectric constant of dry soil is approximately 3-5, while that of water is as high as 80. Therefore, changes in soil moisture content significantly alter its dielectric properties. The sensor emits high-frequency electromagnetic waves and measures their propagation characteristics to calculate the moisture content. Modern sensors can achieve a measurement accuracy of ±2%, fully meeting the needs of irrigation control.

[0055] Meteorological data collection is equally crucial; therefore, micro-meteorological monitoring stations need to be installed in representative locations within the nursery, typically in open areas with high elevation and no obstructions. These stations continuously collect the following data:

[0056] Light intensity I: The unit is watts per square meter (W / m²), which represents the solar radiation power received per unit area. This parameter directly affects the photosynthesis and transpiration of plants.

[0057] Air temperature T: The unit is degrees Celsius (°C), which affects the rate of water evaporation and plant physiological activities.

[0058] Relative humidity (RH): expressed as a percentage, reflecting the degree of water vapor saturation in the air.

[0059] Wind speed v and wind direction: Wind speed, measured in meters per second (m / s), is the main factor affecting spray drift loss.

[0060] Atmospheric pressure Although it has a minor impact on irrigation, it helps improve the measurement accuracy of other parameters.

[0061] All sensor data is transmitted to the central controller via a wireless IoT protocol. A preferred solution is to use LoRaWAN (Long Range Wide Area Network) technology, which features long transmission distance, low power consumption, and strong anti-interference capabilities. In open areas, LoRaWAN can achieve a transmission distance of over 10 kilometers. Each sensor node is equipped with an independent power supply to ensure normal operation even on cloudy or rainy days. A preferred solution is to use a solar panel and a battery as the independent power source.

[0062] The final step in system initialization is parameter loading. The central controller reads the previously saved system parameters from non-volatile memory, including the area of ​​each zone, soil type, planted seedling varieties and planting dates, historical irrigation records, etc. Simultaneously, the controller obtains the latest weather forecast information via the network interface, particularly the probability and amount of precipitation for the next 24 hours.

[0063] S2. Intelligent Irrigation Decision Generation

[0064] Once all environmental data has been collected, the central controller begins running an intelligent decision-making algorithm. The core of this algorithm is a plant water requirement model that comprehensively considers multiple factors to calculate the actual water requirement for each zone.

[0065] The algorithm calculates the current water deficit for each partition. The calculation formula is as follows:

[0066]

[0067] In the formula, This represents the current water deficit in partition i, in cubic meters. Represents the area of ​​partition i, in square meters; This represents the target soil moisture content for zone i, which is the optimal value pre-set based on the planted seedling variety and growth stage. This represents the current measured soil moisture content of partition i; The soil bulk density of zone i is expressed in grams per cubic centimeter, obtained through soil testing. This represents the effective root depth of partition i, in meters, and is determined based on the seedling type and age.

[0068] A specific example is a pine seedling area with an area of ​​1000 square meters. The target moisture content is 22%, the current measured moisture content is 18%, the soil bulk density is 1.3 g / cm³, and the root depth is 0.2 meters. Then the water deficit is: D = 1000 × (0.22 − 0.18) × 1.3 × 0.2 = 10.4 cubic meters.

[0069] However, this is not the final amount of water needed for irrigation, because there will be various losses during the irrigation process. The algorithm then needs to calculate the irrigation efficiency loss L: .

[0070] In the formula, Indicates the efficiency loss of irrigation in different zones; i Evaporation loss is related to temperature, humidity, and wind speed; Drift loss is mainly determined by wind speed; Runoff loss is related to soil permeability and irrigation intensity.

[0071] After comprehensive calculation, the actual irrigation requirement for each zone for: .

[0072] In the formula, This represents the actual irrigation requirement for partition i.

[0073] Next, the algorithm needs to determine the irrigation timing. The system prioritizes the following time periods:

[0074] Early morning hours: 04:00-08:00, when the temperature is low, the wind speed is low, and the evaporation loss is minimal;

[0075] Evening hours: 17:00-20:00, avoiding the midday heat.

[0076] However, if there is a rainfall forecast, the algorithm will be dynamically adjusted. This invention sets a rule: if the probability of rainfall in the next 6 hours exceeds 70% and the forecast rainfall exceeds 5 mm, irrigation will be postponed; if the forecast rainfall is between 2 and 5 mm, the amount of irrigation will be appropriately reduced.

