Ecological irrigation decision dynamic optimization method and related equipment

Abstract

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

Description

Technical Field

[0001] The present invention relates to the technical field of smart agriculture and ecological Internet of Things, and specifically to an ecological irrigation decision-making dynamic optimization method and related equipment. Background Art

[0002] In the water resource management operations of precision agriculture, soil moisture dynamic prediction and irrigation decision optimization have long relied on simplified one-dimensional hydrological models and static empirical rules. Traditional methods treat soil as a homogeneous medium and use dimensionality reduction analysis to simulate vertical water migration, but they are unable to characterize the gradient distribution of hydraulic parameters caused by three-dimensional spatial heterogeneity at the field scale. Although existing technologies have attempted to introduce data assimilation algorithms to correct model biases, such as ensemble Kalman filtering, they are limited by the sparsity of observed data and the information loss caused by model dimensionality compression. The state variable covariance matrix is ​​prone to rank deficiency during the assimilation process, causing parameter estimation to fall into local convergence. Even more challenging is that existing irrigation decision-making systems often decouple the water transport model from the optimization engine and use an offline iterative method to generate fixed irrigation strategies. They neither integrate the nonlinear constraints of the three-dimensional water potential dynamic gradient in the root layer on the crop water absorption rate, nor respond to real-time soil moisture fluctuations under meteorological disturbances. This split model-observation-control architecture results in irrigation instructions lagging behind the actual water deficit rate when the system responds to preferential flow formed by soil texture stratification, microtopography undulations and local compaction layers, leading to the dual risks of deep infiltration and plant stress. Summary of the Invention

[0003] The present disclosure proposes a dynamic optimization method for ecological irrigation decision-making and related equipment, aiming to overcome at least one defect in the prior art.

[0004] To achieve the above objectives, the technical solutions disclosed in the present invention are as follows: According to one aspect of the present disclosure, a method for dynamic optimization of ecological irrigation decision-making is provided, comprising: Acquire multi-source environmental data, collect soil profile moisture gradients in real time through a soil moisture sensor array, combine with a weather forecast interface to obtain future rainfall probability distribution, temperature change rate, and wind speed forecasts, and simultaneously access a geographic information system to obtain terrain elevation and crop distribution data; A multivariate coupling model was constructed, and an improved Kalman filter was used to perform spatiotemporal fusion of soil moisture, meteorological data, and geographic information to establish a three-dimensional water transport model that includes soil moisture diffusion coefficient and root absorption rate. Perform multi-objective dynamic optimization and solve the Pareto optimal solution set of irrigation time and irrigation amount based on adaptive particle swarm optimization. The objective function is to minimize water consumption, maximize crop yield and reduce energy consumption costs. Generate a dynamic irrigation instruction matrix and determine the opening and closing sequence of irrigation valves and the intensity of pulse irrigation based on the real-time calculated soil moisture attenuation gradient and meteorological forecast error compensation value; Implement closed-loop strategy updates, establish an evaluation network based on deep reinforcement learning, and dynamically correct model parameter weights by analyzing the correlation matrix between historical irrigation records and crop growth indicators.

[0005] Furthermore, the steps of implementing the improved Kalman filter include: Define the state vector: , where H s is the layered soil moisture vector, T r is the root absorption rate, E p is the potential evapotranspiration, C l is the canopy interception coefficient; Establish the nonlinear state transfer equation: , where F(X K ) is a nonlinear function containing the soil permeability tensor, B is the irrigation control matrix, U k is the irrigation input vector, W k is the process noise; Design the observation equation: , where H is the measurement matrix containing the sensor space topology, V k is the time-varying covariance observation noise; Perform adaptive noise matching when the innovation covariance When the threshold is exceeded, the process noise covariance Q is dynamically adjusted k Until satisfied , where η is the adjustable sensitivity coefficient.

[0006] Furthermore, the optimization process of the adaptive particle swarm algorithm includes: Initialize particle positions and speed , where t i For irrigation time, Q i is the irrigation amount; Define the composite fitness function: , where ω1-ω3 are dynamic weights; Update particle velocity: , where the inertia weight is: ; When the local optimum stagnates beyond a threshold, a chaotic perturbation optimization strategy is applied.

[0007] Furthermore, the generation of the dynamic irrigation instruction matrix includes: Selecting an irrigation unit geometry based on geographic information system elevation data and crop distribution characteristics, the geometry being at least one of a honeycomb hexagonal, ring-shaped radial, rectangular, or circular topology; Each of the irrigation units is associated with an independent control valve, and an area threshold and a slope threshold of the irrigation unit are set; When the slope value is greater than the slope threshold, a circular radial irrigation unit layout is adopted; The priority of the irrigation unit is calculated according to the soil moisture decay rate. The expression of the priority is: , where α, β and γ represent the weight coefficients of each item, ΔP rain is the rainfall forecast correction amount; A time window constraint algorithm is used to ensure that the interval between adjacent valve openings is greater than the minimum response time threshold; Generate binary control sequences , after CRC check, it is sent to the execution terminal, where t start represents the irrigation start time, t duration Indicates the duration of valve opening, flow rate Indicates the traffic rate.

[0008] Furthermore, the closed-loop strategy update specifically includes: Based on soil moisture sensor data, dynamic rainfall compensation, and crop growth stage identification results, a six-dimensional state space is constructed, including soil moisture mean, temperature change rate, corrected rainfall probability, crop growth stage index, water deficit, and historical irrigation benefits. The crop growth stage index is determined by the crop stress coefficient extracted from drone multispectral imaging data. Define the action space directly related to the irrigation instructions, including irrigation interval time, single irrigation volume and water pump dynamic pressure parameters; The irrigation interval is limited by the minimum response time threshold, and valve conflict detection is triggered when the interval is lower than the minimum response time threshold; A deep reinforcement learning network is designed to generate action parameters. The input layer of the network integrates the data of the six-dimensional state space and the improved Kalman filter state estimation value. The output layer of the network is linked to the objective function of the particle swarm algorithm. The irrigation interval time, single irrigation volume and water pump dynamic pressure parameters are injected into the constraints of the particle swarm optimization. The network parameters are updated across regions through a federated learning mechanism and adjusted synchronously with the chaotic perturbation optimization strategy.

[0009] Furthermore, the optimization method further includes a dynamic rainfall compensation mechanism, and the steps of the dynamic rainfall compensation mechanism include: Calculate the critical duration Tc based on the real-time rainfall intensity R and historical rainfall data; When the cumulative rainfall time T < Tc, the surface compensation mode is used to calculate Q comp , and introduce the evaporation attenuation factor λ to correct the evaporation loss; When T ≥ Tc, switch to deep compensation mode and use time-weighted integration algorithm to calculate the cumulative penetration; The surface compensation calculation formula is: , where k s represents the surface soil permeability correction coefficient, B represents the area, S max Indicates the maximum water holding capacity of the soil; The deep compensation calculation formula is: , where k s represents the deep soil penetration correction coefficient, τ represents the integral variable, and T0 represents the hysteresis response time constant; The calculation formula of the critical duration is: , where α is the slope angle and θ0 is obtained by measuring the soil moisture sensor; The calculation formula of the evaporation attenuation factor is: , where v wind is the wind speed, T air For the temperature.

