Drip irrigation system for towel gourd cultivation

Abstract

The invention relates to the technical field of drip irrigation control, and discloses a drip irrigation system for towel gourd cultivation, which comprises an atmosphere demand simulation unit, a soil humidity sensing unit, a drip irrigation execution unit and a main controller, the main controller takes a feedforward demand signal generated by the atmosphere demand simulation unit and a dynamically updated crop coefficient as a main basis for irrigation, and performs safety correction by using a feedback correction signal VWC generated by the soil humidity sensing unit; the main controller further utilizes the historical irrigation amount, the soil humidity net variation VWC and the time integral value of the feedforward demand signal to continuously update the crop coefficient through water balance calculation.

Description

Technical Field

[0001] This invention relates to a drip irrigation system for loofah cultivation, belonging to the field of drip irrigation control technology. Background Technology

[0002] In current facility agriculture production, drip irrigation systems are widely used. Their automated control logic generally relies on soil moisture sensors. This method monitors the volumetric water content of the soil matrix and initiates water replenishment when it falls below a preset irrigation threshold, thereby maintaining a relatively stable water-holding state in the root zone environment. This constitutes the main operating mode of existing automated irrigation. However, soil moisture is a lagging indicator, reflecting the historical cumulative result of water consumption rather than the immediate demand of plant transpiration. This characteristic is particularly evident when applied to crops such as loofah, which have large canopy leaf area and high transpiration rates. The actual water demand of the loofah canopy is determined by the rapidly changing atmospheric microclimate, including light, temperature, and humidity. When atmospheric transpiration demand increases rapidly in a short period of time, the root water absorption rate may not be able to meet the canopy demand. At this time, the plant has entered a momentary water stress, but due to the existence of the soil water-holding buffer zone, the soil moisture sensor reading has not yet reached the irrigation threshold. This causes the control system to be unable to respond to this high-frequency, short-term real stress, which, in the long run, leads to damage to the crop's photosynthetic efficiency.

[0003] To address this lag, the industry has attempted various approaches, but all face new constraints: 1. Constructing an evapotranspiration calculation model based on multidimensional meteorological parameters relies on high-cost meteorological station hardware and computing resources. Furthermore, the model calibration is complex and maintenance costs are high, making it difficult to widely apply in conventional agricultural production. 2. Employing high-frequency, timed pulsed irrigation processes can easily lead to prolonged root oversaturation and oxygen deficiency, and the timing of irrigation lacks a direct physical correlation with the actual needs of crops. Meanwhile, some research focuses on improving the physical structure of drip irrigation equipment, attempting to solve engineering problems such as dripper clogging. For example, the Chinese patent with authorization announcement number CN221128293U... A novel patent discloses a drip irrigation device for cultivating shredded gourd. The key technical aspect of this solution is to address the problem of soil easily entering the outlet and causing blockage after the drip pipe has been in contact with the ground for a long time. It mainly uses a mechanical anti-blocking component, including an arc plate, an outlet pipe, and an anti-blocking rod, to unclog and prevent blockage of the drip hole or outlet pipe through physical displacement. However, this type of solution only optimizes the physical structure of the irrigation execution end. Its innovation is focused on ensuring water can flow out, but it does not address the more core issue of when to flow out, which is the irrigation decision-making logic. When the system triggers irrigation, it will most likely still rely on the lag feedback control based on soil moisture. Therefore, it also cannot solve the problem of instantaneous water stress on crops caused by the lag in control logic.

[0004] Therefore, the technical problem to be solved by this invention is how to design a new control method that can avoid dependence on lagging indicators such as soil moisture and avoid using complex and expensive digital calculation models, obtain a leading indicator that can reflect the driving force of atmospheric transpiration in real time in a simple, cost-controllable and reliable way, and use this as the main basis to construct irrigation control logic to achieve synchronous response between irrigation and the actual water demand of crops. Summary of the Invention

[0005] This invention provides a drip irrigation system for loofah cultivation. Its main purpose is to solve the problem of obtaining a leading indicator that can reflect the driving force of atmospheric transpiration in real time in a simple, cost-controllable and reliable manner, and to use this as the main basis to construct irrigation control logic to achieve synchronous response between irrigation and the actual water demand of crops.

[0006] To achieve the above objectives, the present invention provides a drip irrigation system for loofah cultivation, the system comprising:

[0007] The system includes an atmospheric demand simulation unit, a soil moisture sensing unit, a drip irrigation execution unit, and a main controller.

[0008] The atmospheric demand simulation unit is configured to generate a feedforward demand signal characterizing the driving force of atmospheric transpiration. ;

[0009] The soil moisture sensing unit is configured to generate a feedback correction signal characterizing the soil moisture in the root zone. ;

[0010] The main controller is configured to perform the following steps: Step a, based on the feedforward demand signal With a currently stored crop coefficient Multiply to generate a basic irrigation instruction; step b, adjust based on feedback signals. The reading is used to reject or correct the basic irrigation instruction based on a preset safety threshold; step c, at the end of a preset calibration cycle, based on the total irrigation amount applied by the drip irrigation execution unit during the cycle. and the net change in soil moisture between the start and end points of the cycle as measured by the soil moisture sensing unit. Calculate an actual evapotranspiration rate Step d: Within the same calibration cycle, by analyzing the feedforward demand signal... Perform time integration to calculate a total potential evaporation. Step e, calculate the actual evapotranspiration. With total potential evaporation The ratio between them is used to calculate and update the crop coefficient used in step a. .

[0011] Preferably, the atmospheric demand simulation unit includes: a standardized evaporator configured to be exposed to the canopy microclimate of a loofah plant; a micro-water reservoir for supplying water to the standardized evaporator; and a measurement component configured to measure the rate of water loss in the micro-water reservoir to generate a feedforward demand signal. The measuring components are weighing sensors or liquid level sensors.

[0012] Preferably, when the main controller executes step b, the rejection or correction includes: when a correction signal is fed back. When the soil moisture level exceeds a preset over-wetness threshold, the execution intensity of basic irrigation instructions is rejected or reduced; when a correction signal is fed back... When the soil moisture level falls below a preset drought threshold, a supplementary irrigation instruction is triggered in addition to the basic irrigation instruction.