[0077] Wind speed limitation is another important consideration; this invention sets a safety threshold. When the real-time wind speed exceeds this value, the system will suspend irrigation regardless of other conditions until the wind speed drops below the threshold. This is because strong winds not only cause severe drift losses but also lead to uneven spraying and may even damage seedlings.

[0078] The growth stage of seedlings also plays a crucial role in decision-making. The system database stores growth models of common forest tree species, dividing the growth cycle into different stages: budding stage, rapid growth stage, lignification stage, and nursery preparation stage. Each stage has different water requirements and sensitivities. Seedlings in the budding stage need to have the soil kept moist but should avoid waterlogging, and a "small amount, frequent" irrigation strategy should be adopted. Seedlings in the lignification stage need appropriate water control to promote xylem development, and the soil moisture content can be maintained at a lower level for a period of time.

[0079] Based on all the above analysis, the central controller generates a detailed irrigation plan for each zone, including: irrigation start time, expected duration, target irrigation volume, recommended sprinkler pressure, and whether fertilizer should be added.

[0080] The advantage of the decision-making algorithm described in this invention lies in its comprehensiveness and adaptability. Traditional timed irrigation is like setting an alarm clock, which rings at the appointed time regardless of actual needs. In contrast, this invention is more like a caring gardener, capable of observing the weather, checking the soil, understanding the growth status of seedlings, and then making the most suitable arrangements. This intelligent decision-making not only saves water resources but also creates the best growth environment for seedlings.

[0081] S3. Dynamic adjustment of water supply pressure

[0082] When the irrigation command is executed, the system's constant pressure variable frequency water supply unit enters the working state, which is a key technical link to ensure uniform spraying.

[0083] Reference Figure 2 , Figure 2 This is a schematic diagram illustrating the dual pressure regulation principle of a water supply adjustable spray method for cultivating forest seedlings and its supporting system according to the present invention.

[0084] Water pumps are the core power source of a water supply system. A better option is to choose multistage centrifugal pumps as the system's pumps because they are highly efficient, have a large head, and operate smoothly. The rated flow rate and head of the pump are determined based on the maximum simultaneous water demand of the entire nursery. For example, for a 100-acre nursery, if the maximum simultaneous irrigation area is designed to be 20 acres, and each acre requires 10 cubic meters of water per irrigation, planned to be completed within 2 hours, then the required flow rate is: .

[0085] A frequency converter is a key device for controlling the speed of a water pump. It receives signals from a PID controller and converts ordinary 50Hz AC power into AC power with a variable frequency, thereby changing the speed of the water pump motor. The flow rate of a water pump is approximately proportional to its speed, the head is proportional to the square of the speed, and the power is proportional to the cube of the speed. This means that by adjusting the speed, water supply parameters can be precisely controlled, and power consumption decreases significantly when the speed is reduced.

[0086] The pressure sensor is typically installed on the main pipeline before the first branch point after the pump outlet to monitor the pipeline pressure in real time. The pressure signal is converted into a 4-20mA current signal and transmitted to the controller.

[0087] The PID controller is the brain of the entire pressure regulation process. "PID" is an abbreviation for Proportional-Integral-Derivative control, a high-level control algorithm widely used in industrial control. Its working principle can be understood as follows:

[0088] Proportional control (P): Adjusts based on the current pressure deviation. The greater the deviation, the greater the adjustment. This is similar to when driving and noticing the car veering off course, you immediately turn the steering wheel in the opposite direction; the greater the deviation, the more you turn.

[0089] Integral control (I): Adjustments are made based on accumulated historical deviations. If the pressure remains consistently low, it indicates insufficient proportional control and requires strengthening. This is analogous to noticing a car consistently veering to one side and needing to continuously apply a fixed force in the opposite direction.

[0090] Differential control (D): Adjustments are made based on predictions of future deviation trends. If the pressure is decreasing rapidly, adjustments should be increased in advance, even if the current deviation is not large. This is like seeing a curve ahead and starting to turn in advance.

[0091] The mathematical expression for the PID control algorithm is: .

[0092] In the formula, u(t) represents the control output, i.e., the frequency setpoint of the frequency converter; e(t) represents the pressure deviation. , Set the pressure for the system. The actual pressure detected by the pressure sensor at time t; These represent the proportional, integral, and derivative coefficients, respectively, and need to be determined based on the specific system debugging.

