A drought and heat wave compound disaster active defense irrigation method based on land-air decoupling mechanism

Abstract

The application provides a drought and heat wave compound disaster active defense irrigation method based on a land-air decoupling mechanism, comprising the following steps: step 1, a multi-source sensing and gradient monitoring fusion system suitable for a land-air decoupling algorithm is constructed; step 2, a dynamic land-air coupling index based on hydraulic and thermal force collaborative constraints is constructed; step 3, a disaster critical mutation early warning and active triggering mechanism based on double derivatives and physiological phase change collaboration is established; step 4, a land-air decoupling irrigation amount solving model containing a horizontal flow heat compensation mechanism is constructed; step 5, non-uniform spatial rotation irrigation matrix optimization and scheduling are carried out based on a microclimate cold island footprint model; step 6, a double closed loop verification and dynamic termination mechanism based on stomatal hydraulic hysteresis and thermal phase change reversal is established; and step 7, adaptive updating of a post-disaster cold island footprint and horizontal flow compensation parameters is realized. The method can effectively prevent disaster rebound caused by premature water stop, and guarantees the survival and yield safety of crops under extreme stress.

Description

Technical Field

[0001] This invention relates to the fields of smart agriculture and agricultural meteorological disaster prevention technology, specifically to an active irrigation method for preventing combined drought and heat wave disasters based on a land-atmosphere decoupling mechanism. Background Technology

[0002] As global warming intensifies, compound meteorological disasters combining sudden droughts and extreme heat waves are becoming more frequent. This leads to a sharp increase in farmland evaporation demand and rapid depletion of soil moisture, which can easily induce stomatal closure, photosynthetic stagnation, and even irreversible yield losses in crops. In responding to such extreme disasters, accurately capturing the abrupt changes in land-atmosphere interactions and implementing decoupling regulation is crucial. However, existing agricultural meteorological monitoring and irrigation decision-making technologies have significant limitations: First, traditional monitoring relies heavily on single-point static stations and fixed low-frequency sampling (e.g., hourly), making it difficult to capture the horizontal advection energy input by hot and dry winds and the instantaneous phase transition of crop stomata under heat wave impact, resulting in data lag and insufficient spatial representativeness; second, existing land-atmosphere coupling assessment indices mostly use static linear weights, failing to reflect the nonlinear drop in crop stomatal conductance and the "thermal memory" fatigue effect under high-temperature stress, leading to false alarms or missed alarms in disaster warnings; third, conventional irrigation models are generally based on the one-dimensional vertical energy balance assumption (e.g., the Penman-Monteith equation), ignoring the significant contribution of horizontal thermal advection to crop water consumption during drought and heat waves, resulting in an underestimation of theoretical water demand and difficulty in breaking the positive feedback chain between land and atmosphere; finally, irrigation control logic often relies solely on the absolute threshold of soil moisture, ignoring the "hydraulic lag effect" of stomatal reopening after rehydration and the wind field diffusion law of the cold island effect, leading to premature termination of irrigation or uneven spatial distribution, and failing to achieve true physiological-ecological decoupling. Therefore, there is an urgent need to develop a new system that integrates multi-source gradient sensing, dynamic nonlinear coupling assessment, three-dimensional advection thermal compensation, and physiological phase change synergistic control to solve the problem of precise defense under extreme complex disasters.

[0003] Existing agricultural meteorological monitoring and precision irrigation technologies face the following technical bottlenecks and shortcomings when dealing with combined disasters of drought and extreme heat waves:

[0004] (1) Spatial and temporal limitations of monitoring and lack of advection capture: Traditional monitoring relies on single-point static stations and fixed low-frequency sampling, which cannot capture the "boundary-center" micro-meteorological gradient formed by the horizontal advection energy input by hot dry winds. It is also difficult to capture the instantaneous nonlinear change (phase transition point) when crop stomata change from opening to closing under high temperature stress, resulting in serious time delay and spatial blind spots in disaster early warning.

[0005] (2) Rigid land-atmosphere coupling assessment index: Existing land-atmosphere coupling indices usually use fixed static weights, which cannot dynamically reflect the cliff-like decline in crop stomatal conductance and the "thermal fatigue memory" effect when soil moisture depletion and atmospheric evaporation demand surge, resulting in a distortion of the assessment of the intensity of drought heat waves.

[0006] (3) The single dimension of the irrigation model leads to water volume deviation: Conventional irrigation decision models (such as the Penman-Monteith equation) are generally based on the assumption of one-dimensional vertical energy balance, ignoring the huge contribution of the significant horizontal advection heat flux to crop water consumption under extreme heat waves, resulting in the calculated theoretical irrigation volume being far lower than the actual energy decoupling requirement required to block the positive feedback between land and air.

[0007] (4) The regulation mechanism ignores physiological lag and wind field effect: The existing control logic mostly relies on the absolute threshold of soil moisture, ignoring the "hydraulic lag effect" of stomata reopening after rehydration and the wind field diffusion law of cold island effect, resulting in delayed irrigation triggering, premature termination or uneven spatial distribution, which cannot effectively relieve the heat stress shock state of crops. Summary of the Invention

[0008] Objective of the Invention: The technical problem to be solved by this invention is to address the shortcomings of existing technologies by providing an active defense irrigation method for combined drought and heat wave disasters based on a land-atmosphere decoupling mechanism. This technology is mainly used for early warning of micro-meteorological changes in farmland under combined drought and heat wave disasters, monitoring of crop physiological stress, and precise irrigation control in non-uniform spaces.

[0009] This method includes the following steps:

[0010] Step 1: Construct a multi-source sensing and gradient monitoring fusion system adapted to the land-atmosphere decoupling algorithm;

[0011] Step 2: Construct a dynamic land-atmosphere coupling index based on hydraulic and thermal synergistic constraints;

[0012] Step 3: Establish a disaster critical mutation early warning and active triggering mechanism based on the synergy of dual derivatives and physiological phase transition;

[0013] Step 4: Construct a land-atmosphere decoupled irrigation quantity solution model that includes an advection thermal compensation mechanism;

[0014] Step 5: Optimize and schedule the non-uniform spatial rotation irrigation matrix based on the micro-meteorological cold island footprint model;

[0015] Step 6: Establish a dual closed-loop verification and dynamic termination mechanism based on pore hydraulic hysteresis and thermodynamic phase change reversal.

[0016] Step 7: Implement adaptive updates of the post-disaster cold island footprint and advection compensation parameters.

[0017] Step 1 includes:

[0018] Step 1.1: Establish a monitoring network for the boundary and central micro-meteorological advection gradient;

[0019] Based on the prevailing wind direction during historical drought and heat wave periods in the target area, a spatial gradient monitoring network is constructed, including: windward boundary monitoring stations: deployed at the windward edge of farmland to collect characteristic parameters of advection hot and dry winds in real time, including boundary air temperature T. bound Boundary wind speed U bound and relative humidity (RH) bound ;

[0020] Core flux stations in the hinterland of farmland: Deployed downwind or in the central hinterland of farmland, these stations measure net radiation flux R in real time using an eddy covariance system. n Soil heat flux G, sensible heat flux H, and latent heat flux LE; simultaneously, the ambient air temperature T is collected. core Real-time acquisition of the horizontal air temperature difference ΔT between the windward boundary and the core of the farmland hinterland. horizontal =T bound -T core ;

[0021] Step 1.2, deploy soil vertical profile sensors and crop canopy physiological monitoring equipment, including:

[0022] Dynamic root zone soil sensing: Soil moisture sensors are buried in layers at four depths: 0~10cm, 10~30cm, 30~60cm, and 60~100cm, to continuously collect the volumetric water content of the first soil layer. , where l is the soil layer number, and l=1, 2, 3, 4 correspond to four depths: 0~10cm, 10~30cm, 30~60cm, and 60~100cm, respectively;

[0023] Based on the collected data, the rate of change of soil moisture content at different depths over time was calculated. Where t is time and d represents the derivative; subsequently, based on the rate of decrease of the soil moisture content change rate in each layer, normalization is performed according to the following formula to determine the dynamic weight coefficient of each layer. :

[0024] ,

[0025] Among them, intermediate parameters ;

[0026] When all layers When the sum is 0 or less than a preset threshold, a weighting coefficient is set. 1 / 4; and according to the weighting coefficient The weighted calculation of the overall soil volumetric water content θ in the root zone was performed.