[0013] Preferably, before executing step a, the main controller is further configured to: correct the signal based on the feedback. Given the current value, a smoothing time constant is calculated and determined; a time-domain smoothing filter with the smoothing time constant as a parameter is used to smooth the feedforward demand signal. The process is performed to generate a smoothed feedforward demand signal; and in step a, the main controller replaces the feedforward demand signal with the smoothed feedforward demand signal. To generate basic irrigation instructions; wherein the main controller is configured to: increase the smoothing time constant when the soil moisture is higher than a preset smoothing adjustment threshold; and decrease the smoothing time constant when the soil moisture is lower than the preset smoothing adjustment threshold.

[0014] Preferably, the main controller is further configured to: monitor the feedback correction signal in step b. The frequency of events that trigger a preset drought threshold and result in supplementary irrigation commands. When the event frequency satisfy When conditions are met, determine the feedforward demand signal. Unreliable, among which A preset logic conflict frequency threshold is set; in response to the determination, the control logic of the drip irrigation system is downgraded to rely solely on feedback correction signals. The reactive closed-loop mode generates an alert indicating that the atmospheric demand simulation unit needs maintenance.

[0015] Preferably, when executing step c, the main controller is configured to: read the total irrigation volume within a preset calibration period. Read the net change in soil moisture at the beginning and end of the preset calibration cycle. Based on the principle of water balance, through Calculate the actual evapotranspiration .

[0016] Preferably, the drip irrigation execution unit includes multiple drip irrigation hardware units, which are physically divided into at least two hydraulic isolation zones; each hydraulic isolation zone includes at least one independently controllable valve; the main controller is further configured to: when the health status of the first hydraulic isolation zone is assessed as being in a risky state, trigger a hydraulic isolation control strategy for the first hydraulic isolation zone at the logical topology level of system control, the hydraulic isolation control strategy including modifying the irrigation scheduling of the second hydraulic isolation zone which is physically adjacent to the first hydraulic isolation zone, so that the irrigation execution time of the first hydraulic isolation zone and the second hydraulic isolation zone are forcibly separated on the time axis.

[0017] Preferably, the system further includes at least one root zone environmental sensor, configured within the hydraulic isolation zone, for collecting root zone environmental data within the hydraulic isolation zone. The root zone environmental data includes at least soil volumetric water content. Data and soil electrical conductivity Data; the steps for the main controller to assess health status include: based on Data and Data can be used to diagnose whether the first hydraulic isolation zone is in a specific risk pattern of high humidity and high salinity or loss of root water absorption capacity.

[0018] Preferably, the main controller also has a built-in hydrodynamic digital twin model; the main controller is further configured to: after an irrigation event occurs, monitor the dynamic response curve of the root zone environmental data, compare the dynamic response curve with the health response curve predicted by the hydrodynamic digital twin model, and when the deviation between the dynamic response curve and the health response curve exceeds a preset threshold, assess the health status of the hydraulic isolation zone as a risk state.

[0019] Preferably, the system also includes flow or pressure sensors for monitoring the main pipeline and branch pipelines in each hydraulic isolation zone; the main controller is further configured to perform an energy flow or mass flow conservation check on the physical integrity of the system based on the readings of the flow or pressure sensors before irrigation is performed, in order to diagnose physical blockages or leaks.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. The present invention provides a control logic for a drip irrigation system that uses the feedforward demand signal generated by the real-time acquired atmospheric evaporation loss rate as the main control basis for generating basic irrigation instructions. The traditional soil moisture signal, which reflects historical conditions, is transformed into a feedback correction signal used to correct or reject the basic instruction. This resetting of the control hierarchy allows irrigation decisions to no longer be limited by the lag in soil moisture, but to directly respond to the immediate atmospheric driving force causing water consumption, thereby preventing crops from entering a state of instantaneous water stress due to irrigation response delays. Before generating the basic irrigation instruction, the main controller is configured to first dynamically determine a smoothing time constant based on the feedback correction signal, i.e., the current value of soil moisture. The controller uses a time-domain smoothing filter with this time constant as a parameter to process the feedforward demand signal, generating a smoothed feedforward demand signal, and using this to calculate the basic irrigation instruction. When soil moisture is high, the controller increases the time constant to enhance smoothness, while when soil moisture is low, it decreases the time constant to improve response sensitivity. This mechanism maintains the system's responsiveness to changes in actual demand while avoiding invalid jitter and mechanical wear of the irrigation execution unit caused by high-frequency noise in the original feedforward signal.

[0022] 2. After a calibration cycle is completed, the total irrigation volume of the drip irrigation execution unit and the net change in soil moisture measured by the soil moisture sensor unit during that cycle are retrieved. Based on these two data, an actual evapotranspiration is calculated. At the same time, the controller performs time integration on the feedforward demand signal within the same cycle to obtain the total potential evapotranspiration. By calculating the ratio between the aforementioned actual evapotranspiration and the total potential evapotranspiration, the system can automatically update and apply a crop coefficient that matches the current canopy state of the crop, so that the calculation basis of the feedforward irrigation logic changes dynamically with crop growth.

[0023] 3. The main controller of the present invention is also configured to continuously monitor the frequency of events that trigger drought thresholds and initiate supplementary irrigation by feedback correction signals; when the frequency of such events exceeds a logic conflict threshold within a preset time window, the controller determines that the feedforward demand signal is unreliable; in response to this determination, the control logic automatically degrades to a reactive closed-loop mode that relies solely on feedback correction signals, and generates an alarm indicating that the atmospheric demand simulation unit needs maintenance, thereby preventing systemic irrigation shortages caused by physical contamination or failure of the feedforward signal source. Attached Figure Description

[0024] Figure 1 This is a diagram of the adaptive control logic architecture of the drip irrigation system of the present invention;

[0025] Figure 2 For the present invention Data relationship diagram of the dynamic self-calibration process;

[0026] Figure 3This is a timing diagram of the irrigation decision-making process that integrates feedforward and feedback signals according to the present invention. Detailed Implementation

[0027] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention is further described below with reference to specific illustrations. However, the following embodiments are only preferred embodiments of this invention and are not intended to limit the scope of protection of this invention.