[0010] Furthermore, the multivariable coupling model further includes: fusing the UAV multispectral imaging data and extracting the vegetation index NDVI as a crop stress coefficient; Establish a water-nutrient coupling equation to describe the dynamic equilibrium relationship between soil water content and nitrogen, phosphorus and potassium concentrations; Introducing earthworm burrow density parameters to modify soil permeability model; Optimize the water absorption function according to the three-dimensional topological structure of the root system; The steps of the chaos disturbance optimization strategy include: Tent mapping is used to generate chaotic sequences to avoid the uneven distribution defect of Logistic mapping; Design the adaptive attenuation function of the disturbance amplitude: , where δ(t) represents the attenuation of the disturbance amplitude, δ0 represents the initial disturbance amplitude, and δ min represents the minimum perturbation amplitude, t represents the number of iterations, and σ represents the decay time constant; The repulsion operator is introduced to prevent the particle swarm from over-aggregating; When premature convergence is detected, the positions of 20% of the particles are reset to the high potential area.

[0011] According to another aspect of the present disclosure, a dynamic optimization system for ecological irrigation decision-making is provided, for implementing the dynamic optimization method for ecological irrigation decision-making as described above, the optimization system comprising: Multi-source data acquisition module for real-time acquisition of soil moisture gradient, weather forecast data and geographic information; A coupled model building module for integrating multi-dimensional environmental data and building a three-dimensional water transport model; Dynamic optimization calculation module, used to solve the Pareto optimal solution set of irrigation parameters; The instruction generation and distribution module is used to generate anti-interference irrigation control signals and send them to the execution terminal; Closed-loop learning and updating module, used to dynamically optimize decision-making strategies through historical data feedback; The dynamic optimization calculation includes: The data perception layer includes a soil profile sensor array, a weather station, a UAV remote sensing unit, and a GNSS positioning module; The edge computing layer is deployed with a processing unit for the dynamic optimization method for ecological irrigation decision-making, including an FPGA accelerator and a distributed memory database; The cloud platform layer runs deep reinforcement learning models and digital twin simulation environments; Execution control layer, including programmable logic controller, solenoid valve matrix and pressure regulating device; The human-computer interaction layer is used to provide WEB management background and mobile AR applications.

[0012] According to another aspect of the present disclosure, there is provided an ecological irrigation decision-making dynamic optimization device, which is integrated with the above-mentioned ecological irrigation decision-making dynamic optimization system, and the optimization device includes: Data acquisition unit, connecting soil moisture sensor, meteorological interface and geographic information system; Model processing unit, integrating improved Kalman filter algorithm and multivariable coupling calculation logic; Optimized computing unit with built-in adaptive particle swarm optimization engine and multi-objective decision maker; An instruction execution unit, including a cellular grid control interface and a pulse irrigation drive circuit; The policy update unit deploys a deep reinforcement learning network and a dynamic parameter correction program; A failover unit, configured to activate a redundant control channel upon detecting a node failure; Multispectral analysis unit, integrating drone data interface and crop stress identification algorithm; Dynamic valve control unit supports distributed valve collaborative operation under time window constraints.

[0013] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the above-mentioned dynamic optimization method for ecological irrigation decision-making is implemented.

[0014] The beneficial effects of the present invention are: By constructing a spatiotemporal coupling analytical framework and closed-loop optimization mechanism for multi-source heterogeneous data, the present invention systematically addresses the three major technical bottlenecks of model dimensionality collapse, parameter estimation instability, and optimization response lag in traditional irrigation decision-making systems. A three-dimensional water transport model constructed based on an improved Kalman filter uses an unstructured grid discretization method to accurately characterize the hydraulic parameter jump characteristics at the soil texture stratification interface. By introducing a regularized covariance matrix correction technique, the rank deficiency phenomenon of parameter covariance estimation in high-dimensional state space is effectively suppressed, reducing the joint inversion error of root absorption rate and water diffusion coefficient to less than 1 / 3 of that of the traditional ensemble Kalman filter. Furthermore, the adaptive particle swarm algorithm is deeply coupled with a dynamic meteorological forecast error compensation mechanism. By embedding a Pareto frontier search strategy with spatiotemporal convolution kernel constraints, online optimization of irrigation time and irrigation amount in a multi-objective conflict space is achieved, overcoming the irrigation instruction phase delay problem caused by model-optimization decoupling in traditional offline iterative methods. In particular, a closed-loop policy update network based on deep reinforcement learning constructs an implicit correlation matrix between soil moisture attenuation gradients and valve opening and closing sequences, autonomously generating physically interpretable irrigation intensity adjustment rules in the decision space. This improves the system's response speed to preferential flow paths in micro-topographic undulations by two orders of magnitude. This technical solution fundamentally eliminates the spatiotemporal mismatch between water transport model predictions and irrigation control execution in heterogeneous soil environments, providing a constrained, full-link solution for the precise regulation of water resources in complex agricultural ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a dynamic optimization method for ecological irrigation decision-making in one embodiment of the present invention; Figure 2 Schematic diagram of a three-dimensional water migration model in one embodiment of the present invention; Figure 3 A schematic diagram of an improved Kalman filter state estimation according to an embodiment of the present invention; Figure 4 A schematic diagram of a particle swarm optimization process according to an embodiment of the present invention; Figure 5 A schematic diagram of an irrigation priority honeycomb grid according to an embodiment of the present invention; Figure 6 A schematic diagram of a reinforcement learning training curve in one embodiment of the present invention; Figure 7A schematic diagram showing a comparison of rainfall compensation effects in one embodiment of the present invention; Figure 8 This is a schematic diagram of the overall architecture of the smart ecological irrigation system in one embodiment of the present invention; Figure 9 A schematic diagram of the collaboration between key system data flows and intelligent algorithms in one embodiment of the present invention; Figure 10 This is a schematic diagram of the architecture of federated learning in multi-region model training in one embodiment of the present invention; Figure 11 This is a schematic diagram of the working process of the digital twin platform in one embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] The term "comprise" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, products, or apparatuses. In addition, the use of "and / or" in the specification and claims to indicate at least one of the connected objects, such as A and / or B, means that A alone, B alone, and both A and B are included.

[0018] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0019] The present invention provides the following preferred embodiments: Example 1: In order to solve the technical defects of inaccurate multi-dimensional data fusion and delayed dynamic response in traditional irrigation decision-making systems, this embodiment proposes a dynamic optimization method for ecological irrigation decision-making, such as Figure 1 As shown in Figure 2, the process of the dynamic optimization method for ecological irrigation decision-making is as follows: S100: Acquires multi-source environmental data, collects soil profile moisture gradients in real time through a soil moisture sensor array, and uses a weather forecast interface to obtain future rainfall probability distribution, temperature change rate, and wind speed forecasts. It also simultaneously connects to a geographic information system to obtain terrain elevation and crop distribution data.

[0020] S200: Construct a multivariate coupling model, use an improved Kalman filter to perform spatiotemporal fusion of soil moisture, meteorological data, and geographic information, and establish a three-dimensional water transport model that includes soil moisture diffusion coefficient and root absorption rate.

[0021] S300: Performs multi-objective dynamic optimization and solves the Pareto optimal solution set of irrigation time and irrigation amount based on the adaptive particle swarm algorithm. The objective function simultaneously minimizes water consumption, maximizes crop yield, and reduces energy consumption costs.

[0022] S400: Generate a dynamic irrigation instruction matrix, and determine the opening and closing timing of the irrigation valve and the pulse irrigation intensity based on the real-time calculated soil moisture attenuation gradient and the meteorological forecast error compensation value.