[0027] High-frequency infrared canopy temperature array: Deploy non-contact infrared temperature sensors to continuously acquire crop canopy temperature T. c And obtain the ambient air temperature T in real time. a By controlling the canopy temperature T c Subtract air temperature T a Real-time calculation of the temperature difference ΔT between the canopy and the air canopy =T c -T a , will ΔT canopy As a core physiological indicator characterizing the stomatal thermal stress state of crops;

[0028] Step 1.3 introduces an adaptive frequency conversion acquisition mechanism based on stress threshold;

[0029] The following dual-modal acquisition strategy is adopted:

[0030] Normal cruise mode: When the calculated saturated vapor pressure difference (VPD) is < 1.5 kPa and the relative available soil moisture (REW) is > 0.5, data is polled and stored at the normal frequency.

[0031] High-frequency capture mode for heat wave surges: Once the rate of temperature rise is detected... Or, if the VPD exceeds the preset safety threshold, it will automatically trigger the high-frequency acquisition mode to record the flux data and canopy temperature in a high-frequency pulse manner.

[0032] Step 1.4, Forced energy closure correction of flux data under extreme heat waves;

[0033] On hot and arid underlying surfaces, the energy measured by the primitive eddy covariance system is usually not closed, i.e., R exists. n To address the issue of -G>H+LE, a Bowen ratio energy closure correction algorithm is introduced at the data fusion layer. This algorithm is based on the Bowen ratio before and after correction. Assuming that it remains unchanged, the effective energy (R) n -G) is forcibly allocated to sensible heat and latent heat to obtain the corrected sensible heat flux. and latent heat flux :

[0034] ,

[0035] ,

[0036] in, .

[0037] Step 2 includes:

[0038] Step 2.1, obtain the state-driven variables;

[0039] Calculate the relative available soil moisture at time t and normalized saturated vapor pressure difference VPD norm (t):

[0040] ,

[0041] ,

[0042] in, The overall soil volumetric water content in the root zone, , The measured volumetric water content at time t for layer l. Field holding capacity Moisture content at the wilting point; The actual saturated water vapor pressure difference at time t; and These are the maximum and minimum saturated vapor pressure differences during the current crop growth period, respectively.

[0043] when The evapotranspiration deficit at time t is calculated using the following formula. :

[0044] ,

[0045] when or When the value is less than the preset threshold, the smoothed value of the adjacent time period is used for calculation;

[0046] in The actual latent heat flux after correction at time t;

[0047] Calculate the soil moisture anomaly at time t :

[0048] ;

[0049] Step 2.2: Construct a nonlinear dynamic weighting function;

[0050] Calculate the dynamic weight β(t) of soil moisture deficit at time t:

[0051] ,

[0052] in, As the basic weight of soil, The maximum weight under the drought limit state; The critical relative available water for inducing nonlinear stomatal closure; k1 is the soil drought sensitivity coefficient greater than 0, and e is the natural constant;

[0053] Calculate the dynamic weights of atmospheric dry heat stress at time t :

[0054] ,

[0055] in, Here, k2 represents the atmospheric baseline weight, and k2 is a greater than 0 atmospheric drought sensitivity coefficient. The normalized saturated water vapor pressure difference at time t;

[0056] Calculate the dynamic weight of sensible heat flux at time t Dynamic weighting of latent heat flux :

[0057] ,

[0058] ,

[0059] in, and These are the initial calibration coefficients. for The upper limit of the atmospheric dry heat stress weight is taken when the maximum value is 1. , and It is a non-negative value;

[0060] Step 2.3, dynamic weight normalization processing;

[0061] Real-time normalization of dynamic weights:

[0062] ,

[0063] Where i is the weight category index. , , , , respectively, correspond to the dynamic weights of sensible heat flux, soil moisture deficit, latent heat flux and atmospheric dry heat stress; Represents the dynamic weight of the i-th class; These are the normalized weights;

[0064] Step 2.4, introduce the stomatal physiological thermal memory penalty term Ω(t) at time t:

[0065] ,

[0066] in, The highest temperature on the i-th day before the current moment. This is the upper limit of the optimal temperature for crop growth; λ is the thermal memory sensitivity coefficient, and λ is the time decay constant;

[0067] Step 2.5, calculate the dynamic land-atmosphere coupling index D-LACI(t) at time t:

[0068] First, the temperature difference between the canopy and the air is normalized to obtain the normalized canopy temperature difference. :

[0069] ,

[0070] in, Let t be the temperature difference between the canopy and the air. and These represent the maximum and minimum temperature differences between the canopy and the air during the current crop growth period;

[0071] ,

[0072] in, , , and These are the sensible heat flux, soil moisture deficit, latent heat flux, and dynamic weights of atmospheric dry heat stress after normalization in step 2.3.

[0073] Step 3 includes:

[0074] Step 3.1: Extract the time-series dynamic features of D-LACI;

[0075] Based on the time series of the dynamic land-atmosphere coupling index obtained in step 2, the evolution rate and acceleration of the dynamic land-atmosphere coupling index are calculated in real time using the sliding window algorithm:

[0076] ,

[0077] ,

[0078] in, The evolution rate of the dynamic land-atmosphere coupling index at time t; A LACI (t) represents the evolution acceleration of the dynamic land-atmosphere coupling index at time t; d / dt and d² / dt² are the first-order and second-order differential operators with respect to time t, respectively.

[0079] Step 3.2: Extract the reverse phase transition characteristics of the canopy and air temperature difference;

[0080] The criteria for determining the physiological abnormal transition of canopy heat stress are defined as follows:

[0081] and ,

[0082] in, An empirical threshold for the abnormal rate of change of the temperature difference between the canopy and the air is set, which characterizes the increase rate of the temperature difference between the canopy and the air per unit time. When this value is exceeded, it is determined that the heat stress is intensified. The set safe temperature difference threshold for the canopy;

[0083] Step 3.3: Construct the critical mutation dual verification trigger matrix;

[0084] The following three dynamic defense lines are defined:

[0085] State I: V LACI (t)≤0 or A LACI (t)≤0 or <0, plant transpiration and cooling are normal or the land-atmosphere coupling index does not show an accelerating upward trend, continue monitoring and do not trigger irrigation;

[0086] State II: V LACI (t)>0 and A LACI (t)>0 and ≥0, but or This triggers step 4 in advance to calculate the defensive basic irrigation volume;

[0087] State III: V LACI (t)>0 and A LACI (t)>0, and and Step 4 is immediately triggered to calculate the forced cooling irrigation volume including advection compensation, and a highest priority execution command is sent to the control terminal.

[0088] Step 4 includes:

[0089] Step 4.1, calculate the target latent heat flux gap ΔLE in the vertical direction. vertical ;

[0090] Calculate the target latent heat flux :

[0091] ,

[0092] in, The maximum safe heat flux threshold is preset based on crop type, growth period, and historical calibration results;

[0093] Calculate the current vertical latent heat gap :

[0094] ,

[0095] when When the temperature is >0, it indicates that the current transpiration cooling capacity is insufficient to pull the canopy temperature back to a safe range, and irrigation is needed to replenish soil moisture to increase the latent heat output of transpiration; when When this occurs, it indicates that the current actual latent heat flux has met or exceeded the target latent heat flux requirement, and no vertical irrigation compensation is needed. ;

[0096] Step 4.2, calculate the horizontal advection dry hot wind energy compensation term at time t. :

[0097] ,

[0098] in, air density, The specific heat capacity of air at constant pressure. (t) represents the wind speed at the windward side boundary at time t. (t) and These represent the air temperatures at the windward boundary and the inland core at time t, respectively. The length of the cold island effect wind zone. (t) represents the characteristic height of the near-surface atmospheric mixing layer at time t;

[0099] Step 4.3: Solve for the vertical-horizontal coupled defensive total latent heat requirement and theoretical irrigation water depth. ;

[0100] By combining the vertical gap with the horizontal compensation term, and based on the early warning status triggered in step 3.3, a defensive total latent heat demand model is constructed:

[0101] ,

[0102] in, (t) represents the total defensive latent heat demand at time t. is the advection energy absorption coefficient, which characterizes the actual offsetting ratio of irrigation evaporation to advection heat;

[0103] When state II is triggered, only the defensive basic irrigation amount is calculated, taking... =0;

[0104] When state III is triggered, the forced cooling irrigation amount including advection compensation is calculated, the advection compensation term is activated, and the value is taken. >0;

[0105] Using the latent heat of vaporization The total heat energy demand is converted into the required theoretical irrigation water depth:

[0106] ,

[0107] in The preset irrigation intervention time window, The density of water;

[0108] Step 4.4: Apply soil hydraulic and dynamic safety constraints and output the final irrigation amount. ;

[0109] Infiltration constraints are applied to the theoretical irrigation volume:

[0110] ,

[0111] in, For soil saturated hydraulic conductivity, saturated moisture content, (t) represents the average soil volumetric water content in the root zone at time t. For root depth, This represents the maximum infiltration rate at time t.