[0028] The drip irrigation system for loofah cultivation provided by this invention, in a typical system-level configuration, mainly consists of four core functional units: an atmospheric demand simulation unit, a soil moisture sensing unit, a drip irrigation execution unit, and a main controller; wherein the atmospheric demand simulation unit is used to generate a feedforward demand signal characterizing the driving force of atmospheric transpiration in real time. The soil moisture sensing unit is used to monitor the soil water holding capacity in the root zone and generate a feedback correction signal. The drip irrigation execution unit includes the solenoid valves, water pumps, drippers, and piping network necessary for irrigation; the main controller, which can be a local programmable logic controller (PLC), an embedded microcontroller, or a remote cloud server, serves as the system's control center. Its core operating mechanism lies in... Signals serve as the primary driving force for irrigation decisions, The signal serves as a boundary condition for safety correction, and the main controller is also configured to utilize... and Historical data, through water balance calculations, were used to determine a key crop coefficient. Periodic dynamic self-calibration was performed, thereby constructing an irrigation control system architecture with feedforward drive, feedback correction, and dynamic adaptive capabilities. In a specific engineering implementation, the atmospheric demand simulation unit was constructed to avoid the shortcomings of traditional weather station models, which are complex, expensive, and have lagging soil moisture indicators. Instead, a low-cost physical simulation method was adopted to obtain leading indicators. To this end, the atmospheric demand simulation unit was configured to include: a standardized evaporator, such as a porous ceramic evaporating dish with a diameter of 10 cm and a standard porosity, or a standardized fiber wick. The key to this evaporator is its deployment location; it must be placed on the loofah to ensure that it is exposed to the same microclimate environment as the loofah leaves. The system is subject to the same light, temperature, humidity, and wind speed disturbances; a miniature water reservoir, such as a sealed 500mL water bottle, independently and continuously supplies water to the evaporator through capillary action or negative pressure balance; and a measuring component for real-time measurement of the rate of water loss due to evaporation in the miniature water reservoir; in one embodiment, the measuring component is a high-precision weighing sensor, such as a weighing sensor with a range of 1kg and an accuracy of 0.1g, for real-time monitoring of the total weight of the water reservoir and its internal water; in another embodiment, the component may be a non-contact liquid level sensor; the main controller performs high-frequency differential calculations on the readings of the measuring component, for example, calculating, if it is weighing,... This yields a real-time evaporation loss rate in grams per minute or millimeters per hour; this rate signal is defined as the feedforward demand signal. Its physical significance is strongly correlated with the potential transpiration rate of the loofah canopy in principle.

[0029] The core operating mechanism of the main controller of this system aims to solve the problem of instantaneous water stress caused by drastic changes in atmospheric demand in loofah cultivation. Its control logic has been reconstructed into an architecture with feedforward drive as the main component and feedback correction as the auxiliary component. First, the main controller executes step a, namely the feedforward drive logic: the controller, for example, acquires data in real time at a 5-minute interval. The signal is read, and a crop coefficient currently stored within it is retrieved. ,Should The source of the value will be detailed in subsequent steps; the controller executes... In some configurations, the multiplication operation can also be performed by multiplying by a calibrated irrigation area coefficient. Actively generate a basic irrigation command. The instruction This represents the estimated real-time water requirement of the loofah canopy under current atmospheric conditions; next, the main controller executes step b, the feedback correction logic. At this point, the soil moisture sensing unit deployed in the loofah root zone, such as a soil moisture sensing unit using an FDR or TDR type sensor, generates a feedback correction signal. It is used as a safety valve; the correction logic is executed based on a preset safety threshold. Specifically, as claimed in claim 3, the rejection or correction includes a two-dimensional judgment: first, when... The signal indicates that the soil moisture content is higher than a preset over-wetness threshold. This threshold needs to be calibrated according to the soil type, such as the volumetric water content of sandy loam. If the value is greater than 85%, it indicates that the root zone is close to saturation. The controller then determines that there is a risk of root rot and will reject or reduce the intensity of the instruction, for example, by halving the intensity of the basic irrigation instruction. The operation; secondly, when When the signal indicates that soil moisture is below a preset drought threshold, this threshold also needs to be calibrated, for example... If the water level is less than 40%, it indicates that the crop stress threshold has been reached, and the controller will determine that there has been excessive historical water shortage. At this point, it will... In addition to the directives, a supplementary irrigation command can be forcibly triggered, such as a fixed-duration water pulse, to ensure the most basic water security in the root zone; this reset of the control architecture allows for more flexible irrigation decisions. It can synchronize with atmospheric driving forces in real time, avoiding hysteresis stress, and at the same time utilize feedback signals. Perform boundary correction.

[0030] Furthermore, to solve the above feedforward logic... The problem of difficulty in accurately setting values, namely the loofah canopy The value changes drastically with growth dynamics, and manual static settings can lead to irrigation errors. Therefore, the main controller of this invention is further configured to execute a dynamic set of... The self-calibration logic, namely steps c, d, and e, is based on the water balance principle and uses the existing signal source in the system to solve the problem in reverse. In a preset calibration cycle, for example, set to 24 hours, typically executed at 3:00 AM daily, at the end of the cycle, the main controller performs the following procedure: Step c, the controller retrieves historical data for the cycle, first, accumulating the total irrigation amount applied by the drip irrigation execution unit. This is the known output of the controller; secondly, read the start of this cycle. and the end Two moments Read the data and calculate the net change in soil moisture. ,in, This is a pre-defined effective root zone depth value, such as 0.3 meters determined by root sampling or ground-penetrating radar; subsequently, the controller... Calculate an actual evaporation rate This value represents the actual total water consumption of the loofah during that cycle; in step d, the controller retrieves all data from the same calibration cycle. Historical sample values ​​are used to calculate a total potential evaporation rate by integrating them over time and summing the results. Step e: The controller performs a division operation, that is... Calculate and update the crop coefficients used in step a. This newly calculated Values, for example, automatically updated from 0.81 to 0.83, will automatically become the values ​​used for calculations in the next 24-hour period. This provides new evidence; the introduction of this mechanism allows irrigation strategies to be synchronized with the actual canopy growth dynamics of the loofah.