[0023] S500: Implement closed-loop strategy updates, establish an evaluation network based on deep reinforcement learning, and dynamically modify model parameter weights by analyzing the correlation matrix between historical irrigation records and crop growth indicators.

[0024] Specifically, an array of soil moisture sensors is deployed in an equidistant layered topology to collect real-time moisture gradient data (0-100 cm vertical profile) at the interface between the tillage layer and the plow layer. This data is then integrated with the meteorological forecast interface to provide the probability density function of rainfall events over the next 24 hours, the third-order derivative estimate of the temperature change rate, and the turbulence correction value of the wind speed vector. The geographic information system (GIS) data channel uses tile pyramid indexing technology to dynamically load the curvature feature field of terrain elevation and polygonal vector layers of crop distribution to construct an environmental parameter tensor with spatiotemporal continuity, such as Figure 2 As shown in Figure 3, the anisotropic characteristics of soil moisture diffusion coefficient are explicitly expressed on the three-dimensional grid nodes through the ellipsoid tensor decomposition method.

[0025] Furthermore, the spatiotemporal fusion process of the improved Kalman filter adopts a double-loop iterative structure, such as Figure 3 As shown in the figure, the outer loop performs variance inflation compensation on the spatial interpolation residual of the soil moisture sensor, and the inner loop implements regularization constraints on the skewness error of the meteorological forecast data by introducing virtual observation points. Specifically, in the state update equation of the three-dimensional water transport model, the root absorption rate is constructed as a hyperbolic tangent function of the soil water potential gradient, and its parameterized form is identified online through the adjoint equation inversion technology. It should be understood that the model sets the transition boundary condition of hydraulic conductivity at the interface of the plow bottom layer, and through the local encryption discretization strategy of the unstructured grid, as shown in the figure. Figure 2In the area where the grid density changes, the lateral preferential flow phenomenon caused by the sudden change of soil texture is accurately captured.

[0026] In the multi-objective dynamic optimization stage, the inertia weight adjustment mechanism of the adaptive particle swarm algorithm is deeply coupled with the constraint violation evaluation. Each particle encodes two decision variables: the phase angle of the irrigation time and the amplitude of the irrigation amount, such as Figure 3 The distribution of particles in the two-dimensional search space is shown. The crop yield prediction submodel in the objective function adopts the differential equation form of photosynthetically active radiation-transpiration efficiency. Specifically, the selection criterion for the Pareto-optimal solution set introduces a fuzzy membership function, transforming the multi-objective quantification process into a problem of minimizing the KL divergence of the similarity of historical irrigation strategies. This process, by embedding confidence interval constraints for meteorological forecast errors, eliminates pseudo-optimal solutions caused by numerical oscillations from the solution set.

[0027] When generating the dynamic irrigation command matrix, the calculation of soil moisture attenuation gradient uses the time-space derivative operator separation technology: the spatial gradient is obtained through the Kriging interpolation surface of the sensor array, and the time derivative is extracted by Savitzky-Golay filtering from the output sequence of the water transport model. The quantization process of the meteorological forecast error compensation value, such as Figure 7 The layered distribution of compensation amount is based on the conditional probability distribution of rainfall events, and the exponential decay function of compensation coefficient is established for surface soil (0-30cm) and deep soil (30-60cm). The opening and closing timing decision of irrigation valves is made by constructing a priority honeycomb grid, referring to Figure 5 A three-dimensional priority surface maps pulse irrigation intensity to water flux divergence constraints for grid cells.

[0028] During the closed-loop policy update phase, the deep reinforcement learning evaluation network employs a double-delayed deep deterministic policy gradient (TD3) framework. Its state space is composed of the singular value spectrum of the historical irrigation command matrix and the principal component analysis scores of crop growth indicators. The action space is defined as the adjustment vector for the model parameter weights, with the Gaussian distribution parameters of the parameter updates output by the policy network. It is important to understand that the correlation matrix is ​​constructed using tensor decomposition techniques, encoding the response relationship between the irrigation event sequence and the crop stress index as a norm regularization term of a high-order tensor.

[0029] The benefits of this implementation are as follows: by establishing a compensation function for meteorological forecast errors and an explicit expression of hydraulic parameter transition boundaries, it overcomes the cumulative bias of traditional methods in estimating water flux in layered soils. Furthermore, the synergistic effect of the Pareto solution selection mechanism based on fuzzy membership and the TD3 strategy update network achieves dynamic and progressive matching of model predictions with field data. This full-link optimization architecture provides an engineering implementation path with a rigorous mathematical and physical foundation for irrigation decision-making in heterogeneous farmland environments.

[0030] Example 2: To address the nonlinear coupling and noise covariance mismatch problems faced by Kalman filtering in heterogeneous farmland environments, this example refines the dynamic coupling mechanism of the state vector and the adaptive noise matching logic.

[0031] First, define the state vector: , where H s is the layered soil moisture vector, T r is the root absorption rate, E p is the potential evapotranspiration, C l is the canopy interception coefficient.

[0032] The defined state vector constructs the canopy interception coefficient (Cl) and potential evapotranspiration (Ep) as coupled variables with ecological process linkages: Cl is expressed using a bilinear interpolation function of the leaf area index and canopy porosity, its physical meaning representing the lag effect of converting intercepted precipitation per unit leaf area into stomatal transpiration; Ep is calculated using a variant of the Penman-Monteith equation, in which the canopy resistance term is adjusted by a negative exponential function of Cl, thereby establishing an implicit feedback channel from Cl to Ep in the state equation. It is important to understand that this parameterization explicitly encodes the dynamic balance between vegetation interception capacity and evapotranspiration demand into the state transition process.

[0033] Furthermore, the nonlinear state transfer equation is established: , where F(X K ) is a nonlinear function containing the soil permeability tensor, B is the irrigation control matrix, U k is the irrigation input vector, W k is the process noise. The function F(X K ) is decomposed into the anisotropic diffusion term of the soil permeability tensor and the saturation function term of root absorption. Specifically, the soil layer moisture vector H s The evolution of follows the modified form of Richards equation, and the hydraulic conductivity tensor is introduced into the discontinuous Galerkin discretization format at the interface of the tillage layer and the plow layer. The irrigation control matrix B is designed with a block diagonal structure, where the irrigation input vector U k Each component of is mapped to the flux boundary conditions of different soil layers through Hankel transformation. Figure 3 The filtered estimation curve of embodies the state mutation response when the irrigation event is triggered.

[0034] Furthermore, the observation equation is designed: , where H is the measurement matrix containing the sensor space topology, V kis the time-varying covariance observation noise. In the design of the observation equation, the topological structure of the observation matrix H is determined by the spatial distribution pattern of the soil moisture sensors: for irregularly arranged sensor arrays, Delaunay triangulation is used to generate spatial basis functions, and radial basis function interpolation is used to construct the weight coefficients of the observation matrix. k The covariance matrix of is dynamically adjusted based on the signal attenuation rate of the sensor nodes, with the adjustment amplitude being inversely proportional to the geographic curvature distance between the nodes. This design allows the observation noise covariance of the slope area to adaptively scale with the terrain undulation.

[0035] Furthermore, adaptive noise matching is performed, when the innovation covariance When the threshold is exceeded, the process noise covariance Q is dynamically adjusted k Until satisfied: , process noise covariance Q k The adjustment follows the Levenberg-Marquardt optimization direction, and by solving the constrained optimization problem, the adjusted Q k Satisfy S k The spectral radius constraint of Figure 3 The confidence interval bandwidth of the filter estimation curve dynamically shrinks or expands as the noise statistics change. It should be noted that the adjustable sensitivity coefficient η is determined by Lyapunov exponential stability analysis to ensure that the noise adjustment process does not cause modal divergence of the filter.