[0112] The final output command shows the amount of irrigation water. for:

[0113] ,

[0114] if (t)> The difference (t)- Convert to canopy spray or micro-mist spray dosage.

[0115] Step 5 includes:

[0116] Step 5.1: Irrigation management gridding and real-time wind field vector mapping;

[0117] The target area is divided into N×M independent and controllable micro-irrigation grid units, where N and M represent the number of grid units in the horizontal and vertical directions of the target area, respectively, and both N and M are positive integers. The three-dimensional wind field data from the windward boundary station in step 1 is extracted in real time to obtain the current prevailing wind direction angle θ. w With wind speed U bound And map it to the grid coordinate system to determine the upwind starting boundary and downwind airflow path of the entire domain;

[0118] Step 5.2, based on Gaussian plume diffusion theory, construct the cold air advection cooling footprint equation:

[0119] ,

[0120] in, The effective cooling contribution of irrigation grid k to the downwind grid (i, j); This represents the maximum theoretical evaporative cooling rate of the source grid. Distance in the downwind direction. This refers to the crosswind distance; This refers to the length of the cold air attenuation along the windward direction; σ is the lateral turbulent diffusion coefficient; exp is the natural exponential function;

[0121] Step 5.3: Solve for the optimal source and sink topology matrices under limited water volume;

[0122] Establish the optimization objective function:

[0123] ,

[0124] Where g represents the set of all grid cells within the target region; The set of grids selected to have irrigation enabled; k is... The irrigation grid index in the text; η is the diminishing effect adjustment coefficient for cooling, which characterizes the marginal deceleration rate of the cumulative cooling contribution at a single point;

[0125] The constraints are:

[0126] ,

[0127] in, The irrigation water depth for grid k; Let k be the area of ​​grid k; This represents the maximum total water volume that the system can currently access.

[0128] The objective function is solved in real time using an optimization algorithm, and an optimal spatial water distribution matrix M consisting of 0s and 1s is output. opt 1 represents grid irrigation is enabled, and 0 represents it is disabled, relying on advection cooling;

[0129] Step 5.4: Dynamically issue instructions for non-uniform patchy irrigation and spraying;

[0130] The optimal spatial water distribution matrix M opt The analysis is based on the control commands of the underlying solenoid valves. During periods of strong winds and heat waves, the system outputs a non-uniform interval irrigation topology map, with each activated grid following the I calculated in step 4. final And with ultra-fine atomization ratio for precise operation, if the wind direction changes suddenly during the operation, i.e. Δθ w If the temperature exceeds 30°, the system will immediately return to step 5.1 to recalculate the dynamic cold island footprint and switch the irrigation matrix sequence.

[0131] Step 6 includes:

[0132] Step 6.1: High-frequency tracking of the canopy cooling trajectory and recovery derivative;

[0133] After the irrigation command is executed, the cooling target grid selected in step 5 is locked, and the temperature difference ΔT between the canopy and the air is continuously monitored at extremely high frequency. canopy Real-time calculation of canopy cooling rate:

[0134] ;

[0135] in, Let t be the canopy cooling rate at time t;

[0136] Step 6.2: Establish a dual phase transition reversal termination determination matrix based on physiological and environmental factors;

[0137] The defense response will only be allowed to be completely terminated if the following dual phase transition reversal criteria are met:

[0138] Condition A: The dynamic land-atmosphere coupling index calculated in step 2 falls back below the preset safety baseline, and the first derivative of the dynamic land-atmosphere coupling index... ≤0;

[0139] Condition B: Canopy temperature difference ΔT canopy The value changes from positive to a stable negative value and remains there for a period of time exceeding the preset stomatal physiological inertia time window.

[0140] Only when conditions A and B are met simultaneously is the land-atmosphere decoupling considered successful, and an order to stop irrigation or spraying is issued.

[0141] Step 6.3, Pulsed micromist intervention under the hysteresis dilemma;

[0142] If the irrigation water volume calculated in step 4 has been completed, but condition B in step 6.2 is not met, the pulsed micro-mist compensation program is triggered: surface drip irrigation or flood irrigation is suspended, and the spatial ultrasonic micro-mist or high-pressure micro-spray system is automatically started. Each time, micro-mist is released into the canopy space in a short pulse manner to increase local air humidity and reduce canopy transpiration resistance, thereby promoting the reopening of stomata until condition B is met.

[0143] Step 7 includes: After the drought and heat wave process ends, the offline review and parameter self-learning phase begins.

[0144] Extract the actual wind field, actual total water consumption, and actual cooling area during this round of disasters, and substitute them into the cold air advection cooling footprint equation in step 5 and the formula in step 4.2;

[0145] The lateral turbulent diffusion coefficient σ was analyzed using the nonlinear least squares method based on measured data from this disaster event. v The advection energy absorption coefficient ξ is inverted and corrected, and the corrected parameters are updated to the system parameter database.

[0146] The present invention also provides an electronic device, including a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method.

[0147] The present invention also provides a storage medium storing a computer program or instructions that, when the computer program or instructions are run on a computer, execute the steps of the method described.

[0148] Beneficial effects: (1) Breaking through the limitations of single-point static monitoring and realizing the accurate capture of micro-meteorological advection gradient: This invention abandons the traditional single-point central station mode and innovatively constructs a "boundary-center" gradient monitoring network. By comparing the temperature and humidity differences between the windward boundary and the farmland hinterland in real time, the system can quantify the horizontal advection energy (H) of the dry and hot wind input. adv This effectively solves the energy balance calculation errors caused by the neglect of external thermal advection in existing technologies. At the same time, an "adaptive frequency conversion acquisition mechanism" based on stress threshold is introduced, which automatically switches to high-frequency sampling (e.g., once every minute) during periods of heat wave surge, ensuring accurate capture of the "phase transition point" of stomata closure and the moment of flux change, and greatly improving the timeliness of disaster early warning.

[0149] (2) Constructing a dynamic nonlinear coupling index to accurately reflect the crop's thermal stress state: The dynamic land-atmosphere coupling index (D-LACI) proposed in this invention overcomes the shortcomings of traditional indices that use static constant weights. By introducing a nonlinear dynamic weight function that evolves in real time with soil moisture deficit (REW) and atmospheric evaporation demand (VPD), and combining it with a stomatal "thermal fatigue memory" penalty term, D-LACI can accurately quantify the precipitous decline in crop stomatal conductance under extreme high temperatures. This dynamic evaluation mechanism enables the system to distinguish between the "linear consumption period" and the "nonlinear mutation period," thereby triggering defensive irrigation in advance before the disaster outbreak (State II) and effectively blocking the positive feedback chain between land and atmosphere.

[0150] (3) Establishing a three-dimensional advection heat compensation model significantly improves the physical accuracy of irrigation decisions: Addressing the shortcomings of existing irrigation models that only consider one-dimensional vertical energy balance, this invention constructs a three-dimensional irrigation volume calculation model that includes horizontal advection heat compensation. This model not only calculates the latent heat gap in the vertical direction but also superimposes the horizontal advection energy consumption caused by hot, dry winds, resulting in a higher theoretical irrigation water depth (Ii). theory This better meets the real physical needs under extreme heat waves. In addition, by combining soil hydraulic constraints with an ultra-fine misting spraying mechanism, it ensures rapid cooling and decoupling of the crop canopy while avoiding deep seepage and surface runoff, thus achieving efficient use of water resources.

[0151] (4) Introducing a hydrodynamic cold island footprint model to achieve precise rotational irrigation in a spatially non-uniform environment: This invention utilizes a micrometeorological cold island footprint model (MCFM) and real-time wind field vector mapping to solve the problem of low efficiency of traditional uniform irrigation under strong wind conditions. By solving the optimal "source-sink" topology matrix, the system can dynamically adjust the opening sequence of the irrigation grid according to wind direction and speed, utilizing the evaporative cooling effect of the upwind grid to cover the downwind area, maximizing the equivalent cooling area across the entire region. This hydrodynamic-based spatial optimization strategy significantly improves the cooling energy efficiency ratio of irrigation during periods of drought and heat waves when water resources are scarce.

[0152] (5) Dual closed-loop control based on physiological hysteresis to ensure complete relief of heat shock: The irrigation termination mechanism proposed in this invention breaks through the limitation of a single soil moisture threshold and fully considers the "hydraulic hysteresis effect" after plant stomatal rehydration. By monitoring the canopy temperature recovery rate (R... cool The system establishes a dual reversal condition of "microclimate environment + plant physiological phase transition" to ensure that irrigation is only stopped after the stomata have truly reopened and the crop has escaped the heat stress state. This closed-loop feedback mechanism effectively prevents the rebound of disasters caused by premature water cessation and safeguards the survival and yield of crops under extreme stress. Attached Figure Description

[0153] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0154] Figure 1 This is the overall flowchart of an irrigation method for proactive defense against combined drought and heat wave disasters based on the land-atmosphere decoupling mechanism.