[0031] In the actual engineering environment of loofah cultivation The signal source, due to its high sensitivity, is easily interfered with by factors such as brief strong winds or cloud drift, generating high-frequency noise. If the controller directly uses this noise signal, it will cause high-frequency, ineffective control jitter in the drip irrigation execution unit, including valves and water pumps, shortening the mechanical life of the hardware. To resolve this specific engineering problem, as described in claim 4, the main controller is further configured to execute an asynchronous time-domain filter before executing step a. This procedure utilizes slow variables. De-dynamic modulation of fast variables Smoothness: The controller first reads the current feedback correction signal. The value is used to calculate and determine a smoothing time constant. The calibration procedure for this calculation is as follows: set a smoothing adjustment threshold, for example... When soil moisture When the value is above this threshold, it indicates that the root region buffer is sufficient, and anti-jitter is prioritized. The controller increases the smoothing time constant, for example... =30 minutes; when soil moisture When the value falls below this threshold, it indicates that the buffer is exhausted, response speed takes priority, and the controller reduces the smoothing time constant, for example... =1 minute; the controller uses a dynamic... Standard time-domain smoothing filters with parameters such as the exponential moving average (EMA) algorithm, are used to smooth the original data. The signal is processed to generate a smooth feedforward demand signal. Furthermore, in step a, the main controller uses this smoothed feedforward demand signal. Replace the original feedforward demand signal To generate basic irrigation instructions, i.e. This mechanism ensures system responsiveness while effectively filtering out high-frequency noise, thus guaranteeing the engineering stability and service life of the execution unit.

[0032] In addition, this system integrates a sensor failure diagnosis mechanism to address potential failure modes that may occur in the atmospheric demand simulation unit in real field conditions, such as chronic contamination of evaporators by dust or algae. Such failures can lead to… The signal is severely and persistently low, leading to chronic under-feeding of the system. This mechanism is achieved by monitoring the persistent conflict between the feedforward and feedback logic: in step d, the main controller is configured to continuously monitor the feedback correction signal in step b, i.e., the auxiliary logic. Triggering a preset drought threshold, such as This leads to a higher frequency of supplemental irrigation commands. Step e, the controller has a built-in... That is, the logical conflict frequency threshold, for example, calibrated as =10 times / 48 hours, and determine Does it meet the requirements? The condition; when this condition is met, the controller determines: frequent upward corrections by the feedback logic necessarily mean that the feedforward logic is based on... The signal is systematically low, and this lowness is in In the case of self-calibration, the failure can be attributed to a physical malfunction of the atmospheric demand simulation unit; therefore, the controller determines the feedforward demand signal. If unreliable; in step f, in response to the determination, the controller immediately performs a safety degradation: automatically degrading the control logic of the drip irrigation system to rely solely on feedback correction signals. The traditional reactive closed-loop model, that is, When the temperature falls below the drought threshold, irrigation is triggered only, and an alarm is generated simultaneously indicating that the atmospheric demand simulation unit needs maintenance. This mechanism ensures that the system can automatically avoid the risk of systemic irrigation insufficiency and switch to a safe degraded mode when core sensors fail.

[0033] In large-scale facility agriculture for loofah cultivation, this system can also be configured with extended system-level disease prevention and self-inspection functions. The drip irrigation execution unit may include multiple drip irrigation hardware units, which are physically divided into at least two hydraulically isolated zones (HQZs), for example, one HQZ for every 20 meters of irrigation branch pipe. Each hydraulically isolated zone includes at least one independently controllable valve. This configuration aims to proactively manage the risk of cross-zone transmission of soil-borne pathogens, such as blight zoospores, caused by irrigation water flow (interflow). The system can also be configured with at least one rhizosphere environmental sensor within the hydraulically isolated zone. This sensor, in addition to... In addition to data, it can also integrate the collection of soil electrical conductivity. Data functionality; the main controller is configured to be based on Data and Data is used to assess the health status of the HQZ; for example, when the first hydraulic isolation zone (HQZ-A) is diagnosed as being in a state of high humidity and high salinity, for example... Reading greater than 80% and A reading greater than 3.0 mS / cm, or loss of root water absorption capacity, for example, after standard irrigation. When a specific risk pattern with a rate of decline below the health benchmark is assessed as a risk state, the controller triggers a hydraulic isolation control strategy, which is executed at the logical topology level. This strategy includes modifying the irrigation schedule of the second hydraulic isolation zone (HQZ-B) that is physically adjacent to HQZ-A, so that the irrigation execution times of HQZ-A and HQZ-B are forcibly separated on the time axis, for example, by an interval of 2 hours, thereby creating a dry barrier or time barrier between HQZs and actively cutting off the hydraulic transmission route of pathogens.

[0034] To further improve the accuracy of the HQZ risk assessment, the main controller can also incorporate a hydrodynamic digital twin model. This model is a non-physical entity, pre-calibrated with a health response curve based on the soil type of each HQZ, such as water holding capacity curves and dripper flow rates. For example, after irrigation stops... The time required for the peak to drop to 80% =45 minutes; the main controller is configured to monitor root zone environmental data after an irrigation event occurs. The dynamic response curve; the dynamic response curve, such as the measured descent time The dynamic response curve is compared with the health response curve predicted by the hydrodynamic digital twin model; when the deviation between the dynamic response curve and the health response curve exceeds a preset threshold, for example... The controller then determines that the HQZ has experienced hydrodynamic hysteresis, possibly caused by localized siltation or decreased root vitality, and assesses its health status as sub-healthy or at risk. This effectively identifies early anomalies in areas not covered by sensors. Finally, to ensure that all the above-mentioned HQZ-based control logic, such as isolation and digital twin models, relies on an intact physical network, the system may also include flow or pressure sensors for monitoring the main pipeline and branch pipelines in each hydraulic isolation zone. The main controller is further configured to run a physical integrity self-check before irrigation. This self-check can be performed by executing a pressure pulse test, based on the readings of the flow or pressure sensors, to verify the physical integrity of the system by energy flow or mass flow conservation, for example, verifying the flow rate input to the main pipeline. Is it equal to the sum of the flow rates of all HQZ branches with open valves? When the verification result When the deviation from zero is significant, it indicates that a physical blockage or leak has occurred, and an alert is given priority to prevent the epidemic prevention logic or digital twin model from operating based on incorrect physical assumptions.