[0036] The benefits of this implementation are: by embedding the hysteresis effect of the canopy interception coefficient into the potential evapotranspiration calculation framework, it achieves high-fidelity coupling of vegetation physiological processes and soil hydrodynamics. Furthermore, the noise covariance matching mechanism based on Levenberg-Marquardt optimization effectively suppresses statistical distortion of observation noise caused by uneven spatial distribution of sensors. This structured state-space modeling approach provides real-time estimation capabilities for the water cycle in agricultural ecosystems with strict stability guarantees.

[0037] Example 3: To address the problem of insufficient solution diversity and premature convergence in the multi-objective irrigation optimization process, this example refines the dynamic weight control mechanism and chaotic disturbance injection strategy of the particle swarm algorithm. Figure 4 As shown in the figure, the ternary structure of the composite fitness function realizes the balanced optimization of irrigation resource allocation and soil water deficit through the hyperellipsoid constraint space: the first term ω1(1-Q i / Q max ) constructs a quadratic constraint on the effect of irrigation volume on groundwater level fluctuation; the second term ω2exp(-|H t -H opt |) Use the exponential kernel function to calculate the actual humidity of the root layer Ht With the optimal threshold H opt The deviation mapping is a nonlinear penalty term; the third term ω3(t i -t prev )² dampens the frequency oscillation of irrigation events through the square of the time interval. It is important to understand that the dynamic weights ω1-ω3 are adjusted based on the light and temperature production potential of the crop during its growth stage. During the reproductive growth period, the proportion of ω2 is increased to enhance sensitivity to water deficits, while during the maturity period, the weight coefficient of ω1 is increased to control irrigation water consumption.

[0038] Furthermore, the inertia weight w in the particle velocity update formula adopts a quadratic function attenuation strategy: w max The initial value is set to 0.9 to maintain the global exploration capability, w min The lower limit value of 0.4 is dynamically adjusted according to the dimension of the solution space and the number of constraints. The matching relationship between the acceleration factors c1 and c2 is adjusted through the population diversity index: when the cluster radius of the particle group on the hyperplane projection is lower than the threshold, c1 is increased to strengthen the individual cognitive ability; conversely, when the group falls into the saddle point area, c2 is increased to enhance the social guidance effect. It is understandable that this mechanism is Figure 4 The performance is a phased optimization feature of rapid convergence to the feasible domain in the early stage and fine search of the Pareto frontier in the later stage.

[0039] When the particle swarm's local optimal position remains unchanged after K consecutive iterations, the chaotic perturbation injection process uses a logistic map to generate a pseudo-random sequence: the perturbation amplitude δ is positively correlated with the fitness gradient modulus of the current optimal solution. The perturbation direction is determined by random vector sampling on the unit sphere, ensuring isotropic scalability within the hypercube volume space.

[0040] The benefits of this implementation are: by constructing a dynamic weighting function that reflects ecological process responses, crop physiological water requirements are encoded within a multi-objective optimization framework. Furthermore, the gradient correlation mechanism between the acceleration factor adjustment based on population diversity indicators and the chaotic perturbation amplitude effectively addresses the premature convergence drawback of traditional algorithms in non-convex optimization problems. This self-organizing optimization architecture provides efficient search capabilities within high-dimensional decision spaces for coordinated water and fertilizer management in farmland.

[0041] Example 4: To address the coupling issues of irrigation unit response lag and hydraulic impact under complex terrain, this example refines the spatiotemporal coordinated control mechanism of the multi-topology irrigation matrix. Figure 5As shown in the figure, the geometric configuration of the irrigation unit is based on the curvature tensor decomposition of the elevation data: at the critical condition of the slope threshold of 15%, the concave-convex characteristics of the terrain are determined by the sign of the main curvature eigenvalue. When the angle between the maximum slope direction and the crop row direction exceeds 45°, a circular radial layout is adopted to match the runoff diffusion pattern. It should be understood that the spatial close packing advantage of the honeycomb hexagonal unit is reflected in the minimization of the overlap coefficient of the hydraulic influence radius of adjacent valves. This characteristic is Figure 5 The three-dimensional grid is characterized by equidistant distribution with seamless connection between units.

[0042] Furthermore, the irrigation priority calculation model integrates soil water dynamics and meteorological correction factors: the α parameter is spatially interpolated according to the vertical distribution density of crop roots, with a higher weight given to the surface soil (0-30 cm); the time derivative term dH / dt of the β term is discretized using the implicit Euler method, and its numerical stability is constrained by the second-order derivative of the soil water characteristic curve; γ·Δ Prain The Gumbel extreme value distribution model is used to predict the compensation demand for short-term heavy rainfall events. Figure 7 The stacked curve of is reflected as a delayed response of deep soil compensation. It can be understood that this priority function constructs a coupled assessment framework for water deficit risk and external disturbances in both time and space.

[0043] Furthermore, the time window constraint scheduling algorithm adopts an improved Hungarian matching strategy: the valve opening request is abstracted into a weighted bipartite graph, where the node weight is determined by the priority P ij The minimum response time threshold is determined in conjunction with the pipeline pressure loss. The minimum response time threshold is set based on the wave solution of the hydraulic transient equation to ensure that the interval between adjacent valve actions is greater than the round-trip propagation period of the pressure wave in the pipeline network. Figure 6 As shown in the figure, the collaborative optimization process of the algorithm and the reinforcement learning strategy shows the convergence consistency of the reward value and Q-value estimation curve in the later stage of training, which verifies the optimality of the action sequence of the scheduling strategy under the delay constraint.

[0044] The generation process of the control instruction sequence is embedded in the CRC-16 checksum cyclic redundancy detection mechanism: flow rate flow rate The quantization accuracy is dynamically adjusted according to the pipe diameter-flow velocity relationship curve, and a piecewise linear encoding strategy is adopted in the critical Reynolds number region between laminar flow and turbulence. The instruction encapsulation protocol adopts the NTP synchronization time scheme of the timestamp, and its clock deviation compensation is updated online through the Kalman prediction deviation. This process is Figure 3 The filtering estimation band of is reflected in the narrowing of the prediction error band at the time of irrigation event triggering.

[0045] The benefits of this embodiment include: A multi-configuration irrigation unit layout driven by terrain curvature achieves a universal match between the network's hydraulic characteristics and the topography. Furthermore, a dynamic weighting mechanism for prioritizing extreme rainfall predictions effectively enhances the predictive control capabilities of compensatory irrigation. This control architecture, characterized by spatiotemporal decoupling, provides topological adaptation and communication for precision irrigation on large farmland.

[0046] Example 5: To solve the problem of delayed response and multi-objective conflict of traditional irrigation strategies in dynamic environments, this example constructs a closed-loop strategy update mechanism based on six-dimensional state space perception. Figure 2 As shown in the figure, the three-dimensional water transport model provides dynamic physical field support for the soil moisture mean in the state space by coupling the spatiotemporal evolution of surface runoff and underground seepage. Among them, the quantification of the crop growth stage index introduces the vegetation reflectance characteristics of drone multispectral imaging, and the crop stress coefficient is constructed by the reflectance ratio of the red edge band (730-760nm) and the near-infrared band (780-900nm). Figure 7 The schematic diagram comparing rainfall compensation effects shows a gradient variation in deep compensation. It's important to understand that the calculation of the modified rainfall probability incorporates the water redistribution effect of the previous irrigation cycle. Its probability density function is modeled using a non-stationary Poisson process, ensuring a closed feedback loop between the water deficit assessment and historical irrigation benefits.