[0155] Figure 2 This is a schematic diagram of a single-source grid Gaussian plume cold air diffusion and cooling footprint model. Detailed Implementation

[0156] like Figure 1 As shown, this embodiment of the invention provides an active irrigation method for preventing combined drought and heat wave disasters based on a land-atmosphere decoupling mechanism, comprising the following steps:

[0157] Step 1: Construct a multi-source sensing and gradient monitoring fusion system adapted to the land-atmosphere decoupling algorithm;

[0158] The multi-source sensing and gradient monitoring fusion system is an adaptive high-frequency monitoring and data fusion network based on the "boundary-center" gradient. It is used to collect micro-meteorological characteristic parameters and energy advection input data in real time during drought and heat wave outbreaks, specifically including:

[0159] Step 1.1: Deploy a "boundary-center" micro-meteorological advection gradient monitoring network;

[0160] Based on the prevailing wind direction during historical drought and heatwave periods in the target area, a spatial gradient monitoring network is deployed along the prevailing wind direction axis: (1) Windward side boundary monitoring station: deployed at the windward edge of the farmland, i.e., at the upwind port where the drought air mass or heatwave air mass enters the farmland area. The windward side boundary monitoring station is equipped with a temperature sensor, a wind speed sensor and a humidity sensor to collect characteristic parameters of the advection dry and hot wind in real time, including the boundary air temperature T. bound Boundary wind speed U bound and relative humidity (RH) bound (2) Core flux station in the hinterland of farmland: located downwind or in the central hinterland of farmland, the core flux station is equipped with an eddy covariance system for real-time measurement of net radiation flux R. n The core flux station is equipped with soil heat flux G, sensible heat flux H, and latent heat flux LE; it also has a temperature sensor to simultaneously collect the inland air temperature T. core With the above layout, the system can acquire the horizontal temperature gradient ΔT in real time. horizontal， Its calculation method is ΔT horizontal =T bound -T core .

[0161] Step 1.2, deploy soil vertical profile sensors and crop canopy physiological monitoring equipment; (1) Dynamic root zone soil sensing: soil temperature and humidity sensors are buried in layers at four depths: 0~10cm, 10~30cm, 30~60cm, and 60~100cm. These sensors are used to continuously collect the volumetric water content of each soil layer. Where l is the soil stratification number, and l=1, 2, 3, 4 correspond to the four depths mentioned above, respectively. Based on the collected data, the system calculates the rate of change of soil moisture content over time at different depths. (Where t is time); Subsequently, based on the rate of decrease of the soil moisture content change rate in each layer, the dynamic weight coefficients for each layer are determined by normalization according to the following formula. :

[0162] ,

[0163] in ;

[0164] When all layers When the sum is 0 or less than a preset threshold, equal weights are set. 1 / 4; and based on this, the overall soil volumetric water content θ in the root zone is calculated using a weighted average.

[0165] (2) High-frequency infrared canopy temperature array: Non-contact infrared temperature sensors are deployed to continuously acquire crop canopy temperature T. c And obtain the ambient air temperature T in real time. aBy controlling the canopy temperature T c Subtract air temperature T a Real-time calculation of the temperature difference ΔT between the canopy and the air canopy =T c -T a This is used as a core physiological indicator to characterize the stomatal thermal stress state of crops.

[0166] Step 1.3 introduces an "adaptive frequency conversion acquisition mechanism" based on a stress threshold;

[0167] The adaptive frequency conversion acquisition mechanism adopts a dual-mode acquisition strategy, including a normal cruise mode and a high-frequency capture mode for heat wave surges. The system automatically switches between the two modes based on real-time monitoring data.

[0168] (1) Normal cruise mode: When the saturated vapor pressure difference (VPD) calculated by the system in real time is < 1.5 kPa and the relative effective soil moisture (REW) is > 0.5, the system determines that the current environment has not reached the stress state, and the acquisition module operates in normal cruise mode. Each sensor polls and stores data at a sampling frequency of 30 minutes / time to reduce power consumption and reduce data redundancy. (2) High-frequency capture mode for heat wave surge: When the rate of temperature rise is detected... When VPD ≥ 1.5 kPa, the system automatically switches to high-frequency acquisition mode, and each sensor continuously records flux data and canopy temperature at a sampling frequency of 1 minute / time.

[0169] Step 1.4, Forced energy closure correction of flux data under extreme heat waves;

[0170] On hot and arid underlying surfaces, the energy measured by the original eddy covariance system is usually not closed, i.e., R n -G>H+LE. This embodiment introduces a Bowen ratio energy closure correction algorithm in the data fusion layer. This algorithm is based on the Bowen ratio before and after correction. ( Assuming that the effective energy (R) remains constant, the effective energy (R) will be... n -G) is forcibly allocated to sensible heat and latent heat to obtain the corrected sensible heat flux. and latent heat flux :

[0171] ,

[0172] .

[0173] Step 2: Construct the dynamic land-atmosphere coupling index (D-LACI) based on hydraulic-thermal synergistic constraints;

[0174] This embodiment constructs a dynamic land-atmosphere coupling index (D-LACI) that introduces nonlinear dynamic weights and a stomatal memory penalty term to replace the fixed static weight linear weighting method. The construction process of the D-LACI includes the following steps:

[0175] Step 2.1, obtain the state-driven variables;

[0176] Based on the data obtained in step 1, calculate the soil relative available water REW(t) and normalized saturated vapor pressure difference at time t. Evapotranspiration deficit EF_deficit(t) and soil moisture anomaly SM_anomaly(t).

[0177] (1) Calculate the relative available soil moisture REW(t) at time t:

[0178] ,

[0179] in, The weighted average soil volumetric water content (unit: m³ / m³) obtained in step 1.2 is calculated as follows: , The dynamic weight coefficients of the l-th layer are... (where l is the measured volumetric water content of the l-th layer), l=1, 2, 3, 4 correspond to the four soil depths set in step 1.2 respectively; Field holding capacity Moisture content at the wilting point;

[0180] (2) Calculate the normalized saturated water vapor pressure difference at time t. :

[0181] ,

[0182] in, The actual saturated water vapor pressure difference at time t; and These represent the maximum and minimum saturated vapor pressure difference recorded during the current crop growth period.

[0183] (3) Calculate the evaporation deficit at time t. :

[0184] ,

[0185] in, The actual latent heat flux at time t after Bowen specific energy closure correction in step 1.4 is... Calculate according to the above formula; when or Less than the preset threshold of 10 W m-2 The calculation is performed using smoothed values ​​from nearby time periods to avoid numerical anomalies caused by the denominator approaching zero.

[0186] (4) Calculate the soil moisture anomaly at time t :

[0187] ,

[0188] in, The value range of is [0, 1]. When =1, =0 indicates sufficient soil moisture with no abnormalities; when When it approaches 0, A value close to 1 indicates a severe deficiency of soil moisture.

[0189] Step 2.2: Construct a nonlinear dynamic weighting function;

[0190] This step constructs dynamic weights for soil moisture deficit based on the state-driven variables obtained in step 2.1. Dynamic weights of atmospheric dry heat stress Dynamic weighting of sensible heat flux and dynamic weight of latent heat flux .

[0191] (1) Calculate the dynamic weight β(t) of soil moisture deficit at time t:

[0192] ,

[0193] in, As the basic weight of soil, The maximum weight under the drought limit state; The critical relative available water for inducing nonlinear stomatal closure; k1 is the soil drought sensitivity coefficient greater than 0, e is the natural constant, e ≈2.71828;

[0194] (2) Calculate the dynamic weight of atmospheric dry heat stress at time t :

[0195] ,

[0196] in, Here, k2 represents the atmospheric baseline weight, and k2 is a greater than 0 atmospheric drought sensitivity coefficient. The normalized saturated water vapor pressure difference at time t;

[0197] (3) Calculate the dynamic weights of sensible heat flux α(t) and latent heat flux γ(t) at time t:

[0198] ,

[0199] ,

[0200] in, and These are the initial calibration coefficients. for The upper limit of the atmospheric dry heat stress weight is taken when the maximum value is 1, i.e. , and It is a non-negative value;

[0201] Step 2.3: Perform real-time normalization on the dynamic weights; ,

[0202] Where i is the weight category index. , , , , respectively, correspond to the dynamic weights of sensible heat flux, soil moisture deficit, latent heat flux and atmospheric dry heat stress; Represents the dynamic weight of the i-th class; These are the normalized weights;

[0203] Step 2.4, introduce the stomatal physiological thermal memory penalty term Ω(t) at time t;

[0204] Under continuous high-temperature stress, the stomatal regulation capacity of crops decreases daily due to heat accumulation damage. The current drought response depends not only on the immediate environmental conditions but also on the historical high-temperature history. Therefore, a stomatal physiological thermal memory penalty term Ω(t) is introduced to exponentially decay and accumulate the overheating degree of the previous three days. The calculation formula is as follows:

[0205] ,

[0206] in, The highest temperature on the i-th day before the current moment. The upper limit of the optimal temperature for crop growth; when ≤ At that time, the contribution of overheating on that day is 0; μ is the thermal memory sensitivity coefficient, μ> 0, and the larger its value, the stronger the amplification effect of heat accumulation on the exponential; λ is the time decay constant, λ> 0, and the larger its value, the faster the influence of earlier high temperature events decays on the current moment.