[0035] Example 1: In a greenhouse environment for high-yield loofah cultivation, the loofah is in the peak period of canopy growth and fruit enlargement, a stage that is highly sensitive to water stress. A typical operating condition in this scenario is that on a sunny summer afternoon, the microclimate inside the greenhouse fluctuates drastically due to intermittent cloud cover. At 2:00 PM, cloud cover leads to reduced sunlight, and the atmospheric demand simulation unit deployed near the loofah canopy accordingly measures a lower feedforward demand signal. Its value is 0.2 g / min; at the same time, the feedback correction signal measured by the soil moisture sensing unit At a level of 65%, this value is higher than a preset drought threshold of 40% and lower than a preset over-wet threshold of 85%; at this point, the main controller... Its internal storage, for example, a crop coefficient of 0.85. The calculated basic irrigation instructions The intensity was low, and the drip irrigation execution unit was not triggered. At 14:05 in the afternoon, the clouds dispersed, and strong sunlight instantly entered the greenhouse, causing the transpiration demand of the loofah canopy to rise rapidly within minutes. Because the atmospheric demand simulation unit was exposed to the same microclimate, its evaporation rate also increased, affecting the feedforward demand signal. The initial reading rose to 0.9 g / min, accompanied by high-frequency fluctuations; at this point, the asynchronous time-domain filter mechanism configured in the system was activated, and the main controller first checked the current... The value is 64%, which is still higher than a preset 50% smoothing adjustment threshold. The controller determines that the root buffer is sufficient and should prioritize suppressing control jitter, therefore a longer smoothing time constant is selected. For example, 20 minutes, for The original signal is processed to generate a smoothly rising, smoothed feedforward demand signal; the main controller then uses this smoothed signal to calculate... Because the strength of this instruction has been significantly increased, and the current 63% The signal did not trigger any safety thresholds, so the feedback correction signal was not rejected; the controller then immediately activated the drip irrigation unit, initiating an irrigation response that matched the current atmospheric demand, actively replenishing water to the root zone. This response time was earlier than the dependency... Delayed irrigation is triggered only when the signal drops to the 40% drought threshold.

[0036] In this scenario, feedforward logic and self-calibration logic work together; the system executes at this moment. The calculations are based on... The parameters are not based on manual static estimation, but rather derived from the system's automatic calibration in the previous calibration cycle, for example, at 3:00 AM that day. Self-calibration procedure; in this procedure, the main controller retrieves historical data from the past 24 hours, including total irrigation volume. It is 5.1mm, and is composed of The net change in soil moisture calculated from the reading changing from 61% to 59%. The value is -0.6 mm, calculated based on a root depth calibration of 0.3 meters; accordingly, the main controller performs water balance calculations. The actual evapotranspiration during this period is obtained. It is 5.7mm; at the same time, the controller performs all operations within the same cycle. The total potential evaporation is obtained by integrating the signal over time. It is 6.7mm; ultimately, the controller executes... The calculation, namely 5.7mm divided by 6.7mm, was used to calculate and update the value. The value is 0.85; Self-calibration mechanism, Signals and These two signals, measured at different time scales—the former being the immediate guiding signal and the latter the measured value of the lagging buffer state—are coupled through water balance calculations, making the coefficients upon which the system's feedforward drive logic depends... It can automatically track the actual growth status of the loofah canopy.

[0037] Example 2: To objectively verify the response capability of the drip irrigation system of the present invention to rapid changes in atmospheric transpiration demand, and its technical effect compared with traditional soil moisture control methods, a set of comparative experiments were conducted. The purpose of the experiments was to quantitatively evaluate the ability of the system of the present invention to maintain stable root zone water and avoid instantaneous water stress under a simulated scenario of sudden changes in high transpiration demand of loofah. The experiments were conducted in a greenhouse compartment with environmental control capabilities. The greenhouse was equipped with an adjustable light intensity artificial light source system, whose maximum photosynthetically active radiation (PAR) could reach [value missing]. The control group was equipped with a soil moisture sensor unit and drip irrigation execution unit of the same specifications and placement as the experimental group. The control group had an adjustment accuracy of 2 meters long, 0.5 meters wide, and 0.4 meters deep, filled with homogeneous sandy loam substrate. Its field water holding capacity was measured to be approximately 35 ± 2% VWC. The sensor probe was buried 15 cm away from the dripper and at a depth of 20 cm. The control group consisted of a main controller, a solenoid valve, and a dripper. The control group used a traditional drip irrigation control method, equipped only with a soil moisture sensor unit and drip irrigation execution unit of the same specifications and placement as the experimental group. Its irrigation logic was set to when Irrigation is triggered when the water level falls below a preset initiation threshold and stops when the threshold is reached. During the experiment, the irrigation water source and dripper flow rate for both groups were set to 2 L / h, and all hardware conditions were kept consistent. The data acquisition system was used to record data from both systems. Signal (experimental group only) The sampling frequency for signals, irrigation events (start and stop times), and environmental parameters (light intensity, air temperature and humidity) is set to 1 minute. This frequency is chosen to capture minute-level environmental changes.