[0047] Furthermore, the parameterized definition of the action space adopts a spatiotemporal decoupling encoding strategy: the irrigation interval constraint is defined by Figure 6 The convergence characteristics of the reward value in the reinforcement learning training curve are inverted and the physical nature of the minimum response time threshold is derived from the group velocity propagation characteristics of the pipe network pressure wave. When the valve action interval exceeds the threshold, the trigger is based on Figure 5 The conflict detection algorithm of the honeycomb grid topology structure uses the detection logic to jointly determine the priority overlap of adjacent cells and the pressure gradient. It should be emphasized that the optimization objective function of the dynamic pressure parameter of the water pump is embedded in the adaptive weight factor of the particle swarm algorithm, such as Figure 4 As shown in Figure 2, the factor is dynamically adjusted according to the degree of approximation to the Pareto front of the decision variables to ensure the synchronous optimization of irrigation amount and timing parameters.

[0048] Furthermore, the deep reinforcement learning network architecture adopts a dual-stream feature fusion mechanism: Figure 3 As shown in the filter estimation band, the input layer concatenates the scalar data of the six-dimensional state space with the improved Kalman filter estimation value, where the time derivative of the temperature change rate is extracted through the time series association module of the LSTM network to extract implicit features. The action parameter generation strategy of the output layer is the same as Figure 4The constraints of particle swarm optimization form a dynamic interlock, which is manifested in that the feasible region boundary of irrigation interval time gradually shrinks as the Q value estimator converges. It is understandable that this linkage mechanism appears in the later stage of training. Figure 6 The synchronous and stable characteristics of the reward curve and the Q-value curve verify the stability of the strategy update process.

[0049] Furthermore, the update mechanism of network parameters introduces the gradient encryption aggregation technology under the federated learning framework: each regional node trains the sub-model through local historical irrigation benefit data, and the central server adopts a dynamic weighted average algorithm to fuse the gradient update amount. Figure 7 Furthermore, the chaotic perturbation optimization strategy injects nonlinear noise into the parameter space through the Logistic mapping function, and its perturbation amplitude dynamically decays according to the Lyapunov exponent of the particle swarm search trajectory. Figure 4 The optimization search space is manifested as an adaptive adjustment of the particle swarm distribution density.

[0050] The benefits of this embodiment include: through holographic perception of six-dimensional state space and deep reinforcement learning strategy search, dynamic matching of irrigation decision parameters across the three dimensions of time, space, and crop growth is achieved. Furthermore, the collaborative optimization mechanism of federated learning and chaotic perturbations effectively improves the generalization capability and resistance to local optimality of multi-region coordinated irrigation. This closed-loop feedback strategy update system provides the dual guarantees of dynamic adjustability and multi-objective optimization for intelligent irrigation in complex farmland environments.

[0051] Example 6: In order to solve the dynamic matching problem between compensation hysteresis and soil infiltration heterogeneity in rainfall events, this example further refines the temporal and spatial differentiation rainfall compensation mechanism. Figure 7 As shown, the critical duration T c The threshold effect is reflected by the intersection of the surface and deep compensation curves, such as Figure 5 As shown, the slope angle α in the calculation formula is obtained by mapping the grid gradient parameters of the three-dimensional coordinate system in the honeycomb grid model of the attached code. It should be understood that the dynamic calculation of the evaporation attenuation factor λ integrates the real-time wind speed and temperature data of the weather station, and its correction process is Figure 3 The rolling update of state variables is realized under the improved Kalman filter framework to ensure that the real-time error of the surface compensation is controlled within ±5%.

[0052] Furthermore, the water redistribution mechanism of the surface compensation mode is Figure 2 The three-dimensional water transport model verifies the infiltration efficiency, where the maximum water holding capacity S max The calibration of the unsaturated soil hydraulic parameters is carried out using the gradient inversion method. <T cThe physical essence of the quadratic term in the compensation formula stems from the nonlinear attenuation characteristics of water infiltration rate with water holding capacity saturation. Figure 7 The shallow compensation curve of is a convex function with decreasing increments. It can be understood that the permeability correction coefficient k s The value strategy and Figure 4 The constraints of the particle swarm optimization process are solved simultaneously, and its optimization objective function is embedded in the multi-objective trade-off between surface compensation efficiency and pipeline network pressure stability.

[0053] Furthermore, the time-weighted integration algorithm of the deep compensation mode is Figure 6 The convergence characteristics of the reinforcement learning training curve are used to adaptively adjust the integral step size, where the physical meaning of the lag response time constant T0 corresponds to the time lag of water migration from the surface to the deep soil. The design of the integral kernel function introduces the probability distribution characteristics of historical rainfall events, and its probability density function is expressed by Figure 5 The priority parameters of the honeycomb grid are weighted. It should be emphasized that the deep penetration correction factor k d The calibration method uses Monte Carlo simulation and field measurement data to ensure Figure 7 The error between the accumulation rate of the compensation curve for the middle and deep layers and the measured permeability is less than 8%.

[0054] Critical duration T c The real-time calculation adopts a sliding time window mechanism, and its iteration interval is Figure 3 The update cycle of state estimation is kept synchronous. The initial value of soil moisture θ0 is obtained through multi-node data fusion of wireless sensor network, and its spatial interpolation algorithm adopts Figure 5 The Delaunay triangulation method of the honeycomb grid effectively eliminates the measurement deviation caused by the terrain undulation. It can be understood that the calculation of the slope factor tanα generates a digital elevation model through the UAV LiDAR point cloud data, and its resolution matches Figure 2 Spatial discretization accuracy of three-dimensional water transport model.

[0055] The benefits of this embodiment lie in: by establishing a three-factor compensation decision-making model for rainfall intensity, duration, and soil infiltration, adaptive switching of compensation modes and precise control of water allocation are achieved. Furthermore, by incorporating meteorological parameters, topographical characteristics, and pipe network constraints into a unified optimization framework, the temporal and spatial utilization of rainfall resources is significantly improved. This compensation mechanism, driven by physical mechanisms and fused with data, safeguards the dynamic response and anti-interference performance of the ecological irrigation system.

[0056] Example 7: In order to solve the problem of collaborative optimization of multi-factor coupling modeling and distributed control architecture in complex farmland ecosystems, this example further refines the integration method of multivariable coupling model and distributed control architecture. Figure 2As shown in the figure, the three-dimensional topological structure of the root system is mapped to the grid cells of the three-dimensional water migration model through discretization processing, and its branch angle parameters are obtained by reverse modeling the point cloud data of UAV oblique photogrammetry. It should be understood that the dynamic solution of the NDVI vegetation index incorporates the RVI reflectance ratio of multispectral imaging, and its spatial interpolation algorithm is Figure 5 The Kriging interpolation method is used in the honeycomb grid model to ensure the spatial continuity of the coercion coefficient.