[0093] In practical discrete control systems, the algorithm is executed with a sampling period Δt: .

[0094] A better solution is to install a pressure tank, which is a sealed pressure vessel with an internal rubber bladder dividing the container into a water chamber and an air chamber. When the water pump's supply exceeds the water consumption, the excess water enters the tank and compresses the bladder, storing energy. When the water consumption suddenly increases, the bladder expands, expelling the stored water to meet the instantaneous demand. This is similar to a capacitor in a circuit, smoothing out pressure fluctuations.

[0095] Example of working process: Assume the system is set to a pressure of... Initially, all valves were closed, water usage was zero, and pressure rapidly rose to over 3.0 bar. The pressure sensor detected this. The deviation is e = −0.2 bar. The PID controller calculates that the pump speed needs to be reduced, and the frequency converter reduces the frequency from 50Hz to 45Hz. As the pump speed decreases, the water supply decreases, and the pressure gradually drops back to 3.0 bar.

[0096] Then, a valve in one of the zones opens, initiating irrigation. Water usage suddenly increases, and the pipe pressure begins to drop, say to 2.8 bar. The pressure sensor detects this change, the PID controller calculates that the pump speed needs to be increased, the frequency converter raises the frequency to 52Hz, the pump speed increases, the water supply increases, and the pressure rises back to 3.0 bar and remains stable.

[0097] This pressure regulation method ensures uniform spraying, allowing each nozzle to operate at its designed pressure regardless of the number of zones activated; it avoids the water hammer effect, as the direct start and stop of traditional water pumps can generate pressure shock waves in the pipeline, potentially damaging pipes and equipment; moreover, it significantly reduces energy consumption, as the water pump operates in its high-efficiency zone most of the time, avoiding the inrush current from frequent start and stop; it extends equipment lifespan, and stable operation reduces mechanical wear.

[0098] S4. Precise water allocation and execution by zone

[0099] The irrigation plan generated by the central controller needs to be implemented in each seedling zone, a task completed by the zone execution layer. Therefore, zone planning is a key issue. A preferred solution is to divide the entire nursery into several irrigation zones, each with an area controlled between 500-1000 square meters. The division principles of this invention include:

[0100] Variety uniformity: Plant seedlings of the same variety and age as much as possible within the same zone;

[0101] Topographical similarity: The terrain within the same zone should be as flat as possible, with a slope not exceeding 5%;

[0102] Water source proximity: The zoning should be as regular as possible to reduce pipe length;

[0103] Convenience of management: The boundaries of the zones are coordinated with facilities such as roads and ditches.

[0104] Each partition is equipped with a complete set of execution units, including:

[0105] The zonal solenoid valve assembly consists of solenoid valves, which are key components controlling water flow. Normally closed solenoid valves powered by 24V DC are selected, as this voltage is safe and reliable, meeting agricultural electrical safety standards. The response time of the solenoid valves is crucial; a good solenoid valve can fully open or close within 2-3 seconds. Each solenoid valve is equipped with a manual opening / closing device for manual operation in case of power outages or automatic system malfunctions.

[0106] A branch pipe pressure regulator is installed at the inlet of each branch pipe in each zone to ensure that the pressure entering that zone remains stable at a set value regardless of changes in the main pipe pressure. Its working principle is based on the principle of force balance: the regulator contains a spring, and the target pressure is set by adjusting a knob. When the inlet pressure changes, the internal diaphragm senses the pressure change and moves the valve core, changing the water flow area and thus keeping the outlet pressure constant.

[0107] For example, the pressure in the main pipeline may fluctuate between 2.8 and 3.2 bar, but after passing through the pressure regulator, the pressure entering the zone can be stabilized at 2.5 bar (if the zone requires low-pressure spraying) or 3.5 bar (if high-pressure spraying is required). This independence allows the system to use completely different spraying strategies for different zones.

[0108] The flow meter is used to accurately measure the water consumption of each zone. A turbine flow meter is used, which has the advantages of simple structure, high reliability, and moderate price. The core of the flow meter is a freely rotating impeller. The water flow drives the impeller to rotate, and the rotational speed is proportional to the flow velocity. The impeller rotational speed is converted into a pulse signal by a magnetic induction or photoelectric sensor, and the controller calculates the cumulative flow by counting the pulses.