[0038] Before the experiment began, the soil volumetric moisture content of the two groups of cultivation troughs was pre-irrigated to reduce the water content. The crop coefficient was uniformly adjusted to approximately 60%; the crop coefficient stored internally in the main controller of the experimental group was also adjusted accordingly. Set to 0.85, this value is based on the previous day's... The self-calibration procedure was used to obtain the safety thresholds for the feedback correction signals in the experimental group, which were set based on substrate characteristics as a drought threshold of 40% VWC and an over-wet threshold of 85% VWC. The irrigation logic thresholds for the control group were set as follows: when... Irrigation should be initiated when the water level is below 45%. Irrigation was stopped when the light intensity rose to 75%; the experiment simulated a scenario of a sudden change in afternoon light conditions: in the initial phase (0-30 minutes), the light source PAR was maintained at... The relative humidity of the air is 70%; during the abrupt change phase (30-35 minutes), the PAR is linearly and rapidly increased to [value missing]. Meanwhile, reduce the relative humidity to 60%; maintain the high-demand phase (35-90 minutes), keeping PAR at [value missing]. The relative humidity was 60%; to simulate noise interference of sensor signals in an engineering environment, the experimental group collected raw data. The signal was artificially superimposed with Gaussian white noise with a signal-to-noise ratio of approximately 25 dB.

[0039] During the test run, system status data was continuously recorded, and typical data for key nodes are shown in Table 1. Referring to Table 1, in the initial low-demand phase, for example, T=20min, the test group... The signal fluctuates around 0.2 g / min against a noisy background. After passing through an asynchronous time-domain filter, at this point... Greater than 50%, using a longer period =20min, after smoothing, the generated basic irrigation command Low intensity, irrigation not triggered, two groups All decrease slowly; when there is a sudden change in light and humidity, for example, T=40min, the original The signal rises rapidly and fluctuates, reaching an average level of approximately 0.9 g / min. At this point, the asynchronous time-domain filter... Still above 50%, the output smooth feedforward demand signal steadily increases, and the main controller calculates... Increase, and at this time the experimental group The value was 58%, higher than the drought threshold of 40% and lower than the excessive moisture threshold of 85%. The feedback correction signal was not rejected, and the main controller, at T=42min, based on... Irrigation was triggered; in contrast, the main controller in the control group did not detect it during this period. The signal was at approximately 57%, triggering its 45% activation threshold, thus maintaining a non-irrigation state; until T=65min, the control group... It only dropped to 44.8%, triggering its first irrigation; throughout the high-demand maintenance phase, the experimental group, due to its early response, The percentage consistently fluctuated between 50% and 70%, while the control group experienced... The fluctuation range of the water potential in the experimental group was greater than that in the control group, which decreased from about 57% to below 45% and then rebounded. At the same time, the water potential of the leaves of the two groups of loofah plants was measured at T=60min, which is 25 minutes after the occurrence of high demand. The results showed that, using the pressure chamber method, the water potential of the leaves of the experimental group was -0.85MPa, while that of the control group was -1.25MPa. The latter showed a more obvious state of water stress.

[0040] Table 1: A comparison table of system response data.

[0041]

[0042] Experimental data show that the drip irrigation system of this invention, based on feedforward demand signals, is effective. The main driving logic, combined with asynchronous time-domain filtering and The self-calibration mechanism enables a rapid, stable, and quantitative response to irrigation under simulated high transpiration demand abrupt changes in loofah cultivation, with a response time that is faster than relying solely on feedback correction signals. This proactive response helps maintain soil moisture in the root zone within a more stable and suitable range, and reduces the degree of instantaneous water stress experienced by plants under high transpiration loads.

[0043] Example 3: This example combines Figures 1 to 3 A description of a drip irrigation system for loofah cultivation, such as... Figure 1As shown, the atmospheric demand simulation unit generates a feedforward demand signal, which is sent to an asynchronous time-domain filter. The soil moisture sensing unit generates a feedback correction signal, which is used to dynamically adjust the smoothing constant of the asynchronous time-domain filter and is also sent to the main controller. The asynchronous time-domain filter smooths the feedforward demand signal to reduce system jitter and outputs a smoothed feedforward demand signal to the main controller. Under the logic of integrating feedforward drive and feedback correction, the main controller combines the feedback correction signal to generate a basic irrigation command (corrected) and sends it to the drip irrigation execution unit. After the drip irrigation execution unit executes the irrigation command, the total irrigation data it generates is sent to the crop coefficient self-calibration unit. At the same time, the net change in soil moisture monitored by the soil moisture sensing unit and the integral value of the feedforward demand signal generated by the atmospheric demand simulation unit are also sent to the crop coefficient self-calibration unit. The crop coefficient self-calibration unit dynamically updates the crop coefficient based on water balance and updates the crop coefficient to the main controller, thus forming a closed-loop adaptive adjustment path.

[0044] like Figure 2 As shown, the horizontal axis represents time (hours), and the left vertical axis represents... / (mm), the right vertical axis is The figure shows the actual evapotranspiration through three curves. (mm) (solid line), Total potential evaporation (mm) (dashed line) and crop coefficient (Dotted lines), this diagram visually shows How are values ​​(points and lines) determined? (Solid line) and The calculation relationship (dashed line) is dynamically and adaptively updated within this 24-hour period. For example... Figure 3 As shown, the process is executed in cycles every 5 minutes by the main controller. It begins with the atmospheric demand simulation unit sending a feedforward demand signal of evaporation rate. After receiving this signal, the main controller reads the stored crop coefficient. The system calculates the basic irrigation command as feedforward signal × crop coefficient. Then, the main controller receives the feedback correction signal (soil moisture VWC) from the soil moisture sensing unit and performs a three-branch safety correction judgment: if the soil moisture is greater than the over-wet threshold of 85%, the irrigation command intensity is rejected or reduced, causing the drip irrigation execution unit to not execute or to execute at half the intensity; if the soil moisture is less than the drought threshold of 40%, a supplementary irrigation command is triggered and the basic command + supplementary irrigation is executed; if the soil moisture is within the safe range, the basic irrigation command is executed directly. In the latter two cases, the drip irrigation execution unit opens the solenoid valve and water pump and returns the execution status to the main controller after the operation.