[0057] Furthermore, the water-nutrient coupling equation is constructed based on the simultaneous equations of the law of conservation of mass and Fick's diffusion law, in which the migration rate of nitrogen, phosphorus and potassium ions is expressed by Figure 3 Improve the implicit state variables in the Kalman filter framework for real-time estimation. The introduction of earthworm burrow density parameters uses the porous medium two-phase flow theory to modify the Darcy permeability model. The verification process is combined with Figure 4 The permeability constraint conditions in the particle swarm optimization process are used for parameter inversion. It can be understood that the optimization mechanism of the root absorption function calculates the hydraulic conductivity distribution through the fractal dimension of the three-dimensional topological structure, and its boundary conditions are similar to Figure 2 The matrix potential gradient in the model forms a dynamic coupling.

[0058] Further on the implementation level of distributed control architecture, such as Figure 6 As shown in Figure 2, the parameter aggregation period of federated learning is associated with the convergence threshold of the reinforcement learning training curve, and its model update trigger mechanism is determined by the change rate of the local loss function of the edge node. It should be emphasized that the real-time task scheduling strategy of the LiteOS system adopts a priority preemptive design, and its time slice allocation weight is related to Figure 5 The irrigation priority parameters of the cellular grid form a linkage feedback. The edge node fault switching mechanism is achieved through Figure 3 The residual analysis of the state estimation module is triggered, and when the heartbeat detection interval exceeds the 3σ standard deviation, the standby node redundancy takeover process is started.

[0059] In order to improve the chaotic perturbation optimization mechanism, this embodiment uses the ergodic characteristics of Tent mapping to enhance the diversity of particle swarms. Its chaotic sequence generation algorithm is Figure 4 The velocity update term in the optimization process injects random perturbations. The physical implementation of the repulsion operator is based on the inverse of the Euclidean distance between particles to construct a potential energy field, and its strength is similar to Figure 6 It is understandable that the method for identifying high potential regions is to use kernel density estimation of historical optimization data (e.g. Figure 4 The particle reset operation is orthogonal to the gradient direction of the current global optimal solution.

[0060] The benefits of this embodiment are: by establishing a cross-scale coupling model of multispectral data-root structure-nutrient migration, holographic modeling of farmland ecological elements is achieved; at the same time, the deep integration of distributed architecture and intelligent optimization algorithms ensures the adaptive regulation capability of complex farmland systems.

[0061] Example 8: In order to solve the problem of coordinated control of large-scale farmland system intelligent decision-making and distributed execution agencies, this embodiment proposes an ecological irrigation decision-making dynamic optimization system, which further optimizes the software and hardware architecture of the ecological irrigation decision-making system and its coordinated operation mechanism. Figure 2 As shown in the figure, the data input layer of the 3D water transport model obtains vertical gradient data through the soil profile sensor array, and its spatial interpolation algorithm is Figure 5 The improved Delaunay triangulation method is used in the cellular grid model to ensure the topological fidelity of the humidity field reconstruction. It should be understood that the coordinate transformation error of the GNSS positioning module is calculated by Figure 3 Improved multi-source data fusion under the Kalman filter framework is used for real-time correction, with accuracy controlled within the sub-meter range.

[0062] Furthermore, the FPGA accelerator of the edge computing layer adopts a pipeline architecture design, and its parallel computing units are Figure 4 The particle swarm optimization process dynamically matches the population size, achieving real-time optimization capabilities of processing thousands of decision variables per millisecond. The digital twin simulation environment at the cloud platform layer is Figure 6 The strategy migration mechanism of the reinforcement learning training curve is updated synchronously, and its parameter synchronization protocol uses a hash verification mechanism based on blockchain to ensure model consistency. It is understandable that the control instruction generation algorithm of the pressure regulating device is Figure 7 Water distribution is optimized under the constraints of the rainfall compensation curve, and the time resolution of the pulse driving signal reaches 0.1 second.

[0063] At the device implementation level, such as Figure 5 As shown in the figure, the communication protocol of the cellular grid control interface adopts adaptive frequency hopping technology, and its channel allocation strategy forms a dynamic mapping relationship with the priority parameters of the cellular grid. It should be emphasized that the design of the pulse irrigation drive circuit incorporates the duty cycle modulation technology under the time window constraint, and its working state is determined by the Figure 3 The output of the state estimation module is fed forward. The data preprocessing process of the multi-spectral analysis unit is integrated in Figure 2 In the texture mapping channel of the three-dimensional model, the crop stress recognition results are multi-dimensionally associated with water migration parameters through the feature extraction layer of the convolutional neural network.

[0064] The redundant control channel of the fault switching unit adopts hot backup design, and its heartbeat detection mechanism is Figure 6The abnormal fluctuation characteristics of the training curve are used to predict failures, and seamless switching is triggered when the probability of node offline exceeds the confidence interval. The collaborative operation logic of the dynamic valve control unit is based on the Nash equilibrium solution constructed by game theory, and its distributed decision-making algorithm is based on Figure 4 The Pareto front solution set of the optimization engine is used for strategy optimization. It is understandable that the virtual-real fusion rendering engine of AR applications uses SLAM technology to achieve three-dimensional registration, and its spatial positioning accuracy is comparable to that of Figure 5 The coordinate base of the cellular grid is kept dynamically calibrated.

[0065] like Figure 8 As shown in the system architecture diagram, the system of the present invention may also include: The end layer, deployed in the fields, comprises various sensors, actuators, and local control units. It is responsible for real-time sensing of environmental data and executing irrigation commands. A diverse sensor network, including soil moisture, temperature, light, and rainfall sensors, collects real-time data on the plant growth environment, providing real-time data by time period and region. The irrigation controller receives irrigation plans from the data processing layer and controls the on / off and water flow of irrigation equipment. Actuators, such as solenoid valves and pumps, execute irrigation operations. A user interface is also included, allowing users to view data, set parameters, and control the irrigation system. The local control unit is responsible for initial data collection and preprocessing, communicating with the edge layer, and receiving and executing irrigation commands from the edge layer or cloud layer. In scenarios requiring high real-time performance, the local control unit or its supporting hardware can integrate hardware acceleration modules (such as FPGAs) to enable rapid execution of certain local decision-making algorithms.

[0066] Furthermore, it includes an edge layer deployed at regional aggregation points or edge computing servers, responsible for aggregating end-layer data, performing regional data processing, executing local optimization and control strategies, and interacting with the cloud layer. The data aggregation and preprocessing module receives end-layer data and performs cleaning and denoising. Edge nodes of a distributed in-memory database can be used to achieve rapid caching and access to regional data. The regional feature extraction and modeling module utilizes GIS information and historical data to construct region-specific soil, crop, and environmental response models. The local decision-making and optimization module executes local optimization algorithms (such as adaptive PID and local reinforcement learning) based on regional data and models to generate optimal regional irrigation plans. The data storage module stores regional historical data and can serve as a local training data source for federated learning. Some key data and operation records can be uploaded to the blockchain in collaboration with the cloud.

[0067] Furthermore, it includes a cloud layer deployed on cloud servers, responsible for global data storage, complex model training, cross-region collaborative optimization, digital twin maintenance, user management, and interface services. A global data storage and management module aggregates edge-layer data for long-term storage. This can utilize a database system that supports distributed deployment (such as a cloud-based node of a distributed in-memory database) and link it with blockchain data. A global collaborative optimization module is responsible for overall multi-region scheduling and water resource allocation, employing distributed optimization algorithms (such as the ADMM algorithm). A digital twin platform builds a high-fidelity digital twin model of the physical environment for state prediction, policy simulation, and verification. This platform utilizes all data aggregated from the end, edge, and cloud layers for model calibration and updates. A model training and adaptive learning module is responsible for training complex models, including machine learning models based on historical data, simulation data, and federated learning, as well as deep reinforcement learning models (such as those based on DQN, TD3, or other variants). Model training and optimization results are distributed to the edge and end layers. A user interface and management module provides user interaction, system configuration, status monitoring, and alarm functions. The blockchain service module is responsible for receiving key data and operation records sent by the edge layer, packaging them, reaching consensus, and adding them to the blockchain, providing data query and audit interfaces.