[0207] Step 2.5, calculate the dynamic land-atmosphere coupling index D-LACI(t) at time t;

[0208] First, the temperature difference between the canopy and the air is normalized to obtain the normalized canopy temperature difference. :

[0209] ,

[0210] in, For step 1.2, the canopy temperature T c Subtract air temperature T a The temperature difference between the canopy and the air is calculated in real time. and These represent the maximum and minimum temperature differences between the canopy and the air during the current crop growth period;

[0211] Then, by combining the normalized state-driving variables and normalized dynamic weights, and adding the stomatal thermal memory penalty term, the dynamic land-atmosphere coupling index D-LACI(t) at time t is calculated:

[0212] ,

[0213] in, , , and These are the normalized sensible heat flux, soil moisture deficit, latent heat flux, and dynamic weights of atmospheric dry heat stress, respectively, after step 2.3. The evapotranspiration deficit value calculated in step 2.1; The soil moisture anomaly value is calculated in step 2.1.

[0214] Step 3: Early warning and active triggering mechanism for critical disaster mutations based on the synergy of dual derivatives and physiological phase transitions;

[0215] Existing technologies generally use static absolute thresholds (such as an exponent greater than a certain value) as irrigation triggering conditions. However, due to the significant time lag between soil moisture infiltration and plant physiological recovery, by the time the absolute threshold is reached, the "positive feedback chain" of drought and heat waves has often already formed, making irrigation insufficient to prevent the disaster. Therefore, this invention proposes a proactive triggering mechanism based on dual verification of the D-LACI evolution rate (derivative) and canopy thermodynamic phase change. The specific steps are as follows:

[0216] Step 3.1: Extract the time series dynamic features of D-LACI (mathematical warning).

[0217] Based on the D-LACI time series output from step 2, the evolution rate (first derivative) and evolution acceleration (second derivative) of D-LACI are calculated in real time using the sliding window algorithm.

[0218] ,

[0219] ,

[0220] in, The evolution rate of the dynamic land-atmosphere coupling index at time t reflects the instantaneous change rate of D-LACI(t); A LACI (t) represents the evolution acceleration of the dynamic land-atmosphere coupling index at time t, reflecting the changing trend of the evolution rate itself; d is the differential symbol; d / dt and d² / dt² are the first and second order differential operators with respect to time t, respectively; when >0 and When the value is greater than 0, it indicates that the land-atmosphere coupling intensity is deteriorating rapidly, and the system is about to transition from the "linear consumption period" to the "nonlinear abrupt change period" of stomata closure.

[0221] Step 3.2, extract the reversal phase transition characteristics of the canopy-air temperature difference (physiological verification);

[0222] A single mathematical index-based warning may produce false alarms due to instantaneous weather fluctuations (such as cloud cover). This step introduces the canopy temperature T obtained in step 1. c With air temperature T a Calculate the difference ΔT canopy =T c -T a Under normal water supply conditions, transpiration cooling plays a dominant role in plant growth, ΔT canopy Typically, these values ​​are negative (or fall within a very small positive range). When soil moisture stress is combined with a sudden surge in high temperatures, stomata are forced to close, transpiration drops precipitously, and the canopy begins to absorb sensible heat like "dry rock," leading to a significant increase in T0. c Quickly surpass T a .

[0223] The criteria for determining the physiological phase transition point are defined as follows:

[0224] The criteria for determining the physiological abnormal transition of canopy heat stress are defined as follows:

[0225] and ,

[0226] in, An empirical threshold for the abnormal rate of change of the temperature difference between the canopy and the air is set, which characterizes the increase rate of the temperature difference between the canopy and the air per unit time. When this value is exceeded, it is determined that the heat stress is intensified. The set safe temperature difference threshold for the canopy;

[0227] Step 3.3: Construct the dual verification trigger matrix for "critical mutation";

[0228] Based on the mathematical dynamics of step 3.1 and the physiological phase transition characteristics of step 3.2, the following three dynamic defense lines are identified:

[0229] State I (Safe Decoupling State): V LACI (t)≤0 or ALACI (t)≤0 or <0, plant transpiration and cooling are normal or the land-atmosphere coupling index does not show an accelerating upward trend, continue monitoring and do not trigger irrigation;

[0230] State II (Pre-mutation state / Advanced defense state): V LACI (t)>0 and A LACI (t)>0 and ≥0, but This triggers step 4 in advance to calculate the defensive basic irrigation volume;

[0231] State III (Disaster Outbreak / Forced Intervention State): V LACI (t)>0 and A LACI (t)>0, and Step 4 is immediately triggered to calculate the forced cooling irrigation volume including advection compensation, and a highest priority execution command is sent to the control terminal.

[0232] Step 4: Construct a land-atmosphere decoupled irrigation quantity solution model that includes a vertical-horizontal coupled advection heat compensation mechanism;

[0233] When step 3 triggers state II or state III, the system enters the calculation process for active defense irrigation. The core of step 4 lies in extending the estimation of irrigation water demand from the traditional one-dimensional vertical energy balance framework to a two-dimensional coupled solution framework that integrates vertical transpiration decoupled water demand with horizontal advection heat loss compensation, and applying soil hydraulic dynamic safety constraints. This allows for obtaining an irrigation volume that can truly block positive land-atmosphere feedback and is engineering-feasible under extreme hot and dry wind conditions. The entire solution process consists of four sub-steps:

[0234] Step 4.1, calculate the target latent heat flux gap ΔLE in the vertical direction. vertical ;

[0235] To achieve decoupling of land-atmosphere physical cooling, the canopy temperature must be forcibly pulled back to a safe threshold, from which the required target latent heat flux (LE) can be derived. target The physical approach to achieving this goal is to increase the latent heat flux of evaporation, thereby consuming more available energy and thus compressing the sensible heat flux and reducing the intensity of canopy heating to the atmosphere. Based on the surface energy balance:

[0236] ,

[0237] in, G(t) is the net radiation flux measured in step 1 at time t; G(t) is the soil heat flux measured in step 1 at time t. The maximum safe heat flux threshold is preset based on crop type, growth period, and historical calibration results. The physical meaning is: when the sensible heat flux does not exceed this value, the canopy temperature can be maintained within a safe range that keeps stomata open normally, and the crop will not trigger the stomatal closure lock-in effect under high-temperature stress. The calibration method for this threshold is as follows: select multiple years of heatwave-free weather data for the same growth period of the target crop, statistically analyze the sensible heat flux distribution corresponding to when the difference between canopy temperature and air temperature does not exceed the crop-specific threshold and stomatal conductance is maintained at a normal opening level, and take its 95th percentile value as... .

[0238] Based on this, calculate the current latent heat flux gap in the vertical direction. :

[0239] ,

[0240] in, The current actual latent heat flux after Bowen ratio correction in step 1.4; when When the temperature is >0, it indicates that the current transpiration cooling capacity is insufficient to pull the canopy temperature back to a safe range, and the system needs to replenish soil moisture through irrigation to increase the latent heat output of transpiration; when When this occurs, it indicates that the current actual latent heat flux has met or exceeded the target latent heat flux requirement, and no vertical irrigation compensation is needed. .