[0045] Example 4: To further illustrate the technical effect of the present invention compared to the method relying solely on soil moisture feedback control, the following comparative experiment was conducted; this comparative example used the exact same experimental platform, environmental conditions, loofah plants, and data acquisition system as Example 2, the difference being the drip irrigation control logic; in this comparative example, the atmospheric demand simulation unit and its corresponding feedforward demand signal were not used. Main driving logic and The system employs a self-calibration mechanism, using a control method based solely on soil moisture feedback for irrigation; specifically, its main controller relies solely on the feedback correction signal provided by the soil moisture sensing unit. To make a decision, the control logic is set as follows: when When the reading is below 45% of the preset irrigation start threshold, the drip irrigation unit is activated to begin irrigation. Irrigation is stopped when the reading rises back to 75% of the preset irrigation stop threshold; this control method corresponds to the operating logic used in the control group in Example 2.

[0046] The experiment replicated the scenario of a rapid change in lighting conditions after noon in Example 2; during the initial low-demand phase, from 0 to 30 minutes, PAR was maintained at ,soil It slowly decreased from approximately 61.8%; when environmental conditions changed abruptly at T=30 to 35 minutes, PAR rose to Furthermore, when the relative humidity drops to 60%, the atmospheric transpiration demand increases rapidly; however, since this proportional control system lacks the ability to sense changes in atmospheric demand, its decision-making relies on soil moisture. This is a lagging indicator; therefore, during the period from T=40 min to T=60 min when high transpiration demand has occurred, although the actual water demand of the loofah canopy has increased, the soil... The readings decreased from approximately 57.1% to 47.9%, as shown in Table 1 of Example 2 (control group data), but still did not reach the 45% irrigation initiation threshold, causing the system to fail to initiate an irrigation response during this period; until T=65min, the soil... The system only initiated irrigation when the water level dropped to 44.8%, below the 45% threshold; irrigation continued until T=75 minutes later. Irrigation was stopped when the water potential of the loofah leaves rose above the 75% threshold. During this period, at T=60min, the average water potential of the loofah leaves was -1.25MPa, lower than the -0.85MPa measured at the same time in the experimental group using the technical solution of this invention in Example 2. The experimental results show that, when using drip irrigation control based solely on soil moisture feedback, the irrigation response lags behind the actual demand in scenarios with a rapid increase in atmospheric transpiration. This lag causes the loofah plants to experience a prolonged root zone water deficit during the high transpiration period, manifested as... The continuous decline and more pronounced water stress state are manifested by a decrease in leaf water potential, which fails to match the crop's water requirements in a timely manner.

[0047] Example 5: To verify the self-diagnosis and safety degradation capabilities of the drip irrigation system when the feedforward demand signal source experiences potential failure, the following simulation experiment was conducted: In a continuously operating loofah cultivation scenario, the evaporator of the atmospheric demand simulation unit was artificially and gradually covered with a thin film with poor water permeability to simulate the feedforward demand signal caused by physical pollution, such as algae growth. The process of systematically low output values; during the initial system run, The signal is normal, and the main controller bases its response on the self-calibrated signal. Value calculation basic irrigation instructions And corrected by feedback signal Boundary corrections were made, and irrigation operations are normal. Maintaining it within the suitable range of 55% to 70%; as simulated pollution intensifies, The signal output value continued to decrease, causing the main controller to calculate... The transpiration rate is lower than the actual transpiration requirements of loofah; initially, due to the buffering effect of soil moisture... The signal decline was slow and had not yet triggered the 40% drought threshold; however, as time went on, the main controller detected a feedback correction signal. Frequency of events that trigger the drought threshold and initiate supplemental irrigation commands It begins to rise; when it is within the preset 48-hour time window, The total number of occurrences reached 12, exceeding the system's built-in logical conflict frequency threshold. When set to 10 times / 48 hours, the main controller determines the feedforward demand signal. Unreliable; therefore, the main controller automatically executes a safety degradation procedure: switching the control logic of the drip irrigation system to rely solely on feedback correction signals. The reactive closed-loop model, that is, with When the soil moisture level falls below 40%, irrigation is initiated solely based on this condition. Simultaneously, an alarm message is sent to the user management terminal stating: "There is a persistent conflict between the atmospheric demand simulation unit signal and the soil moisture feedback. It is recommended to check and maintain the ADS unit."

[0048] Example 6: In a large-scale loofah cultivation system divided into multiple hydraulically isolated zones (HQZs), the following scenario was demonstrated to verify its system-level health diagnosis and proactive disease prevention functions; the system configuration includes root zone environmental sensors distributed within each HQZ, integrating... and The system includes measurement functions with accuracies of ±2% and ±0.1 mS / cm, as well as sensors for monitoring the flow rate of the main pipeline and each HQZ branch pipeline with an accuracy of ±1%. Before a planned irrigation is started, the main controller first executes a physical integrity self-check procedure: briefly opening the main valve and then sequentially opening each HQZ valve to read the flow rate of the main pipeline. Traffic flow of each branch , pass verification and Check if it's within the preset error range, such as ±5%, to confirm there are no blockages or leaks in the pipeline network; after confirming physical integrity, the system performs irrigation on the HQZ-A area according to the predetermined plan; after irrigation, the main controller continuously monitors the dynamic response curve of the root zone environmental sensor in HQZ-A; monitoring reveals that HQZ-A's... The time required for the reading to drop from the peak of 82% to 65.6%, or a decrease of 20%, is... It will last for 75 minutes; the main controller will then... The corresponding time of the health response curve predicted by its built-in hydrodynamic digital twin model Based on the soil type of the area, a 50-minute calibration was performed, and the deviation was calculated for comparison. This value exceeded the preset deviation threshold of 1.3; simultaneously, the controller read the soil conductivity of the area. The value is 3.5 ms / cm, which is higher than the normal threshold, such as 2.5 ms / cm; based on Response lag, deviation exceeding threshold and high By reading these two indicators, the main controller assesses the health status of HQZ-A as risky, specifically identifying it as potentially high humidity and high salinity or decreased root vitality. Subsequently, the main controller triggers a hydraulic isolation control strategy: in subsequent irrigation scheduling, the irrigation execution time of HQZ-B and HQZ-C, which are physically adjacent to HQZ-A, will be forcibly scheduled at least 2 hours after HQZ-A's irrigation is completed, in order to form a time barrier for hydraulic transmission between HQZs and prevent potential pathogens or high salinity water from migrating across zones.