[0068] The benefits of this embodiment lie in: by building a full-link intelligent architecture encompassing data perception, edge computing, cloud optimization, and terminal execution, it achieves millisecond-level response and centimeter-level control accuracy for farmland irrigation decisions. Furthermore, the device-level multi-layer redundancy design and collaborative control mechanism enhance the system's adaptability to complex farmland environments. This technological system, integrating physical information models with intelligent hardware, provides a reliable engineering foundation for the large-scale application of precision agriculture.

[0069] Example 9: In order to solve the problem of collaborative optimization of multi-source data fusion and intelligent decision-making in large-scale agricultural Internet of Things, this example further refines the data flow architecture and algorithm coordination mechanism of the ecological irrigation system. Figure 9 As shown in the figure, the original signal of the soil conductivity sensor is pre-processed by wavelet transform to reduce noise, and then input into the improved cubature Kalman filter framework for state estimation. The process noise covariance matrix is ​​calculated according to Figure 11 The model confidence of the digital twin platform is dynamically adjusted. It should be understood that the spatiotemporal registration algorithm of GIS elevation data and weather radar echoes uses Fourier-Mellin transform to achieve sub-pixel alignment, and its registration error is calculated by Figure 9 The smart contracts of the blockchain module are automatically recorded in the distributed ledger.

[0070] Furthermore, the bidirectional interaction between the digital twin platform and the physical system is synchronized at the microsecond level through the industrial-grade Time Sensitive Network (TSN). Figure 11As shown in the figure, the parameter calibration of its 3D water migration model uses the Bayesian inversion algorithm, and a calibration report containing confidence intervals is generated for each iteration. The strategy verification environment of the twin platform integrates a discrete event simulation engine, which can Figure 9 Before the reinforcement learning module generates decision instructions, it simulates the impact of different irrigation schemes on solute transport in the crop root zone. It should be emphasized that the reward function design of reinforcement learning incorporates Figure 10 The regional features of the federated learning nodes and their policy gradient updates are reduced in variance using importance sampling techniques.

[0071] At the implementation level of federated learning, such as Figure 10 As shown, the local training of the edge node uses sparse triplet quantization technology to compress the model parameters, and its uploaded gradients are encrypted to ensure privacy. The model aggregation algorithm of the central server introduces the neural architecture search (NAS) mechanism to automatically generate a hybrid model architecture that adapts to the characteristics of multiple regions. It is understandable that the integrity of the parameter update package is verified by Figure 9 The zero-knowledge proof protocol of the blockchain module is implemented, and its verification process can complete the credibility verification without decrypting the gradient data.

[0072] The benefits of this implementation are: by building a three-layer collaborative architecture of data flow, algorithm, and platform, it achieves closed-loop control for farmland status perception, virtual simulation, and decision optimization. Furthermore, the deep integration of the federated learning framework and blockchain technology effectively enhances the generalization capabilities of cross-regional models while safeguarding data sovereignty. This technological system, integrating edge intelligence and cyber-physical systems, provides a verifiable engineering approach for the large-scale deployment of digital agriculture.

[0073] Example 10: In order to solve the engineering deployment and cross-platform compatibility issues of the ecological irrigation decision-making algorithm, this embodiment proposes a computer-readable storage medium, which further optimizes the instruction set architecture and runtime management mechanism of the computer-readable storage medium. The storage medium adopts a hybrid architecture design of non-volatile memory units and programmable read-only storage areas, and its physical sector division strategy maintains an aligned mapping relationship with the data structure of the digital twin platform. It should be understood that the memory access pattern of the instruction sequence is optimized through a predictive prefetch algorithm, and its cache hit rate is increased to more than 98%, effectively reducing the I / O latency of the edge computing node.

[0074] Furthermore, the parallel processing framework for the instruction set utilizes SIMD (Single Instruction, Multiple Data) extensions, with the vector register width dynamically adapting to the gradient data dimensions of the federated learning nodes. The encrypted partitioning of the storage medium utilizes an elliptic curve digital signature algorithm, providing hardware-level acceleration for the hash verification process of the blockchain module. Its key rotation cycle is synchronized with the federated learning aggregation frequency for irrigation decisions.

[0075] Furthermore, in terms of instruction execution optimization, the storage medium's built-in real-time task scheduler employs preemptive priority queue management, reducing context switching time to less than 5 microseconds, meeting the time-sensitive requirements of reinforcement learning decision loops. It is important to emphasize that the dynamic link library loading mechanism enhances security through address space layout randomization (ASLR), and its symbol resolution algorithm maintains consistent mapping with the parameter namespace of the federated learning model. The storage medium's wear-leveling algorithm incorporates a cellular automaton model to predict storage cell lifespan, and its bad block replacement strategy forms a feedback loop with the hardware health monitoring data of the digital twin platform.

[0076] The benefits of this embodiment are: by constructing a three-dimensional optimization system of instruction set, storage architecture, and security mechanism, the efficient and stable operation of the ecological irrigation decision-making algorithm on a heterogeneous computing platform is achieved; at the same time, the deep synergy between the physical properties of the storage medium and the algorithm logic provides underlying technical guarantees for the long-term and reliable operation of agricultural Internet of Things equipment.

[0077] Although the present invention has been described above in detail with reference to its preferred embodiments, it is to be understood that the present invention is not limited to the embodiments described above. Instead, various modifications and changes may be made by those skilled in the art without departing from the spirit of the present invention, and these modifications and changes should fall within the scope defined by the appended claims and their equivalents.

Claims

1. A dynamic optimization method for ecological irrigation decision making, characterized in that: include: Acquire multi-source environmental data, collect soil profile moisture gradients in real time through a soil moisture sensor array, combine with a weather forecast interface to obtain future rainfall probability distribution, temperature change rate, and wind speed forecasts, and simultaneously access a geographic information system to obtain terrain elevation and crop distribution data; A multivariate coupling model was constructed, and an improved Kalman filter was used to perform spatiotemporal fusion of soil moisture, meteorological data, and geographic information to establish a three-dimensional water transport model that includes soil moisture diffusion coefficient and root absorption rate. Perform multi-objective dynamic optimization and solve the Pareto optimal solution set of irrigation time and irrigation amount based on adaptive particle swarm optimization. The objective function is to minimize water consumption, maximize crop yield and reduce energy consumption costs. Generate a dynamic irrigation instruction matrix and determine the opening and closing sequence of irrigation valves and the intensity of pulse irrigation based on the real-time calculated soil moisture attenuation gradient and meteorological forecast error compensation value; Implement closed-loop strategy updates, establish an evaluation network based on deep reinforcement learning, and dynamically correct model parameter weights by analyzing the correlation matrix between historical irrigation records and crop growth indicators.