[0109] The basic formula for a flow meter is: V = K × N.

[0110] In the formula, V represents the cumulative flow rate in liters; K represents the instrument coefficient, which is determined during the calibration of each flow meter, and the unit is liters per pulse; N represents the pulse count.

[0111] For example, an optional flow meter with K=0.1 liters / pulse, if 1500 pulses are detected, then the cumulative flow is 150 liters.

[0112] The distributed control node acts as the "cerebellum" of each partition, responsible for receiving instructions from the central controller and specifically controlling the opening and closing of the solenoid valves in its partition, while also monitoring the flow meter readings. The control node is implemented using a microcontroller and has the following functions:

[0113] Receive wireless commands (via LoRa or Zigbee);

[0114] Drive solenoid valve relay;

[0115] Collect flow pulse signals;

[0116] Soil moisture sensors monitor this area;

[0117] The execution status is fed back to the central controller.

[0118] S5. Terminal spraying execution and real-time adjustment

[0119] After passing through zoned control, the water finally reaches the sprinkler heads, where the transformation from "water distribution" to "irrigation" is completed. The sprinkler head design of this system fully considers the special needs of forest seedling cultivation.

[0120] Selecting the right nozzle is the crucial first step. This invention selects different types of nozzles based on different application scenarios:

[0121] The atomizing nozzle is suitable for early seedling cultivation or cutting beds because it produces fine water droplets of 50-150 micrometers that fall gently like natural mist. Advantages include no impact damage to seedlings; increased air humidity and reduced leaf transpiration; and high irrigation uniformity. Typical parameters are: operating pressure 1.5-2.5 bar, flow rate 2-5 L / min, and coverage radius 1-2 meters.

[0122] The rotating diffuser nozzle is suitable for seedlings in their mid-to-late growth stages. It features a rotating mechanism that slowly rotates the nozzle as water flows through it, at a speed of 1-5 revolutions per minute, creating a full-circle or fan-shaped spray area. Advantages include a large coverage radius, low installation density, good wind resistance, high flow rate, and high irrigation efficiency. Typical parameters are: operating pressure 2.5-4.0 bar, flow rate 10-30 L / min, and coverage radius 3-8 meters.

[0123] The drip arrow or micro-sprinkler is suitable for container seedlings or situations requiring localized, precise irrigation. Water is slowly dripped directly onto the roots of each seedling, with virtually no evaporation or drift loss, achieving a water utilization rate of over 95%.

[0124] The adjustable design is a feature of this invention. The nozzle has three adjustable characteristics: adjustable flow rate, adjustable angle, and adjustable spraying mode, as described in detail below.

[0125] The adjustable flow rate is primarily achieved by changing the nozzles. Each nozzle head is equipped with a set of nozzles of different diameters, such as 1.0mm, 1.5mm, 2.0mm, and 2.5mm. The flow rate is related to the nozzle area and the working pressure, and the calculation formula is as follows: .

[0126] In the formula, Q represents the flow rate, in m³ / s; The flow coefficient is generally taken as 0.6-0.9; A represents the nozzle area, in m²; g represents the acceleration due to gravity, 9.8 m / s²; H represents the pressure head, in m.

[0127] For example, a nozzle with a diameter of 2.0 mm has a theoretical flow rate of:

[0128]

[0129]

[0130]

[0131] The adjustable angle is achieved through a universal bracket. The nozzle is mounted on the bracket and can rotate horizontally from 0-360° and tilt vertically from 0-45°. The adjustment principle is that the spraying range of adjacent nozzles should overlap by 10-15%, which is called the "overlap principle". This ensures that the entire area is evenly covered and eliminates spray dead zones.

[0132] The spraying method is adjustable, allowing for full-circle spraying, fan-shaped spraying, or strip spraying by changing the internal components of the nozzle. Fan-shaped spraying is often used at the edges of seedbeds to avoid spraying onto roads and causing waste; strip spraying is used for planting patterns with larger row spacing.

[0133] To ensure spray quality, this invention proposes an end-point monitoring solution, which involves installing the following monitoring devices near each sprinkler head assembly:

[0134] The end pressure gauge is used to monitor the actual pressure reaching the nozzle. If this value is significantly lower than the set value, it may indicate a blockage or leak in the pipeline.