[0241] Step 4.2, calculate the horizontal advection dry hot wind energy compensation term H. adv ;

[0242] Under extreme hot and dry wind conditions, sensible heat from the upwind, dry, and hot surface continuously enters the irrigation area via horizontal advection. This energy is not reflected in the (Rn − G) term of the vertical energy balance, but it additionally raises the canopy temperature. This must be offset by increasing the latent heat of transpiration; otherwise, the irrigation amount will be systematically low. During drought and heat waves, the gradient data from the "windward boundary monitoring station" and the "core station in the heart of the farmland" in step 1 are extracted to calculate the horizontal advection heat flux transported by hot and dry winds into the farmland. :

[0243] ,

[0244] in, air density; The specific heat capacity of air at constant pressure is taken as 1005 J / (kg·K); (t) represents the wind speed at the windward side boundary at time t. (t) and These represent the air temperatures at the windward boundary and the inland core at time t, respectively. The length of the cold island effect wind zone. (t) represents the characteristic height of the near-surface atmospheric mixing layer at time t;

[0245] Step 4.3: Solve for the vertical-horizontal coupled defensive total latent heat requirement and theoretical irrigation water depth. ;

[0246] By combining the vertical latent heat gap with the horizontal advection compensation term, and based on the early warning state triggered in step 3.3, a defensive total latent heat demand model is constructed:

[0247] ,

[0248] in, (t) represents the total defensive latent heat demand at time t. is the advection energy absorption coefficient, which characterizes the actual offsetting ratio of irrigation evaporation to advection heat;

[0249] When state II is triggered, only the defensive basic irrigation amount is calculated, taking... =0;

[0250] When state III is triggered, the forced cooling irrigation amount including advection compensation is calculated, the advection compensation term is activated, and the value is taken. >0;

[0251] After obtaining the total latent heat requirement for defense, the latent heat of vaporization constant is used. Convert the total heat energy demand into the required theoretical irrigation water depth:

[0252] ,

[0253] in The preset irrigation intervention time window, The density of water; Let be the latent heat of vaporization of water, taken as 2.45 × 10⁻⁶ under normal temperature conditions. 6 J / kg.

[0254] Step 4.4: Apply soil hydraulic and dynamic safety constraints and output the final irrigation amount. ;

[0255] To prevent deep seepage or surface runoff caused by large-volume irrigation in a short period of time, infiltration constraints are imposed on the theoretical irrigation volume:

[0256] ,

[0257] in, For soil saturated hydraulic conductivity, saturated moisture content, (t) represents the average soil volumetric water content in the root zone at time t. For root depth, This represents the maximum infiltration rate at time t.

[0258] The final output command shows the amount of irrigation water. Choose the smaller value between theoretical demand and soil infiltration capacity:

[0259] ,

[0260] when (t) At that time, the soil has sufficient infiltration capacity, and the theoretical irrigation amount can be entirely applied through root zone irrigation. = .

[0261] when (t)> At this time, the soil infiltration capacity is insufficient to receive the entire theoretical irrigation volume within the specified time window. Therefore, the root zone irrigation volume is limited by... The difference (t) Convert to canopy spray or micro-mist spray dosage.

[0262] Step 5: Optimization and scheduling of non-uniform spatial rotation irrigation matrix based on micro-meteorological cold island footprint model;

[0263] During periods of combined drought and heatwave disasters, the total amount of regionally available water resources is limited. If the irrigation water depth obtained in step 4 is applied uniformly to all irrigation units... The total water demand of the system may exceed the pumping capacity of the water source or the regional quota limit. To address the optimization problem of irrigation spatial layout under limited water conditions, this step introduces the Gaussian plume diffusion theory, treating each irrigation unit as a source of cold air, such as... Figure 2 As shown, a cooling footprint model for the downwind region is established. With the goal of maximizing the overall cooling coverage effect and constrained by the total available water volume, the optimal non-uniform patchy spatial water distribution matrix is ​​solved. This allows some grids to achieve indirect cooling through the advection cooling effect of the upwind irrigation grids, thus maintaining the overall canopy temperature within a safe range even when water volume is insufficient for full coverage irrigation. The specific steps are as follows:

[0264] Step 5.1: Irrigation management gridding and real-time wind field vector mapping;

[0265] The target area is divided into N×M independent and controllable micro-irrigation grid units, where N and M represent the number of grid units in the horizontal and vertical directions of the target area, respectively, and both N and M are positive integers. The three-dimensional wind field data from the windward boundary station in step 1 is extracted in real time to obtain the current prevailing wind direction angle θ. w With wind speed U boundAnd map it to the grid coordinate system to determine the upwind starting boundary and downwind airflow path of the entire domain;

[0266] Step 5.2: Construct the cold air advection cooling footprint equation based on Gaussian plume diffusion theory.

[0267] like Figure 2 As shown, when irrigation is activated in a grid k, the cold, moist air generated by its evaporation will be transported downwind along the windward direction, while simultaneously undergoing turbulent diffusion in the crosswind direction. The effective cooling contribution of any irrigation grid k to its downwind grid (i, j) is given. The following footprint equation is used for calculation:

[0268] ,

[0269] in, The effective cooling contribution of irrigation grid k to the downwind grid (i, j); This represents the maximum theoretical evaporative cooling rate of the source grid. Distance in the downwind direction. This refers to the crosswind distance; This refers to the length of the cold air attenuation along the windward direction; σ is the lateral turbulent diffusion coefficient; exp is the natural exponential function;

[0270] Step 5.3: Solve for the optimal source and sink topology matrices under limited water volume;

[0271] (1) Establish the following optimization objective function:

[0272] ,

[0273] Where g represents the set of all grid cells within the target region; The set of grids selected to have irrigation enabled; k is... The irrigation grid index is in the table; η is the cooling effect diminishing adjustment coefficient, which characterizes the marginal diminishing rate of the cumulative cooling contribution at a single point.

[0274] (2) The constraints are:

[0275] ,

[0276] in, The irrigation water depth for grid k; Let k be the area of ​​grid k; This is the upper limit of the total water volume that the system can currently utilize, determined by the real-time availability of irrigation water sources. This constraint ensures that the total water consumption of all selected irrigation grids does not exceed the currently available water resources.

[0277] (3) Solve for optimization;

[0278] An optimization algorithm is used to solve the objective function in real time, outputting an optimal spatial water distribution matrix M_opt consisting of 0s and 1s, with dimensions N×M. In the matrix, a value of 1 indicates that irrigation for that grid is enabled, while a value of 0 indicates that the grid is disabled, relying on the advection cooling effect from the upwind irrigation grid for indirect cooling.

[0279] Step 5.4: Dynamically issue instructions for non-uniform patchy irrigation and spraying;

[0280] The optimal spatial water distribution matrix M opt This is interpreted as a low-level solenoid valve control command. Under drought and heatwave conditions, the system outputs a non-uniformly spaced irrigation topology map: each activated grid is configured according to the I calculated in step 4. final The system employs ultra-fine atomization for precise application, with the specific irrigation water depth and spray volume ratio determined based on the calculation results from step 4. During operation, the system continuously monitors the real-time wind direction angle θ returned by the station from step 1. w If a sudden change in wind direction is detected, that is, the difference in wind direction angle Δθ between two consecutive sampling times, then... w If the temperature exceeds 30°, the system will immediately trigger a recalculation process: return to step 5.1 to re-map the coordinates, update the cooling footprint in step 5.2 and the optimized topology in step 5.3 in sequence, and generate a new water distribution matrix M. opt And switch the irrigation matrix sequence to ensure that the cold island footprint always matches the real-time wind field.

[0281] Step 6: Dual closed-loop verification and dynamic termination mechanism based on stomatal physiological hysteresis and thermodynamic phase transition reversal;

[0282] After irrigation, the final determination of the canopy cooling effect depends not only on the degree of soil moisture replenishment but also on the inherent physiological lag in the reopening of stomata under extreme high-temperature stress. If irrigation is stopped immediately based solely on the soil moisture content meeting the standard, the stomata may not have yet resumed effective transpiration, and the canopy temperature may rise again. Therefore, this step first continuously tracks the derivative of the canopy cooling rate at a very high frequency. Based on this, a dual termination judgment matrix is ​​established, consisting of the environmental-level land-atmosphere coupling phase transition reversal condition and the physiological-level stomatal function recovery condition. A termination command is issued only when both conditions are met simultaneously. If all irrigation water has been applied but the stomatal physiological lag condition has not yet been met, a pulsed micro-mist compensation program is further triggered to reduce canopy transpiration resistance and promote stomatal reopening with a very small amount of atomized water until both conditions are met simultaneously, at which point the entire defense response is terminated.

[0283] Step 6.1: High-frequency tracking of the canopy cooling trajectory and recovery derivative;

[0284] After the irrigation command is executed, the cooling target grid selected in step 5 is locked, and the canopy and air temperature difference ΔT is continuously monitored at a sampling frequency of 1 minute. canopy Real-time calculation of canopy cooling rate:

[0285] ;

[0286] in, Let t be the canopy cooling rate at time t;

[0287] Step 6.2: Establish a dual phase transition reversal termination determination matrix based on physiological and environmental factors;

[0288] The defense response will only be allowed to be completely terminated if the following dual phase transition reversal criteria are met:

[0289] Condition A: The dynamic land-atmosphere coupling index calculated in step 2 falls back below the preset safety baseline, and the first derivative of the dynamic land-atmosphere coupling index... ≤0;

[0290] Condition B: Canopy temperature difference ΔT canopy The value changes from positive to a stable negative value and remains there for a period of time exceeding the preset stomatal physiological inertia time window.