[0049] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A drip irrigation system for loofah cultivation, characterized in that, The system includes: The system includes an atmospheric demand simulation unit, a soil moisture sensing unit, a drip irrigation execution unit, and a main controller. The atmospheric demand simulation unit is configured to generate a feedforward demand signal characterizing the driving force of atmospheric transpiration. ; The soil moisture sensing unit is configured to generate a feedback correction signal characterizing the soil moisture in the root zone. ; The main controller is configured to perform the following steps: Step a, based on the feedforward demand signal With a currently stored crop coefficient Multiply to generate a basic irrigation instruction; step b, adjust based on feedback signals. The reading is used to reject or correct the basic irrigation instruction based on a preset safety threshold; step c, at the end of a preset calibration cycle, based on the total irrigation amount applied by the drip irrigation execution unit during the cycle. and the net change in soil moisture between the start and end points of the cycle as measured by the soil moisture sensing unit. Calculate an actual evapotranspiration rate Step d: Within the same calibration cycle, by analyzing the feedforward demand signal... Perform time integration to calculate a total potential evaporation. Step e, calculate the actual evapotranspiration. With total potential evaporation The ratio between them is used to calculate and update the crop coefficient used in step a. .

2. The drip irrigation system for loofah cultivation according to claim 1, characterized in that, The atmospheric demand simulation unit includes: a standardized evaporator configured to be exposed to the canopy microclimate of a loofah plant; a miniature water reservoir for supplying water to the standardized evaporator; and a measurement component configured to measure the rate of water loss from the miniature water reservoir to generate a feedforward demand signal. The measuring components are weighing sensors or liquid level sensors.

3. The drip irrigation system for loofah cultivation according to claim 1, characterized in that, When the main controller executes step b, the rejection or correction includes: when a correction signal is fed back. When the soil moisture level exceeds a preset over-wetness threshold, the execution intensity of basic irrigation instructions is rejected or reduced; when a correction signal is fed back... When the soil moisture level falls below a preset drought threshold, a supplementary irrigation instruction is triggered in addition to the basic irrigation instruction.

4. The drip irrigation system for loofah cultivation according to claim 1, characterized in that, Before executing step a, the main controller is further configured to: correct the signal based on feedback. Given the current value, a smoothing time constant is calculated and determined; a time-domain smoothing filter with the smoothing time constant as a parameter is used to smooth the feedforward demand signal. The process is performed to generate a smoothed feedforward demand signal; and in step a, the main controller replaces the feedforward demand signal with the smoothed feedforward demand signal. To generate basic irrigation instructions; wherein the main controller is configured to: increase the smoothing time constant when the soil moisture is higher than a preset smoothing adjustment threshold; and decrease the smoothing time constant when the soil moisture is lower than the preset smoothing adjustment threshold.

5. A drip irrigation system for loofah cultivation according to claim 3, characterized in that, The main controller is further configured to: monitor the feedback correction signal in step b. The frequency of events that trigger a preset drought threshold and result in supplementary irrigation commands. When the event frequency satisfy When conditions are met, determine the feedforward demand signal. Unreliable, among which A preset logic conflict frequency threshold is set; in response to the determination, the control logic of the drip irrigation system is downgraded to rely solely on feedback correction signals. The reactive closed-loop mode generates an alert indicating that the atmospheric demand simulation unit needs maintenance.

6. The drip irrigation system for loofah cultivation according to claim 1, characterized in that, When the main controller executes step c, it is configured to: read the total irrigation volume within a preset calibration period. Read the net change in soil moisture at the beginning and end of the preset calibration cycle. ; Based on the principle of water balance, through Calculate the actual evapotranspiration .

7. The drip irrigation system for loofah cultivation according to claim 1, characterized in that, The drip irrigation execution unit includes multiple drip irrigation hardware units, which are physically divided into at least two hydraulic isolation zones. Each hydraulic isolation zone includes at least one independently controllable valve. The main controller is further configured to: when the health status of the first hydraulic isolation zone is assessed as being in a risky state, trigger a hydraulic isolation control strategy for the first hydraulic isolation zone at the logical topology level of system control. The hydraulic isolation control strategy includes modifying the irrigation schedule of the second hydraulic isolation zone, which is physically adjacent to the first hydraulic isolation zone, so that the irrigation execution times of the first and second hydraulic isolation zones are forcibly separated on the time axis.

8. A drip irrigation system for loofah cultivation according to claim 7, characterized in that, The system also includes at least one root zone environmental sensor, configured within the hydraulically isolated zone, for collecting root zone environmental data within the hydraulically isolated zone. The root zone environmental data includes at least soil volumetric water content. Data and soil electrical conductivity data; The steps for the main controller to assess its health status include: based on Data and Data can be used to diagnose whether the first hydraulic isolation zone is in a specific risk pattern of high humidity and high salinity or loss of root water absorption capacity.

9. A drip irrigation system for loofah cultivation according to claim 8, characterized in that, The main controller also has a built-in hydrodynamic digital twin model. The main controller is further configured to: monitor the dynamic response curve of the root zone environmental data after an irrigation event occurs, compare the dynamic response curve with the health response curve predicted by the hydrodynamic digital twin model, and assess the health status of the hydraulic isolation zone as a risk state when the deviation between the dynamic response curve and the health response curve exceeds a preset threshold.

10. A drip irrigation system for loofah cultivation according to claim 7, characterized in that, The system also includes flow or pressure sensors for monitoring the main pipeline and branch pipelines in each hydraulic isolation zone; the main controller is further configured to perform an energy flow or mass flow conservation check on the physical integrity of the system based on the readings of the flow or pressure sensors before irrigation is performed.

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