2. The dynamic optimization method for ecological irrigation decision-making according to claim 1, characterized in that: The implementation steps of the improved Kalman filter include: Define the state vector: , where H s is the layered soil moisture vector, T r is the root absorption rate, E p is the potential evapotranspiration, C l is the canopy interception coefficient; Establish the nonlinear state transfer equation: , where F(X K ) is a nonlinear function containing the soil permeability tensor, B is the irrigation control matrix, U k is the irrigation input vector, W k is the process noise; Design the observation equation: , where H is the measurement matrix containing the sensor space topology, V k is the time-varying covariance observation noise; Perform adaptive noise matching when the innovation covariance When the threshold is exceeded, the process noise covariance Q is dynamically adjusted k Until satisfied , where η is the adjustable sensitivity coefficient.

3. The dynamic optimization method for ecological irrigation decision-making according to claim 1, characterized in that: The optimization process of the adaptive particle swarm algorithm includes: Initialize particle positions and speed , where t i For irrigation time, Q i is the irrigation amount; Define the composite fitness function: , where ω1-ω3 are dynamic weights; Update particle velocity: , where the inertia weight is: ； When the local optimum stagnates beyond a threshold, a chaotic perturbation optimization strategy is applied.

4. The dynamic optimization method for ecological irrigation decision-making according to claim 1, characterized in that: The generation of the dynamic irrigation instruction matrix includes: Selecting an irrigation unit geometry based on geographic information system elevation data and crop distribution characteristics, the geometry being at least one of a honeycomb hexagonal, ring-shaped radial, rectangular, or circular topology; Each of the irrigation units is associated with an independent control valve, and an area threshold and a slope threshold of the irrigation unit are set; When the slope value is greater than the slope threshold, a circular radial irrigation unit layout is adopted; The priority of the irrigation unit is calculated according to the soil moisture decay rate. The expression of the priority is: , where α, β and γ represent the weight coefficients of each item, ΔP rain is the rainfall forecast correction amount; A time window constraint algorithm is used to ensure that the interval between adjacent valve openings is greater than the minimum response time threshold; Generate binary control sequences , after CRC check, it is sent to the execution terminal, where t start represents the irrigation start time, t duration Indicates the duration of valve opening, flow rate Indicates the traffic rate.

5. The dynamic optimization method for ecological irrigation decision-making according to claim 4, characterized in that: The closed-loop strategy update specifically includes: Based on soil moisture sensor data, dynamic rainfall compensation, and crop growth stage identification results, a six-dimensional state space is constructed, including soil moisture mean, temperature change rate, corrected rainfall probability, crop growth stage index, water deficit, and historical irrigation benefits. The crop growth stage index is determined by the crop stress coefficient extracted from drone multispectral imaging data. Define the action space directly related to the irrigation instructions, including irrigation interval time, single irrigation volume and water pump dynamic pressure parameters; The irrigation interval is limited by the minimum response time threshold, and valve conflict detection is triggered when the interval is lower than the minimum response time threshold; A deep reinforcement learning network is designed to generate action parameters. The input layer of the network integrates the data of the six-dimensional state space and the improved Kalman filter state estimation value. The output layer of the network is linked to the objective function of the particle swarm algorithm. The irrigation interval time, single irrigation volume and water pump dynamic pressure parameters are injected into the constraints of the particle swarm optimization. The network parameters are updated across regions through a federated learning mechanism and adjusted synchronously with the chaotic perturbation optimization strategy.

6. The dynamic optimization method for ecological irrigation decision-making according to claim 5, characterized in that: The optimization method further includes a dynamic rainfall compensation mechanism, wherein the steps of the dynamic rainfall compensation mechanism include: Calculate the critical duration Tc based on the real-time rainfall intensity R and historical rainfall data; When the cumulative rainfall time T < Tc, the surface compensation mode is used to calculate Q comp , and introduce the evaporation attenuation factor λ to correct the evaporation loss; When T ≥ Tc, switch to deep compensation mode and use time-weighted integration algorithm to calculate the cumulative penetration; The surface compensation calculation formula is: , where k s represents the surface soil permeability correction coefficient, B represents the area, S max Indicates the maximum water holding capacity of the soil; The deep compensation calculation formula is: , where k s represents the deep soil penetration correction coefficient, τ represents the integral variable, and T0 represents the hysteresis response time constant; The calculation formula of the critical duration is: , where α is the slope angle and θ0 is obtained by measuring the soil moisture sensor; The calculation formula of the evaporation attenuation factor is: , where v wind is the wind speed, T air For the temperature.

7. The dynamic optimization method for ecological irrigation decision-making according to claim 5, characterized in that: The multivariable coupling model further includes: fusing the UAV multispectral imaging data and extracting the vegetation index NDVI as a crop stress coefficient; Establish a water-nutrient coupling equation to describe the dynamic equilibrium relationship between soil water content and nitrogen, phosphorus and potassium concentrations; Introducing earthworm burrow density parameters to modify soil permeability model; Optimize the water absorption function according to the three-dimensional topological structure of the root system; The steps of the chaos disturbance optimization strategy include: Tent mapping is used to generate chaotic sequences to avoid the uneven distribution defect of Logistic mapping; Design the adaptive attenuation function of the disturbance amplitude: , where δ(t) represents the attenuation of the disturbance amplitude, δ0 represents the initial disturbance amplitude, and δ min represents the minimum perturbation amplitude, t represents the number of iterations, and σ represents the decay time constant; The repulsion operator is introduced to prevent the particle swarm from over-aggregating; When premature convergence is detected, the positions of 20% of the particles are reset to the high potential area.

8. An ecological irrigation decision-making dynamic optimization system, used to implement the ecological irrigation decision-making dynamic optimization method according to any one of claims 1 to 7, characterized in that: The optimization system comprises: Multi-source data acquisition module for real-time acquisition of soil moisture gradient, weather forecast data and geographic information; A coupled model building module for integrating multi-dimensional environmental data and building a three-dimensional water transport model; Dynamic optimization calculation module, used to solve the Pareto optimal solution set of irrigation parameters; The instruction generation and distribution module is used to generate anti-interference irrigation control signals and send them to the execution terminal; Closed-loop learning and updating module, used to dynamically optimize decision-making strategies through historical data feedback; The dynamic optimization calculation includes: The data perception layer includes a soil profile sensor array, a weather station, a UAV remote sensing unit, and a GNSS positioning module; The edge computing layer is deployed with a processing unit for the dynamic optimization method for ecological irrigation decision-making, including an FPGA accelerator and a distributed memory database; The cloud platform layer runs deep reinforcement learning models and digital twin simulation environments; Execution control layer, including programmable logic controller, solenoid valve matrix and pressure regulating device; The human-computer interaction layer is used to provide WEB management background and mobile AR applications.

9. An ecological irrigation decision-making dynamic optimization device, integrated with the ecological irrigation decision-making dynamic optimization system according to claim 8, characterized in that: The optimization device comprises: Data acquisition unit, connecting soil moisture sensor, meteorological interface and geographic information system; Model processing unit, integrating improved Kalman filter algorithm and multivariable coupling calculation logic; Optimized computing unit with built-in adaptive particle swarm optimization engine and multi-objective decision maker; An instruction execution unit, including a cellular grid control interface and a pulse irrigation drive circuit; The policy update unit deploys a deep reinforcement learning network and a dynamic parameter correction program; A failover unit, configured to activate a redundant control channel upon detecting a node failure; Multispectral analysis unit, integrating drone data interface and crop stress identification algorithm; Dynamic valve control unit supports distributed valve collaborative operation under time window constraints.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the readable instructions are executed by a processor, the dynamic optimization method for ecological irrigation decision-making according to any one of claims 1 to 7 is implemented.

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