[0291] Only when conditions A and B are met simultaneously is the land-atmosphere decoupling considered successful, and an order to stop irrigation or spraying is issued.

[0292] Step 6.3, Pulsed micromist intervention under the hysteresis dilemma;

[0293] If the irrigation water volume calculated in step 4 has been completed, but condition B in step 6.2 is not met, the pulsed micro-mist compensation program is triggered: surface drip irrigation or flood irrigation is suspended, and the spatial ultrasonic micro-mist or high-pressure micro-spray system is automatically started. Each time, micro-mist is released into the canopy space in a short pulse manner to increase local air humidity and reduce canopy transpiration resistance, thereby promoting the reopening of stomata until condition B is met.

[0294] Step 7: Adaptive updating and model optimization of post-disaster cold island footprint and advection compensation parameters;

[0295] After the drought and heat wave ended, the system entered the offline review and parameter self-learning phase: The actual wind field, total water consumption, and actual cooling area during this disaster were extracted and substituted into the micro-meteorological cold island footprint model in step 5 and the advection heat compensation formula in step 4. The lateral turbulent diffusion coefficient σ in this region was then corrected using the nonlinear least squares method. v The advection energy absorption coefficient ξ is used to update the corrected parameters to the system's underlying database, enabling the system to output more accurate water volume calculations and spatial irrigation matrices when facing the next complex disaster, thus achieving adaptive evolution of defense strategies for overwintering and across seasons.

[0296] This invention provides an active irrigation method for preventing combined drought and heat wave disasters based on a land-atmosphere decoupling mechanism. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A proactive irrigation method for mitigating combined drought and heat wave disasters based on a land-atmosphere decoupling mechanism, characterized in that, Includes the following steps: Step 1: Construct a multi-source sensing and gradient monitoring fusion system adapted to the land-atmosphere decoupling algorithm; Step 2: Construct a dynamic land-atmosphere coupling index based on hydraulic and thermal synergistic constraints; Step 3: Establish a disaster critical mutation early warning and active triggering mechanism based on the synergy of dual derivatives and physiological phase transition; Step 4: Construct a land-atmosphere decoupled irrigation quantity solution model that includes an advection thermal compensation mechanism; Step 5: Optimize and schedule the non-uniform spatial rotation irrigation matrix based on the micro-meteorological cold island footprint model; Step 6: Establish a dual closed-loop verification and dynamic termination mechanism based on pore hydraulic hysteresis and thermodynamic phase change reversal. Step 7: Implement adaptive updates of the post-disaster cold island footprint and advection compensation parameters.

2. The method according to claim 1, characterized in that, Step 1 includes: Step 1.1: Establish a monitoring network for the boundary and central micro-meteorological advection gradient; Based on the prevailing wind direction during historical drought and heat wave periods in the target area, a spatial gradient monitoring network is constructed, including: Windward Boundary Monitoring Base Station: Deployed at the windward edge of farmland to collect characteristic parameters of advection hot and dry winds in real time, including boundary air temperature T. bound Boundary wind speed U bound and relative humidity (RH) bound ; Core flux stations in the hinterland of farmland: Deployed downwind or in the central hinterland of farmland, these stations measure net radiation flux R in real time using an eddy covariance system. n Soil heat flux G, sensible heat flux H, and latent heat flux LE; simultaneously, the ambient air temperature T is collected. core Real-time acquisition of the horizontal air temperature difference ΔT between the windward boundary and the core of the farmland hinterland. horizontal =T bound -T core ; Step 1.2, deploy soil vertical profile sensors and crop canopy physiological monitoring equipment, including: Dynamic root zone soil sensing: Soil moisture sensors are buried in layers at four depths: 0~10cm, 10~30cm, 30~60cm, and 60~100cm, to continuously collect the volumetric water content of the first soil layer. , where l is the soil layer number, and l=1, 2, 3, 4 correspond to four depths: 0~10cm, 10~30cm, 30~60cm, and 60~100cm, respectively; Based on the collected data, the rate of change of soil moisture content at different depths over time was calculated. Where t is time and d represents the derivative; subsequently, based on the rate of decrease of the soil moisture content change rate in each layer, normalization is performed according to the following formula to determine the dynamic weight coefficient of each layer. : ， Among them, intermediate parameters ; When all layers When the sum is 0 or less than a preset threshold, a weighting coefficient is set. 1 / 4; and according to the weighting coefficient Weighted calculation of the overall soil volumetric water content θ in the root zone; High-frequency infrared canopy temperature array: Deploy non-contact infrared temperature sensors to continuously acquire crop canopy temperature T. c And obtain the ambient air temperature T in real time. a By controlling the canopy temperature T c Subtract air temperature T a Real-time calculation of the temperature difference ΔT between the canopy and the air canopy =T c -T a , will ΔT canopy As a core physiological indicator characterizing the stomatal thermal stress state of crops; Step 1.3 introduces an adaptive frequency conversion acquisition mechanism based on stress threshold; The following dual-modal acquisition strategy is adopted: Normal cruise mode: When the calculated saturated vapor pressure difference (VPD) is < 1.5 kPa and the relative available soil moisture (REW) is > 0.5, data is polled and stored at the normal frequency. High-frequency capture mode for heat wave surges: Once the rate of temperature rise is detected... Or, if the VPD exceeds the preset safety threshold, it will automatically trigger the high-frequency acquisition mode to record the flux data and canopy temperature in a high-frequency pulse manner. Step 1.4, Forced energy closure correction of flux data under extreme heat waves; On hot and arid underlying surfaces, the energy measured by the primitive eddy covariance system is usually not closed, i.e., R exists. n -G>H+LE, a Bowen ratio energy closure correction algorithm is introduced at the data fusion layer. This algorithm is based on the Bowen ratio before and after correction. Assuming that it remains unchanged, the effective energy (R) n -G) is forcibly allocated to sensible heat and latent heat to obtain the corrected sensible heat flux. and latent heat flux : ， ， in, .

3. The method according to claim 2, characterized in that, Step 2 includes: Step 2.1, obtain the state-driven variables; Calculate the relative available soil moisture at time t and normalized saturated vapor pressure difference VPD norm (t): ， ， in, The overall soil volumetric water content in the root zone, , The measured volumetric water content at time t for layer l. Field holding capacity Moisture content at the wilting point; The actual saturated water vapor pressure difference at time t; and These are the maximum and minimum saturated vapor pressure differences during the current crop growth period, respectively. when The evapotranspiration deficit at time t is calculated using the following formula. : ， when or When the value is less than the preset threshold, the smoothed value of the adjacent time period is used for calculation; in The actual latent heat flux after correction at time t; Calculate the soil moisture anomaly at time t : ； Step 2.2: Construct a nonlinear dynamic weighting function; Calculate the dynamic weight β(t) of soil moisture deficit at time t: ， in, As the basic weight of soil, The maximum weight under the drought limit state; The critical relative available water for inducing nonlinear stomatal closure; k1 is the soil drought sensitivity coefficient greater than 0, and e is the natural constant; Calculate the dynamic weights of atmospheric dry heat stress at time t : ， in, Here, k2 represents the atmospheric baseline weight, and k2 is a greater than 0 atmospheric drought sensitivity coefficient. The normalized saturated water vapor pressure difference at time t; Calculate the dynamic weight of sensible heat flux at time t Dynamic weighting of latent heat flux : ， ， in, and These are the initial calibration coefficients. for The upper limit of the atmospheric dry heat stress weight is taken when the maximum value is 1. , and It is a non-negative value; Step 2.3, dynamic weight normalization processing; Real-time normalization of dynamic weights: ， Where i is the weight category index. , , , , respectively, correspond to the dynamic weights of sensible heat flux, soil moisture deficit, latent heat flux and atmospheric dry heat stress; Represents the dynamic weight of the i-th class; These are the normalized weights; Step 2.4, introduce the stomatal physiological thermal memory penalty term Ω(t) at time t: ， in, The highest temperature on the i-th day before the current moment. This is the upper limit of the optimal temperature for crop growth; λ is the thermal memory sensitivity coefficient, and λ is the time decay constant; Step 2.5, calculate the dynamic land-atmosphere coupling index D-LACI(t) at time t: First, the temperature difference between the canopy and the air is normalized to obtain the normalized canopy temperature difference. : ， in, Let t be the temperature difference between the canopy and the air. and These represent the maximum and minimum temperature differences between the canopy and the air during the current crop growth period; ， in, , , and These are the sensible heat flux, soil moisture deficit, latent heat flux, and dynamic weights of atmospheric dry heat stress after normalization in step 2.