[0135] Rain gauges are used to collect the actual amount of water that falls to the ground and compare it with theoretical calculations to evaluate the uniformity of the sprinkler system.

[0136] Soil moisture sensors are used to monitor real-time changes in soil moisture after irrigation and to assess the effectiveness of irrigation.

[0137] Based on real-time adjustments using monitoring data, this invention establishes several early warning rules, as detailed below:

[0138] When the actual pressure of the nozzle Continuously below the set value When the system reaches 80% capacity, it issues a "potential congestion" warning.

[0139] When the difference in rain gauge readings at different locations within the same zone exceeds 20%, an "uneven spraying" warning will be issued.

[0140] Increase in soil moisture 2 hours after irrigation If the value is below 70% of the expected value, a warning of "poor irrigation effect" will be issued.

[0141] For the spraying process, this invention adopts an intermittent spraying method, that is, spraying for a few minutes and then stopping for a few minutes, repeating this process. The advantages of this method are that it allows time for water to infiltrate, reduces surface runoff, avoids soil surface compaction, and improves water use efficiency. For example, it can be set to spray for 5 minutes and stop for 3 minutes, repeating 4 times, for a total spraying time of 20 minutes, but the actual effect is better than continuous spraying for 20 minutes.

[0142] S6. System Feedback, Learning, and Optimization

[0143] After a complete irrigation task is completed, the system's work does not end, but enters another important phase: effect evaluation, data analysis, and system optimization. This is the key to upgrading ordinary automated systems into intelligent systems.

[0144] S601. Effectiveness Evaluation

[0145] After irrigation ends, the system does not immediately begin the next irrigation cycle. Instead, it waits for a period of time, typically 2-4 hours, to allow the water to fully infiltrate and distribute. Then, it reads the soil moisture data for each zone again to calculate the actual increase in water content. : .

[0146] In the formula, Soil moisture content before irrigation; Soil moisture content 2-4 hours after irrigation.

[0147] Compare this actual increase with the expected increase:

[0148] Deviation rate

[0149] If |δ| > 15%, the system determines that the irrigation effect is not ideal and the reasons need to be analyzed. The reasons set by this invention include:

[0150] Soil parameters such as bulk density and field water holding capacity are not set accurately;

[0151] The estimation bias of the irrigation efficiency loss coefficient L;

[0152] The nozzle is clogged or damaged, resulting in insufficient water spray volume.

[0153] Special weather conditions, such as abnormally high temperatures and strong winds, can also have an impact.

[0154] Parameter optimization is an ongoing process. The system records complete data for each irrigation, forming a historical database. This complete data includes:

[0155] Environmental conditions: soil moisture, air temperature, humidity, wind speed, and sunlight before and after irrigation;

[0156] Irrigation parameters: start time, duration, set pressure, actual pressure, target water volume, actual water volume;

[0157] Results data: increase in moisture content, uniformity assessment.

[0158] Based on this data, the system can automatically adjust some key parameters. For example, the plant water requirement coefficient. This is an important parameter, representing the ratio of a plant's water requirement to a reference evapotranspiration at a specific growth stage. The system can extrapolate actual evapotranspiration from historical data. value: .

[0159] Then compare it with the theoretical value; if the difference persists, update the database. value.

[0160] Another parameter that can be optimized is irrigation efficiency η: .

[0161] By analyzing the η value under different meteorological conditions, the system can establish a more accurate efficiency prediction model.

[0162] Machine learning application: As the amount of data accumulates, a simple machine learning algorithm is introduced, using multiple linear regression to build a water demand prediction model: .

[0163] In the formula, To predict water demand, T, RH, I, v, and θ represent air temperature, relative humidity, light intensity, wind speed, and soil moisture content, respectively. These are the regression coefficients obtained through training with historical data. This is the random error term.

[0164] Fault warning and predictive maintenance are another intelligent feature of this invention. The system establishes a health record for each critical device, recording its normal operating parameter range. For example:

[0165] Solenoid valve: Normal opening time is 2-3 seconds. If it takes 10 seconds to open, the message "Valve action is slow, may need cleaning or replacement" will be displayed.

[0166] Water pump: The normal current range is 10-15A. If it continuously exceeds 18A, it indicates "possible overload or mechanical failure".