3.

4. The method according to claim 3, characterized in that, Step 3 includes: Step 3.1: Extract the time-series dynamic features of D-LACI; Based on the time series of the dynamic land-atmosphere coupling index obtained in step 2, the evolution rate and acceleration of the dynamic land-atmosphere coupling index are calculated in real time using the sliding window algorithm: ， ， in, The evolution rate of the dynamic land-atmosphere coupling index at time t; A LACI (t) represents the evolution acceleration of the dynamic land-atmosphere coupling index at time t; d / dt and d² / dt² are the first-order and second-order differential operators with respect to time t, respectively. Step 3.2: Extract the reverse phase transition characteristics of the canopy and air temperature difference; The criteria for determining the physiological abnormal transition of canopy heat stress are defined as follows: and , in, The set empirical threshold for the rate of abnormal change in the temperature difference between the canopy and the air; The set safe temperature difference threshold for the canopy; Step 3.3: Construct the critical mutation dual verification trigger matrix; The following three dynamic defense lines are defined: State I: V LACI (t)≤0 or A LACI (t)≤0 or <0, plant transpiration and cooling are normal or the land-atmosphere coupling index does not show an accelerating upward trend, continue monitoring and do not trigger irrigation; State II: V LACI (t)>0 and A LACI (t)>0 and ≥0, but or This triggers step 4 in advance to calculate the defensive basic irrigation volume; State III: V LACI (t)>0 and A LACI (t)>0, and and Step 4 is immediately triggered to calculate the forced cooling irrigation volume including advection compensation, and a highest priority execution command is sent to the control terminal.

5. The method according to claim 4, characterized in that, Step 4 includes: Step 4.1, calculate the target latent heat flux gap ΔLE in the vertical direction. vertical ; Calculate the target latent heat flux : ， in, The maximum safe heat flux threshold is preset based on crop type, growth period, and historical calibration results; Calculate the current vertical latent heat gap : ， when When the temperature is >0, it indicates that the current transpiration cooling capacity is insufficient to pull the canopy temperature back to a safe range, and irrigation is needed to replenish soil moisture to increase the latent heat output of transpiration; when When this occurs, it indicates that the current actual latent heat flux has met or exceeded the target latent heat flux requirement, and no vertical irrigation compensation is needed. ; Step 4.2, calculate the horizontal advection dry hot wind energy compensation term at time t. : ， in, air density, The specific heat capacity of air at constant pressure. (t) represents the wind speed at the windward side boundary at time t. (t) and These represent the air temperatures at the windward boundary and the inland core at time t, respectively. The length of the cold island effect wind zone. (t) represents the characteristic height of the near-surface atmospheric mixing layer at time t; Step 4.3: Solve for the vertical-horizontal coupled defensive total latent heat requirement and theoretical irrigation water depth. ; By combining the vertical gap with the horizontal compensation term, and based on the early warning status triggered in step 3.3, a defensive total latent heat demand model is constructed: ， in, (t) represents the total defensive latent heat demand at time t. is the advection energy absorption coefficient, which characterizes the actual offsetting ratio of irrigation evaporation to advection heat; When state II is triggered, only the defensive basic irrigation amount is calculated, taking... =0; When state III is triggered, the forced cooling irrigation amount including advection compensation is calculated, the advection compensation term is activated, and the value is taken. >0; Using the latent heat of vaporization The total heat energy demand is converted into the required theoretical irrigation water depth: ， in The preset irrigation intervention time window, The density of water; Step 4.4: Apply soil hydraulic and dynamic safety constraints and output the final irrigation amount. ; Infiltration constraints are applied to the theoretical irrigation volume: ， in, For soil saturated hydraulic conductivity, saturated moisture content, (t) represents the average soil volumetric water content in the root zone at time t. For root depth, This represents the maximum infiltration rate at time t. The final output command shows the amount of irrigation water. for: ， if (t)> The difference (t)- Convert to canopy spray or micro-mist spray dosage.

6. The method according to claim 5, characterized in that, Step 5 includes: Step 5.1: Irrigation management gridding and real-time wind field vector mapping; The target area is divided into N×M independent and controllable micro-irrigation grid units, where N and M represent the number of grid units in the horizontal and vertical directions of the target area, respectively, and both N and M are positive integers. The three-dimensional wind field data from the windward boundary station in step 1 is extracted in real time to obtain the current prevailing wind direction angle θ. w With wind speed U bound And map it to the grid coordinate system to determine the upwind starting boundary and downwind airflow path of the entire domain; Step 5.2, based on Gaussian plume diffusion theory, construct the cold air advection cooling footprint equation: ， in, The effective cooling contribution of irrigation grid k to the downwind grid (i, j); This represents the maximum theoretical evaporative cooling rate of the source grid. Distance in the downwind direction. This refers to the crosswind distance; This refers to the length of the cold air attenuation along the windward direction; σ is the lateral turbulent diffusion coefficient; exp is the natural exponential function; Step 5.3: Solve for the optimal source and sink topology matrices under limited water volume; Establish the optimization objective function: ， Where g represents the set of all grid cells within the target region; The set of grids selected to have irrigation enabled; k is... The irrigation grid index in the text; η is the diminishing effect adjustment coefficient for cooling, which characterizes the marginal deceleration rate of the cumulative cooling contribution at a single point; The constraints are: ， in, The irrigation water depth for grid k; Let k be the area of ​​grid k; This represents the maximum total water volume that the system can currently access. The objective function is solved in real time using an optimization algorithm, and an optimal spatial water distribution matrix M consisting of 0s and 1s is output. opt 1 represents grid irrigation is enabled, and 0 represents it is disabled, relying on advection cooling; Step 5.4: Dynamically issue instructions for non-uniform patchy irrigation and spraying; The optimal spatial water distribution matrix M opt The analysis is based on the control commands of the underlying solenoid valves. During periods of strong winds and heat waves, the system outputs a non-uniform interval irrigation topology map, with each activated grid following the I calculated in step 4. final And with ultra-fine atomization ratio for precise operation, if the wind direction changes suddenly during the operation, i.e. Δθ w If the temperature exceeds 30°, the system will immediately return to step 5.1 to recalculate the dynamic cold island footprint and switch the irrigation matrix sequence.

7. The method according to claim 6, characterized in that, Step 6 includes: Step 6.1: High-frequency tracking of the canopy cooling trajectory and recovery derivative; After the irrigation command is executed, the cooling target grid selected in step 5 is locked, and the temperature difference ΔT between the canopy and the air is continuously monitored at extremely high frequency. canopy Real-time calculation of canopy cooling rate: ； in, Let t be the canopy cooling rate at time t; Step 6.2: Establish a dual phase transition reversal termination determination matrix based on physiological and environmental factors; The defense response will only be allowed to be completely terminated if the following dual phase transition reversal criteria are met: Condition A: The dynamic land-atmosphere coupling index calculated in step 2 falls back below the preset safety baseline, and the first derivative of the dynamic land-atmosphere coupling index... ≤0; Condition B: Canopy temperature difference ΔT canopy The value changes from positive to a stable negative value and remains there for a period of time exceeding the preset stomatal physiological inertia time window. Only when conditions A and B are met simultaneously is it determined that the land-atmosphere re-decoupling is successful, and an instruction to stop irrigation or stop spraying is issued. Step 6.3, Pulsed micromist intervention under the hysteresis dilemma; If the irrigation water volume calculated in step 4 has been completed, but condition B in step 6.2 is not met, the pulsed micro-mist compensation program is triggered: surface drip irrigation or flood irrigation is suspended, and the spatial ultrasonic micro-mist or high-pressure micro-spray system is automatically started. Each time, micro-mist is released into the canopy space in a short pulse manner to increase local air humidity and reduce canopy transpiration resistance, thereby promoting the reopening of stomata until condition B is met.

8. The method according to claim 7, characterized in that, Step 7 includes: After the drought and heat wave process ends, the offline review and parameter self-learning phase begins. Extract the actual wind field, actual total water consumption, and actual cooling area during this round of disasters, and substitute them into the cold air advection cooling footprint equation in step 5 and the formula in step 4.2; The lateral turbulent diffusion coefficient σ was analyzed using the nonlinear least squares method based on measured data from this disaster event. v The advection energy absorption coefficient ξ is inverted and corrected, and the corrected parameters are updated to the system parameter database.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, It stores a computer program or instructions that, when run on a computer, perform the steps of the method as described in any one of claims 1 to 8.

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