[0167] Pressure sensor: Normal readings should be between 0-6 bar and change smoothly. If there are sharp fluctuations or the readings exceed the range, it indicates that "the sensor may be faulty".

[0168] Flow meter: Monitor the stability of its instrument coefficient K over a long period of time. If a drift of more than 5% is found, prompt "calibration is required".

[0169] These warning messages are communicated to management personnel in multiple ways: displayed on a large screen in the central control room; sent via SMS to designated mobile phones; pushed via mobile app; and for particularly serious faults, alerts are also issued via voice call.

[0170] Water usage reporting and decision support: The system regularly generates various reports to help managers understand irrigation conditions.

[0171] Daily report: Total daily water consumption, water consumption in each zone, deviation from the plan, and water-saving effect;

[0172] Weekly report: Weekly water usage trend, cumulative water savings, equipment uptime, and fault statistics;

[0173] Monthly report: monthly water usage analysis, comparison with the same period last year, cost analysis, and benefit assessment;

[0174] Growing season report: A complete record of the entire growing season, providing a basis for planning in the following year.

[0175] One optimization of this invention is to add a rotation irrigation sequence setting. Assuming the nursery has multiple zones requiring irrigation, activating them simultaneously would necessitate large-scale water pumps and piping. By using rotation irrigation, only 2-3 zones are activated at a time, significantly reducing system investment.

[0176] The proposed irrigation sequence setting in this invention:

[0177] Water shortage priority: Zones with severe water deficits should be irrigated first;

[0178] Hydraulic balance: Zones that are geographically close should not be activated simultaneously to avoid excessively low local pressure;

[0179] Easy to manage: Irrigate in sequence according to the arrangement of the seedbeds, which facilitates manual inspection.

[0180] The execution process is as follows: When it is the turn of a certain zone to be irrigated, the central controller sends a command to the control node of that zone, including the target water volume. and pressure setpoint The control node first adjusts the pressure regulator to the set pressure, then opens the solenoid valve. Water begins to flow, and the flow meter starts counting. The control node compares the accumulated flow in real time. With target traffic .when When the time comes, the control node immediately closes the solenoid valve, ending the irrigation process. The entire process is fully automatic and requires no manual intervention.

Claims

1. A control method for a water-adjustable sprinkler system for cultivating forest seedlings, characterized in that, The system includes a central intelligent decision-making and control layer, a zoned execution and regulation layer, and an end-point sensing and spraying layer. The method includes the following steps: S1, Environmental Data Acquisition and System Initialization: Through the terminal sensing and spraying layer, multi-layer soil volumetric water content data, light intensity data, air temperature and humidity data, wind speed and wind direction data of each seedling zone are collected, as well as weather forecast information is obtained; the above data are transmitted to the central controller of the central intelligent decision and control layer via wireless network. S2, Intelligent Irrigation Decision Generation: The central controller calculates the current water deficit of each zone based on the multi-layer soil volumetric water content data; and integrates the light intensity, air temperature and humidity, wind speed, weather forecast information and the preset seedling growth stage model to calculate the irrigation efficiency loss, actual irrigation demand, and determine the irrigation timing and sprinkler pressure for each zone, generating an irrigation plan that includes the target irrigation amount, sprinkler pressure and irrigation time. S3, Dynamic adjustment of water supply pressure: When irrigation begins, the constant pressure variable frequency water supply unit in the central intelligent decision and control layer starts; the pressure sensor monitors the main pipeline pressure in real time, and the PID controller compares the pressure with the set pressure to dynamically adjust the pump speed to stabilize the water supply pressure; S4, precise water allocation and execution by zone: The central controller sends the irrigation plan to the corresponding partition execution and regulation layer; the partition execution and regulation layer opens the partition solenoid valve, adjusts the water pressure to the spray pressure through the branch pipe pressure regulator, and accumulates the actual water consumption of the partition through the flow meter; when the accumulated water consumption reaches the target irrigation amount, the partition solenoid valve is closed to complete the irrigation of the partition. S5, End-point spray execution and real-time adjustment: After the water flow is distributed through the partition execution and adjustment layer, it reaches the end adjustable sprinkler array for spraying; during the spraying process, the actual pressure at the sprinkler end and / or the amount of water received on the ground are monitored and compared with the set value. If the deviation exceeds the threshold, an early warning is issued. S6, System Feedback, Learning and Optimization: After the scheduled irrigation time is completed, soil volumetric water content data is collected again to calculate the actual increase in water content and compare it with the expected increase in water content during the irrigation decision-making stage to evaluate the irrigation effect. Based on historical irrigation environmental data, execution parameters, and effect data, the plant water requirement model parameters and irrigation efficiency model are optimized.

2. The method according to claim 1, characterized in that, In step S1, the multi-layer soil volumetric water content data includes the volumetric water content of the surface, root, and deep soils collected by measurement points arranged in each zone using a five-point sampling method; the wireless network adopts LoRaWAN technology.

3. The method according to claim 2, characterized in that, In step S2, the current water deficit is calculated. The formula is: in, Let i be the current water deficit of partition i. Let i be the area of ​​partition i. The target soil moisture content for zone i. The measured soil moisture content for partition i is currently... Let i be the soil bulk density. Let be the effective root depth of partition i.

4. The method according to claim 3, characterized in that, In step S2, the actual irrigation requirement The calculation method is as follows: ,in, This represents the actual irrigation requirement for partition i; Indicates evaporation loss; Indicates drift loss; This indicates runoff loss; the irrigation timing is preferably selected in the early morning or evening, and irrigation is postponed or reduced when the predicted rainfall exceeds a threshold within a preset time period, and irrigation is suspended when the real-time wind speed exceeds a safety threshold.

5. The method according to claim 1, characterized in that, In step S3, the control algorithm of the PID controller is as follows: ; Where u(t) is the control output; e(t) is the pressure deviation. , Set the pressure for the system. The actual pressure detected by the pressure sensor at time t; These are the proportional, integral, and differential coefficients, respectively.

6. The method according to claim 1, characterized in that, In step S4, the partition execution and regulation layer further includes a distributed control node, which is used to receive instructions from the central controller, control the opening and closing of the partition solenoid valve, collect the pulse signal of the flow meter to calculate the cumulative water consumption, and feed back the execution status to the central controller.

7. The method according to claim 1, characterized in that, In step S5, the adjustable nozzle array includes atomizing nozzles and / or rotating scattering nozzles; the nozzles have replaceable nozzles to adjust the flow rate and are mounted on universal brackets with adjustable horizontal and vertical pitch angles, and the spray ranges of adjacent nozzles are set with 10-15% overlap.

8. The method according to claim 1, characterized in that, In step S5, an intermittent spraying method is used.

9. The method according to claim 1, characterized in that, In step S6, the methods for optimizing the plant water requirement model parameters include: establishing a water requirement prediction model based on historical data using multiple linear regression. ； in, To predict water demand, T, RH, I, v, and θ represent air temperature, relative humidity, light intensity, wind speed, and soil moisture content, respectively. These are the regression coefficients obtained through training with historical data. This is the random error term.

10. A water-adjustable sprinkler system for cultivating forest seedlings, characterized in that, The system for implementing the method according to any one of claims 1-9 comprises: The central intelligent decision-making and control layer includes an environmental monitoring unit, a central controller, a constant pressure variable frequency water supply unit, and a PID controller. The environmental monitoring unit is used to collect meteorological data, the central controller is used to execute intelligent irrigation decision-making algorithms and generate irrigation plans, the constant pressure variable frequency water supply unit is used to provide a stable pressure water source, and the PID controller is used to adjust the pump speed according to pressure feedback. The zonal execution and regulation layer is communicatively connected to the central intelligent decision-making and control layer, and includes multiple zonal control units; each zonal control unit includes a zonal solenoid valve, a branch pipe pressure regulator, a flow meter, and a distributed control node, used to receive and execute the irrigation plan, and to perform precise water distribution and usage control for its respective zonal area; The end-point sensing and spraying layer, connected to the zoned execution and regulation layer, includes a soil moisture sensor network, micro-meteorological monitoring points, adjustable sprinkler arrays, and end-point feedback sensors arranged in each zone. The soil moisture sensor network is used to collect multi-layer soil volumetric water content data, the micro-meteorological monitoring points are used to collect local meteorological data, the adjustable sprinkler arrays are used to execute spraying operations, and the end-point feedback sensors are used to monitor the spraying execution effect.

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